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def test_spam(self, name, staking_utxo_list, fRandomHeight=False, randomRange=0, randomRange2=0, fDoubleSpend=False, fMustPass=False, fZPoS=False, spending_utxo_list=[]):
' General method to create, send and test the spam blocks\n :param name: (string) chain branch (usually either "Main" or "Forked")\n staking_utxo_list: (string list) utxos to use for staking\n fRandomHeight: (bool) send blocks at random height\n randomRange: (int) if fRandomHeight=True, height is >= current-randomRange\n randomRange2: (int) if fRandomHeight=True, height is < current-randomRange2\n fDoubleSpend: (bool) if true, stake input is double spent in block.vtx\n fMustPass: (bool) if true, the blocks must be stored on disk\n fZPoS: (bool) stake the block with zerocoin\n spending_utxo_list: (string list) utxos to use for spending\n :return: err_msgs: (string list) reports error messages from the test\n or an empty list if test is successful\n '
err_msgs = []
self.log_data_dir_size()
block_count = self.node.getblockcount()
pastBlockHash = self.node.getblockhash(block_count)
randomCount = block_count
self.log.info(('Current height: %d' % block_count))
for i in range(0, self.NUM_BLOCKS):
if (i != 0):
self.log.info(('Sent %d blocks out of %d' % (i, self.NUM_BLOCKS)))
if fRandomHeight:
randomCount = randint((block_count - randomRange), (block_count - randomRange2))
pastBlockHash = self.node.getblockhash(randomCount)
current_block_n = (randomCount + 1)
stakingPrevOuts = self.get_prevouts(staking_utxo_list, randomCount, zpos=fZPoS)
spendingPrevOuts = self.get_prevouts(spending_utxo_list, randomCount)
block = self.create_spam_block(pastBlockHash, stakingPrevOuts, current_block_n, fStakeDoubleSpent=fDoubleSpend, fZPoS=fZPoS, spendingPrevOuts=spendingPrevOuts)
block_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(block.nTime))
block_size = (len(block.serialize()) / 1000)
self.log.info('Sending block %d [%s...] - nTime: %s - Size (kb): %.2f', current_block_n, block.hash[:7], block_time, block_size)
var = self.node.submitblock(bytes_to_hex_str(block.serialize()))
time.sleep(1)
if (((not fMustPass) and (var not in [None, 'bad-txns-invalid-ztdps'])) or (fMustPass and (var != 'inconclusive'))):
self.log.error(('submitblock [fMustPass=%s] result: %s' % (str(fMustPass), str(var))))
err_msgs.append(('submitblock %d: %s' % (current_block_n, str(var))))
msg = msg_block(block)
try:
self.test_nodes[0].handle_connect()
self.test_nodes[0].send_message(msg)
time.sleep(2)
block_ret = self.node.getblock(block.hash)
if ((not fMustPass) and (block_ret is not None)):
self.log.error(('Error, block stored in %s chain' % name))
err_msgs.append(('getblock %d: result not None' % current_block_n))
if fMustPass:
if (block_ret is None):
self.log.error(('Error, block NOT stored in %s chain' % name))
err_msgs.append(('getblock %d: result is None' % current_block_n))
else:
self.log.info('Good. Block IS stored on disk.')
except JSONRPCException as e:
exc_msg = str(e)
if (exc_msg == "Can't read block from disk (-32603)"):
if fMustPass:
self.log.warning('Bad! Block was NOT stored to disk.')
err_msgs.append(exc_msg)
else:
self.log.info('Good. Block was not stored on disk.')
else:
self.log.warning(exc_msg)
err_msgs.append(exc_msg)
except Exception as e:
exc_msg = str(e)
self.log.error(exc_msg)
err_msgs.append(exc_msg)
self.log.info(('Sent all %s blocks.' % str(self.NUM_BLOCKS)))
self.log_data_dir_size()
return err_msgs | -3,516,039,327,091,069,400 | General method to create, send and test the spam blocks
:param name: (string) chain branch (usually either "Main" or "Forked")
staking_utxo_list: (string list) utxos to use for staking
fRandomHeight: (bool) send blocks at random height
randomRange: (int) if fRandomHeight=True, height is >= current-randomRange
randomRange2: (int) if fRandomHeight=True, height is < current-randomRange2
fDoubleSpend: (bool) if true, stake input is double spent in block.vtx
fMustPass: (bool) if true, the blocks must be stored on disk
fZPoS: (bool) stake the block with zerocoin
spending_utxo_list: (string list) utxos to use for spending
:return: err_msgs: (string list) reports error messages from the test
or an empty list if test is successful | test/functional/fake_stake/base_test.py | test_spam | tdpsdevextreme/TradePlusCoin | python | def test_spam(self, name, staking_utxo_list, fRandomHeight=False, randomRange=0, randomRange2=0, fDoubleSpend=False, fMustPass=False, fZPoS=False, spending_utxo_list=[]):
' General method to create, send and test the spam blocks\n :param name: (string) chain branch (usually either "Main" or "Forked")\n staking_utxo_list: (string list) utxos to use for staking\n fRandomHeight: (bool) send blocks at random height\n randomRange: (int) if fRandomHeight=True, height is >= current-randomRange\n randomRange2: (int) if fRandomHeight=True, height is < current-randomRange2\n fDoubleSpend: (bool) if true, stake input is double spent in block.vtx\n fMustPass: (bool) if true, the blocks must be stored on disk\n fZPoS: (bool) stake the block with zerocoin\n spending_utxo_list: (string list) utxos to use for spending\n :return: err_msgs: (string list) reports error messages from the test\n or an empty list if test is successful\n '
err_msgs = []
self.log_data_dir_size()
block_count = self.node.getblockcount()
pastBlockHash = self.node.getblockhash(block_count)
randomCount = block_count
self.log.info(('Current height: %d' % block_count))
for i in range(0, self.NUM_BLOCKS):
if (i != 0):
self.log.info(('Sent %d blocks out of %d' % (i, self.NUM_BLOCKS)))
if fRandomHeight:
randomCount = randint((block_count - randomRange), (block_count - randomRange2))
pastBlockHash = self.node.getblockhash(randomCount)
current_block_n = (randomCount + 1)
stakingPrevOuts = self.get_prevouts(staking_utxo_list, randomCount, zpos=fZPoS)
spendingPrevOuts = self.get_prevouts(spending_utxo_list, randomCount)
block = self.create_spam_block(pastBlockHash, stakingPrevOuts, current_block_n, fStakeDoubleSpent=fDoubleSpend, fZPoS=fZPoS, spendingPrevOuts=spendingPrevOuts)
block_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(block.nTime))
block_size = (len(block.serialize()) / 1000)
self.log.info('Sending block %d [%s...] - nTime: %s - Size (kb): %.2f', current_block_n, block.hash[:7], block_time, block_size)
var = self.node.submitblock(bytes_to_hex_str(block.serialize()))
time.sleep(1)
if (((not fMustPass) and (var not in [None, 'bad-txns-invalid-ztdps'])) or (fMustPass and (var != 'inconclusive'))):
self.log.error(('submitblock [fMustPass=%s] result: %s' % (str(fMustPass), str(var))))
err_msgs.append(('submitblock %d: %s' % (current_block_n, str(var))))
msg = msg_block(block)
try:
self.test_nodes[0].handle_connect()
self.test_nodes[0].send_message(msg)
time.sleep(2)
block_ret = self.node.getblock(block.hash)
if ((not fMustPass) and (block_ret is not None)):
self.log.error(('Error, block stored in %s chain' % name))
err_msgs.append(('getblock %d: result not None' % current_block_n))
if fMustPass:
if (block_ret is None):
self.log.error(('Error, block NOT stored in %s chain' % name))
err_msgs.append(('getblock %d: result is None' % current_block_n))
else:
self.log.info('Good. Block IS stored on disk.')
except JSONRPCException as e:
exc_msg = str(e)
if (exc_msg == "Can't read block from disk (-32603)"):
if fMustPass:
self.log.warning('Bad! Block was NOT stored to disk.')
err_msgs.append(exc_msg)
else:
self.log.info('Good. Block was not stored on disk.')
else:
self.log.warning(exc_msg)
err_msgs.append(exc_msg)
except Exception as e:
exc_msg = str(e)
self.log.error(exc_msg)
err_msgs.append(exc_msg)
self.log.info(('Sent all %s blocks.' % str(self.NUM_BLOCKS)))
self.log_data_dir_size()
return err_msgs |
def crossmul(**kwds):
'A factory for Crossmul'
from .Crossmul import Crossmul
return Crossmul(**kwds) | -7,984,824,203,532,728,000 | A factory for Crossmul | python/packages/isce3/signal/__init__.py | crossmul | piyushrpt/isce3 | python | def crossmul(**kwds):
from .Crossmul import Crossmul
return Crossmul(**kwds) |
@pytest.mark.parametrize(('acquire', 'use', 'release', 'final_result', 'log'), [(_acquire_success, _use_success, _ReleaseSuccess, IOSuccess('use success'), [('acquire success', Success('use success'))]), (_acquire_success, _use_success, _ReleaseFailure, IOFailure('release failure'), []), (_acquire_success, _use_failure, _ReleaseSuccess, IOFailure('use failure'), [('acquire success', Failure('use failure'))]), (_acquire_success, _use_failure, _ReleaseFailure, IOFailure('release failure'), []), (_acquire_failure, _use_success, _ReleaseSuccess, IOFailure('acquire failure'), []), (_acquire_failure, _use_failure, _ReleaseSuccess, IOFailure('acquire failure'), []), (_acquire_failure, _use_success, _ReleaseFailure, IOFailure('acquire failure'), []), (_acquire_failure, _use_failure, _ReleaseFailure, IOFailure('acquire failure'), [])])
def test_all_success(acquire, use, release, final_result, log):
'Ensures that managed works as intended.'
pipeline_logs: List[Tuple[(str, Result[(str, str)])]] = []
pipeline_result = managed(use, release(pipeline_logs))(acquire)
assert (pipeline_result == final_result)
assert (pipeline_logs == log) | 5,979,508,904,576,942,000 | Ensures that managed works as intended. | tests/test_pipeline/test_managed/test_managed_ioresult.py | test_all_success | CucumisSativus/returns | python | @pytest.mark.parametrize(('acquire', 'use', 'release', 'final_result', 'log'), [(_acquire_success, _use_success, _ReleaseSuccess, IOSuccess('use success'), [('acquire success', Success('use success'))]), (_acquire_success, _use_success, _ReleaseFailure, IOFailure('release failure'), []), (_acquire_success, _use_failure, _ReleaseSuccess, IOFailure('use failure'), [('acquire success', Failure('use failure'))]), (_acquire_success, _use_failure, _ReleaseFailure, IOFailure('release failure'), []), (_acquire_failure, _use_success, _ReleaseSuccess, IOFailure('acquire failure'), []), (_acquire_failure, _use_failure, _ReleaseSuccess, IOFailure('acquire failure'), []), (_acquire_failure, _use_success, _ReleaseFailure, IOFailure('acquire failure'), []), (_acquire_failure, _use_failure, _ReleaseFailure, IOFailure('acquire failure'), [])])
def test_all_success(acquire, use, release, final_result, log):
pipeline_logs: List[Tuple[(str, Result[(str, str)])]] = []
pipeline_result = managed(use, release(pipeline_logs))(acquire)
assert (pipeline_result == final_result)
assert (pipeline_logs == log) |
def test_full_typing():
'This test is here to be a case for typing.'
logs: List[Tuple[(str, Result[(str, str)])]] = []
pipeline_result = managed(_use_success, _ReleaseSuccess(logs))(_acquire_success)
assert (pipeline_result == IOSuccess('use success'))
assert (logs == [('acquire success', Success('use success'))]) | 2,958,457,838,396,522,000 | This test is here to be a case for typing. | tests/test_pipeline/test_managed/test_managed_ioresult.py | test_full_typing | CucumisSativus/returns | python | def test_full_typing():
logs: List[Tuple[(str, Result[(str, str)])]] = []
pipeline_result = managed(_use_success, _ReleaseSuccess(logs))(_acquire_success)
assert (pipeline_result == IOSuccess('use success'))
assert (logs == [('acquire success', Success('use success'))]) |
def getDivisionFailure(*args, **kwargs):
"\n Make a C{Failure} of a divide-by-zero error.\n\n @param args: Any C{*args} are passed to Failure's constructor.\n @param kwargs: Any C{**kwargs} are passed to Failure's constructor.\n "
try:
(1 / 0)
except:
f = failure.Failure(*args, **kwargs)
return f | -8,652,234,052,711,301,000 | Make a C{Failure} of a divide-by-zero error.
@param args: Any C{*args} are passed to Failure's constructor.
@param kwargs: Any C{**kwargs} are passed to Failure's constructor. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | getDivisionFailure | XZH950926/meizitu | python | def getDivisionFailure(*args, **kwargs):
"\n Make a C{Failure} of a divide-by-zero error.\n\n @param args: Any C{*args} are passed to Failure's constructor.\n @param kwargs: Any C{**kwargs} are passed to Failure's constructor.\n "
try:
(1 / 0)
except:
f = failure.Failure(*args, **kwargs)
return f |
def test_failAndTrap(self):
'\n Trapping a L{Failure}.\n '
try:
raise NotImplementedError('test')
except:
f = failure.Failure()
error = f.trap(SystemExit, RuntimeError)
self.assertEqual(error, RuntimeError)
self.assertEqual(f.type, NotImplementedError) | -6,052,471,848,568,529,000 | Trapping a L{Failure}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failAndTrap | XZH950926/meizitu | python | def test_failAndTrap(self):
'\n \n '
try:
raise NotImplementedError('test')
except:
f = failure.Failure()
error = f.trap(SystemExit, RuntimeError)
self.assertEqual(error, RuntimeError)
self.assertEqual(f.type, NotImplementedError) |
def test_trapRaisesWrappedException(self):
'\n If the wrapped C{Exception} is not a subclass of one of the\n expected types, L{failure.Failure.trap} raises the wrapped\n C{Exception}.\n '
if (not _PY3):
raise SkipTest('\n Only expected behaviour on Python 3.\n @see U{http://twisted.readthedocs.io/en/latest/core/howto/python3.html#twisted-python-failure}\n ')
exception = ValueError()
try:
raise exception
except:
f = failure.Failure()
untrapped = self.assertRaises(ValueError, f.trap, OverflowError)
self.assertIs(exception, untrapped) | -1,991,415,557,886,170,600 | If the wrapped C{Exception} is not a subclass of one of the
expected types, L{failure.Failure.trap} raises the wrapped
C{Exception}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_trapRaisesWrappedException | XZH950926/meizitu | python | def test_trapRaisesWrappedException(self):
'\n If the wrapped C{Exception} is not a subclass of one of the\n expected types, L{failure.Failure.trap} raises the wrapped\n C{Exception}.\n '
if (not _PY3):
raise SkipTest('\n Only expected behaviour on Python 3.\n @see U{http://twisted.readthedocs.io/en/latest/core/howto/python3.html#twisted-python-failure}\n ')
exception = ValueError()
try:
raise exception
except:
f = failure.Failure()
untrapped = self.assertRaises(ValueError, f.trap, OverflowError)
self.assertIs(exception, untrapped) |
def test_trapRaisesSelf(self):
'\n If the wrapped C{Exception} is not a subclass of one of the\n expected types, L{failure.Failure.trap} raises itself.\n '
if _PY3:
raise SkipTest('\n Only expected behaviour on Python 2.\n @see U{http://twisted.readthedocs.io/en/latest/core/howto/python3.html#twisted-python-failure}\n ')
exception = ValueError()
try:
raise exception
except:
f = failure.Failure()
untrapped = self.assertRaises(failure.Failure, f.trap, OverflowError)
self.assertIs(f, untrapped) | 6,533,851,203,345,425,000 | If the wrapped C{Exception} is not a subclass of one of the
expected types, L{failure.Failure.trap} raises itself. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_trapRaisesSelf | XZH950926/meizitu | python | def test_trapRaisesSelf(self):
'\n If the wrapped C{Exception} is not a subclass of one of the\n expected types, L{failure.Failure.trap} raises itself.\n '
if _PY3:
raise SkipTest('\n Only expected behaviour on Python 2.\n @see U{http://twisted.readthedocs.io/en/latest/core/howto/python3.html#twisted-python-failure}\n ')
exception = ValueError()
try:
raise exception
except:
f = failure.Failure()
untrapped = self.assertRaises(failure.Failure, f.trap, OverflowError)
self.assertIs(f, untrapped) |
def test_failureValueFromFailure(self):
'\n A L{failure.Failure} constructed from another\n L{failure.Failure} instance, has its C{value} property set to\n the value of that L{failure.Failure} instance.\n '
exception = ValueError()
f1 = failure.Failure(exception)
f2 = failure.Failure(f1)
self.assertIs(f2.value, exception) | -3,928,023,598,779,263,500 | A L{failure.Failure} constructed from another
L{failure.Failure} instance, has its C{value} property set to
the value of that L{failure.Failure} instance. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failureValueFromFailure | XZH950926/meizitu | python | def test_failureValueFromFailure(self):
'\n A L{failure.Failure} constructed from another\n L{failure.Failure} instance, has its C{value} property set to\n the value of that L{failure.Failure} instance.\n '
exception = ValueError()
f1 = failure.Failure(exception)
f2 = failure.Failure(f1)
self.assertIs(f2.value, exception) |
def test_failureValueFromFoundFailure(self):
'\n A L{failure.Failure} constructed without a C{exc_value}\n argument, will search for an "original" C{Failure}, and if\n found, its value will be used as the value for the new\n C{Failure}.\n '
exception = ValueError()
f1 = failure.Failure(exception)
try:
f1.trap(OverflowError)
except:
f2 = failure.Failure()
self.assertIs(f2.value, exception) | -6,646,359,321,119,548,000 | A L{failure.Failure} constructed without a C{exc_value}
argument, will search for an "original" C{Failure}, and if
found, its value will be used as the value for the new
C{Failure}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failureValueFromFoundFailure | XZH950926/meizitu | python | def test_failureValueFromFoundFailure(self):
'\n A L{failure.Failure} constructed without a C{exc_value}\n argument, will search for an "original" C{Failure}, and if\n found, its value will be used as the value for the new\n C{Failure}.\n '
exception = ValueError()
f1 = failure.Failure(exception)
try:
f1.trap(OverflowError)
except:
f2 = failure.Failure()
self.assertIs(f2.value, exception) |
def assertStartsWith(self, s, prefix):
'\n Assert that C{s} starts with a particular C{prefix}.\n\n @param s: The input string.\n @type s: C{str}\n @param prefix: The string that C{s} should start with.\n @type prefix: C{str}\n '
self.assertTrue(s.startswith(prefix), ('%r is not the start of %r' % (prefix, s))) | -6,953,724,664,444,620,000 | Assert that C{s} starts with a particular C{prefix}.
@param s: The input string.
@type s: C{str}
@param prefix: The string that C{s} should start with.
@type prefix: C{str} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertStartsWith | XZH950926/meizitu | python | def assertStartsWith(self, s, prefix):
'\n Assert that C{s} starts with a particular C{prefix}.\n\n @param s: The input string.\n @type s: C{str}\n @param prefix: The string that C{s} should start with.\n @type prefix: C{str}\n '
self.assertTrue(s.startswith(prefix), ('%r is not the start of %r' % (prefix, s))) |
def assertEndsWith(self, s, suffix):
'\n Assert that C{s} end with a particular C{suffix}.\n\n @param s: The input string.\n @type s: C{str}\n @param suffix: The string that C{s} should end with.\n @type suffix: C{str}\n '
self.assertTrue(s.endswith(suffix), ('%r is not the end of %r' % (suffix, s))) | -6,076,212,063,322,656,000 | Assert that C{s} end with a particular C{suffix}.
@param s: The input string.
@type s: C{str}
@param suffix: The string that C{s} should end with.
@type suffix: C{str} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertEndsWith | XZH950926/meizitu | python | def assertEndsWith(self, s, suffix):
'\n Assert that C{s} end with a particular C{suffix}.\n\n @param s: The input string.\n @type s: C{str}\n @param suffix: The string that C{s} should end with.\n @type suffix: C{str}\n '
self.assertTrue(s.endswith(suffix), ('%r is not the end of %r' % (suffix, s))) |
def assertTracebackFormat(self, tb, prefix, suffix):
'\n Assert that the C{tb} traceback contains a particular C{prefix} and\n C{suffix}.\n\n @param tb: The traceback string.\n @type tb: C{str}\n @param prefix: The string that C{tb} should start with.\n @type prefix: C{str}\n @param suffix: The string that C{tb} should end with.\n @type suffix: C{str}\n '
self.assertStartsWith(tb, prefix)
self.assertEndsWith(tb, suffix) | 3,913,969,195,792,636,000 | Assert that the C{tb} traceback contains a particular C{prefix} and
C{suffix}.
@param tb: The traceback string.
@type tb: C{str}
@param prefix: The string that C{tb} should start with.
@type prefix: C{str}
@param suffix: The string that C{tb} should end with.
@type suffix: C{str} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertTracebackFormat | XZH950926/meizitu | python | def assertTracebackFormat(self, tb, prefix, suffix):
'\n Assert that the C{tb} traceback contains a particular C{prefix} and\n C{suffix}.\n\n @param tb: The traceback string.\n @type tb: C{str}\n @param prefix: The string that C{tb} should start with.\n @type prefix: C{str}\n @param suffix: The string that C{tb} should end with.\n @type suffix: C{str}\n '
self.assertStartsWith(tb, prefix)
self.assertEndsWith(tb, suffix) |
def assertDetailedTraceback(self, captureVars=False, cleanFailure=False):
"\n Assert that L{printDetailedTraceback} produces and prints a detailed\n traceback.\n\n The detailed traceback consists of a header::\n\n *--- Failure #20 ---\n\n The body contains the stacktrace::\n\n /twisted/trial/_synctest.py:1180: _run(...)\n /twisted/python/util.py:1076: runWithWarningsSuppressed(...)\n --- <exception caught here> ---\n /twisted/test/test_failure.py:39: getDivisionFailure(...)\n\n If C{captureVars} is enabled the body also includes a list of\n globals and locals::\n\n [ Locals ]\n exampleLocalVar : 'xyz'\n ...\n ( Globals )\n ...\n\n Or when C{captureVars} is disabled::\n\n [Capture of Locals and Globals disabled (use captureVars=True)]\n\n When C{cleanFailure} is enabled references to other objects are removed\n and replaced with strings.\n\n And finally the footer with the L{Failure}'s value::\n\n exceptions.ZeroDivisionError: float division\n *--- End of Failure #20 ---\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n @param cleanFailure: Enables L{Failure.cleanFailure}.\n @type cleanFailure: C{bool}\n "
if captureVars:
exampleLocalVar = 'xyz'
exampleLocalVar
f = getDivisionFailure(captureVars=captureVars)
out = NativeStringIO()
if cleanFailure:
f.cleanFailure()
f.printDetailedTraceback(out)
tb = out.getvalue()
start = ('*--- Failure #%d%s---\n' % (f.count, ((f.pickled and ' (pickled) ') or ' ')))
end = ('%s: %s\n*--- End of Failure #%s ---\n' % (reflect.qual(f.type), reflect.safe_str(f.value), f.count))
self.assertTracebackFormat(tb, start, end)
linesWithVars = [line for line in tb.splitlines() if line.startswith(' ')]
if captureVars:
self.assertNotEqual([], linesWithVars)
if cleanFailure:
line = ' exampleLocalVar : "\'xyz\'"'
else:
line = " exampleLocalVar : 'xyz'"
self.assertIn(line, linesWithVars)
else:
self.assertEqual([], linesWithVars)
self.assertIn(' [Capture of Locals and Globals disabled (use captureVars=True)]\n', tb) | -5,696,409,047,367,107,000 | Assert that L{printDetailedTraceback} produces and prints a detailed
traceback.
The detailed traceback consists of a header::
*--- Failure #20 ---
The body contains the stacktrace::
/twisted/trial/_synctest.py:1180: _run(...)
/twisted/python/util.py:1076: runWithWarningsSuppressed(...)
--- <exception caught here> ---
/twisted/test/test_failure.py:39: getDivisionFailure(...)
If C{captureVars} is enabled the body also includes a list of
globals and locals::
[ Locals ]
exampleLocalVar : 'xyz'
...
( Globals )
...
Or when C{captureVars} is disabled::
[Capture of Locals and Globals disabled (use captureVars=True)]
When C{cleanFailure} is enabled references to other objects are removed
and replaced with strings.
And finally the footer with the L{Failure}'s value::
exceptions.ZeroDivisionError: float division
*--- End of Failure #20 ---
@param captureVars: Enables L{Failure.captureVars}.
@type captureVars: C{bool}
@param cleanFailure: Enables L{Failure.cleanFailure}.
@type cleanFailure: C{bool} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertDetailedTraceback | XZH950926/meizitu | python | def assertDetailedTraceback(self, captureVars=False, cleanFailure=False):
"\n Assert that L{printDetailedTraceback} produces and prints a detailed\n traceback.\n\n The detailed traceback consists of a header::\n\n *--- Failure #20 ---\n\n The body contains the stacktrace::\n\n /twisted/trial/_synctest.py:1180: _run(...)\n /twisted/python/util.py:1076: runWithWarningsSuppressed(...)\n --- <exception caught here> ---\n /twisted/test/test_failure.py:39: getDivisionFailure(...)\n\n If C{captureVars} is enabled the body also includes a list of\n globals and locals::\n\n [ Locals ]\n exampleLocalVar : 'xyz'\n ...\n ( Globals )\n ...\n\n Or when C{captureVars} is disabled::\n\n [Capture of Locals and Globals disabled (use captureVars=True)]\n\n When C{cleanFailure} is enabled references to other objects are removed\n and replaced with strings.\n\n And finally the footer with the L{Failure}'s value::\n\n exceptions.ZeroDivisionError: float division\n *--- End of Failure #20 ---\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n @param cleanFailure: Enables L{Failure.cleanFailure}.\n @type cleanFailure: C{bool}\n "
if captureVars:
exampleLocalVar = 'xyz'
exampleLocalVar
f = getDivisionFailure(captureVars=captureVars)
out = NativeStringIO()
if cleanFailure:
f.cleanFailure()
f.printDetailedTraceback(out)
tb = out.getvalue()
start = ('*--- Failure #%d%s---\n' % (f.count, ((f.pickled and ' (pickled) ') or ' ')))
end = ('%s: %s\n*--- End of Failure #%s ---\n' % (reflect.qual(f.type), reflect.safe_str(f.value), f.count))
self.assertTracebackFormat(tb, start, end)
linesWithVars = [line for line in tb.splitlines() if line.startswith(' ')]
if captureVars:
self.assertNotEqual([], linesWithVars)
if cleanFailure:
line = ' exampleLocalVar : "\'xyz\'"'
else:
line = " exampleLocalVar : 'xyz'"
self.assertIn(line, linesWithVars)
else:
self.assertEqual([], linesWithVars)
self.assertIn(' [Capture of Locals and Globals disabled (use captureVars=True)]\n', tb) |
def assertBriefTraceback(self, captureVars=False):
"\n Assert that L{printBriefTraceback} produces and prints a brief\n traceback.\n\n The brief traceback consists of a header::\n\n Traceback: <type 'exceptions.ZeroDivisionError'>: float division\n\n The body with the stacktrace::\n\n /twisted/trial/_synctest.py:1180:_run\n /twisted/python/util.py:1076:runWithWarningsSuppressed\n\n And the footer::\n\n --- <exception caught here> ---\n /twisted/test/test_failure.py:39:getDivisionFailure\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n "
if captureVars:
exampleLocalVar = 'abcde'
exampleLocalVar
f = getDivisionFailure()
out = NativeStringIO()
f.printBriefTraceback(out)
tb = out.getvalue()
stack = ''
for (method, filename, lineno, localVars, globalVars) in f.frames:
stack += ('%s:%s:%s\n' % (filename, lineno, method))
zde = repr(ZeroDivisionError)
self.assertTracebackFormat(tb, ('Traceback: %s: ' % (zde,)), ('%s\n%s' % (failure.EXCEPTION_CAUGHT_HERE, stack)))
if captureVars:
self.assertIsNone(re.search('exampleLocalVar.*abcde', tb)) | 4,406,321,726,828,651,500 | Assert that L{printBriefTraceback} produces and prints a brief
traceback.
The brief traceback consists of a header::
Traceback: <type 'exceptions.ZeroDivisionError'>: float division
The body with the stacktrace::
/twisted/trial/_synctest.py:1180:_run
/twisted/python/util.py:1076:runWithWarningsSuppressed
And the footer::
--- <exception caught here> ---
/twisted/test/test_failure.py:39:getDivisionFailure
@param captureVars: Enables L{Failure.captureVars}.
@type captureVars: C{bool} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertBriefTraceback | XZH950926/meizitu | python | def assertBriefTraceback(self, captureVars=False):
"\n Assert that L{printBriefTraceback} produces and prints a brief\n traceback.\n\n The brief traceback consists of a header::\n\n Traceback: <type 'exceptions.ZeroDivisionError'>: float division\n\n The body with the stacktrace::\n\n /twisted/trial/_synctest.py:1180:_run\n /twisted/python/util.py:1076:runWithWarningsSuppressed\n\n And the footer::\n\n --- <exception caught here> ---\n /twisted/test/test_failure.py:39:getDivisionFailure\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n "
if captureVars:
exampleLocalVar = 'abcde'
exampleLocalVar
f = getDivisionFailure()
out = NativeStringIO()
f.printBriefTraceback(out)
tb = out.getvalue()
stack =
for (method, filename, lineno, localVars, globalVars) in f.frames:
stack += ('%s:%s:%s\n' % (filename, lineno, method))
zde = repr(ZeroDivisionError)
self.assertTracebackFormat(tb, ('Traceback: %s: ' % (zde,)), ('%s\n%s' % (failure.EXCEPTION_CAUGHT_HERE, stack)))
if captureVars:
self.assertIsNone(re.search('exampleLocalVar.*abcde', tb)) |
def assertDefaultTraceback(self, captureVars=False):
'\n Assert that L{printTraceback} produces and prints a default traceback.\n\n The default traceback consists of a header::\n\n Traceback (most recent call last):\n\n The body with traceback::\n\n File "/twisted/trial/_synctest.py", line 1180, in _run\n runWithWarningsSuppressed(suppress, method)\n\n And the footer::\n\n --- <exception caught here> ---\n File "twisted/test/test_failure.py", line 39, in getDivisionFailure\n 1/0\n exceptions.ZeroDivisionError: float division\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n '
if captureVars:
exampleLocalVar = 'xyzzy'
exampleLocalVar
f = getDivisionFailure(captureVars=captureVars)
out = NativeStringIO()
f.printTraceback(out)
tb = out.getvalue()
stack = ''
for (method, filename, lineno, localVars, globalVars) in f.frames:
stack += (' File "%s", line %s, in %s\n' % (filename, lineno, method))
stack += (' %s\n' % (linecache.getline(filename, lineno).strip(),))
self.assertTracebackFormat(tb, 'Traceback (most recent call last):', ('%s\n%s%s: %s\n' % (failure.EXCEPTION_CAUGHT_HERE, stack, reflect.qual(f.type), reflect.safe_str(f.value))))
if captureVars:
self.assertIsNone(re.search('exampleLocalVar.*xyzzy', tb)) | -281,057,180,494,909,540 | Assert that L{printTraceback} produces and prints a default traceback.
The default traceback consists of a header::
Traceback (most recent call last):
The body with traceback::
File "/twisted/trial/_synctest.py", line 1180, in _run
runWithWarningsSuppressed(suppress, method)
And the footer::
--- <exception caught here> ---
File "twisted/test/test_failure.py", line 39, in getDivisionFailure
1/0
exceptions.ZeroDivisionError: float division
@param captureVars: Enables L{Failure.captureVars}.
@type captureVars: C{bool} | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | assertDefaultTraceback | XZH950926/meizitu | python | def assertDefaultTraceback(self, captureVars=False):
'\n Assert that L{printTraceback} produces and prints a default traceback.\n\n The default traceback consists of a header::\n\n Traceback (most recent call last):\n\n The body with traceback::\n\n File "/twisted/trial/_synctest.py", line 1180, in _run\n runWithWarningsSuppressed(suppress, method)\n\n And the footer::\n\n --- <exception caught here> ---\n File "twisted/test/test_failure.py", line 39, in getDivisionFailure\n 1/0\n exceptions.ZeroDivisionError: float division\n\n @param captureVars: Enables L{Failure.captureVars}.\n @type captureVars: C{bool}\n '
if captureVars:
exampleLocalVar = 'xyzzy'
exampleLocalVar
f = getDivisionFailure(captureVars=captureVars)
out = NativeStringIO()
f.printTraceback(out)
tb = out.getvalue()
stack =
for (method, filename, lineno, localVars, globalVars) in f.frames:
stack += (' File "%s", line %s, in %s\n' % (filename, lineno, method))
stack += (' %s\n' % (linecache.getline(filename, lineno).strip(),))
self.assertTracebackFormat(tb, 'Traceback (most recent call last):', ('%s\n%s%s: %s\n' % (failure.EXCEPTION_CAUGHT_HERE, stack, reflect.qual(f.type), reflect.safe_str(f.value))))
if captureVars:
self.assertIsNone(re.search('exampleLocalVar.*xyzzy', tb)) |
def test_printDetailedTraceback(self):
"\n L{printDetailedTraceback} returns a detailed traceback including the\n L{Failure}'s count.\n "
self.assertDetailedTraceback() | 4,912,148,342,093,289,000 | L{printDetailedTraceback} returns a detailed traceback including the
L{Failure}'s count. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printDetailedTraceback | XZH950926/meizitu | python | def test_printDetailedTraceback(self):
"\n L{printDetailedTraceback} returns a detailed traceback including the\n L{Failure}'s count.\n "
self.assertDetailedTraceback() |
def test_printBriefTraceback(self):
'\n L{printBriefTraceback} returns a brief traceback.\n '
self.assertBriefTraceback() | 3,682,398,796,826,470,400 | L{printBriefTraceback} returns a brief traceback. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printBriefTraceback | XZH950926/meizitu | python | def test_printBriefTraceback(self):
'\n \n '
self.assertBriefTraceback() |
def test_printTraceback(self):
'\n L{printTraceback} returns a traceback.\n '
self.assertDefaultTraceback() | 4,268,356,974,114,568,000 | L{printTraceback} returns a traceback. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printTraceback | XZH950926/meizitu | python | def test_printTraceback(self):
'\n \n '
self.assertDefaultTraceback() |
def test_printDetailedTracebackCapturedVars(self):
'\n L{printDetailedTraceback} captures the locals and globals for its\n stack frames and adds them to the traceback, when called on a\n L{Failure} constructed with C{captureVars=True}.\n '
self.assertDetailedTraceback(captureVars=True) | -1,798,578,512,415,068,000 | L{printDetailedTraceback} captures the locals and globals for its
stack frames and adds them to the traceback, when called on a
L{Failure} constructed with C{captureVars=True}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printDetailedTracebackCapturedVars | XZH950926/meizitu | python | def test_printDetailedTracebackCapturedVars(self):
'\n L{printDetailedTraceback} captures the locals and globals for its\n stack frames and adds them to the traceback, when called on a\n L{Failure} constructed with C{captureVars=True}.\n '
self.assertDetailedTraceback(captureVars=True) |
def test_printBriefTracebackCapturedVars(self):
'\n L{printBriefTraceback} returns a brief traceback when called on a\n L{Failure} constructed with C{captureVars=True}.\n\n Local variables on the stack can not be seen in the resulting\n traceback.\n '
self.assertBriefTraceback(captureVars=True) | 7,831,598,147,435,002,000 | L{printBriefTraceback} returns a brief traceback when called on a
L{Failure} constructed with C{captureVars=True}.
Local variables on the stack can not be seen in the resulting
traceback. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printBriefTracebackCapturedVars | XZH950926/meizitu | python | def test_printBriefTracebackCapturedVars(self):
'\n L{printBriefTraceback} returns a brief traceback when called on a\n L{Failure} constructed with C{captureVars=True}.\n\n Local variables on the stack can not be seen in the resulting\n traceback.\n '
self.assertBriefTraceback(captureVars=True) |
def test_printTracebackCapturedVars(self):
'\n L{printTraceback} returns a traceback when called on a L{Failure}\n constructed with C{captureVars=True}.\n\n Local variables on the stack can not be seen in the resulting\n traceback.\n '
self.assertDefaultTraceback(captureVars=True) | -5,146,097,845,496,934,000 | L{printTraceback} returns a traceback when called on a L{Failure}
constructed with C{captureVars=True}.
Local variables on the stack can not be seen in the resulting
traceback. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printTracebackCapturedVars | XZH950926/meizitu | python | def test_printTracebackCapturedVars(self):
'\n L{printTraceback} returns a traceback when called on a L{Failure}\n constructed with C{captureVars=True}.\n\n Local variables on the stack can not be seen in the resulting\n traceback.\n '
self.assertDefaultTraceback(captureVars=True) |
def test_printDetailedTracebackCapturedVarsCleaned(self):
'\n C{printDetailedTraceback} includes information about local variables on\n the stack after C{cleanFailure} has been called.\n '
self.assertDetailedTraceback(captureVars=True, cleanFailure=True) | 142,082,274,718,069,710 | C{printDetailedTraceback} includes information about local variables on
the stack after C{cleanFailure} has been called. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_printDetailedTracebackCapturedVarsCleaned | XZH950926/meizitu | python | def test_printDetailedTracebackCapturedVarsCleaned(self):
'\n C{printDetailedTraceback} includes information about local variables on\n the stack after C{cleanFailure} has been called.\n '
self.assertDetailedTraceback(captureVars=True, cleanFailure=True) |
def test_invalidFormatFramesDetail(self):
'\n L{failure.format_frames} raises a L{ValueError} if the supplied\n C{detail} level is unknown.\n '
self.assertRaises(ValueError, failure.format_frames, None, None, detail='noisia') | 264,949,235,067,547,040 | L{failure.format_frames} raises a L{ValueError} if the supplied
C{detail} level is unknown. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_invalidFormatFramesDetail | XZH950926/meizitu | python | def test_invalidFormatFramesDetail(self):
'\n L{failure.format_frames} raises a L{ValueError} if the supplied\n C{detail} level is unknown.\n '
self.assertRaises(ValueError, failure.format_frames, None, None, detail='noisia') |
def test_stringExceptionConstruction(self):
'\n Constructing a C{Failure} with a string as its exception value raises\n a C{TypeError}, as this is no longer supported as of Python 2.6.\n '
exc = self.assertRaises(TypeError, failure.Failure, 'ono!')
self.assertIn('Strings are not supported by Failure', str(exc)) | 7,303,733,056,566,425,000 | Constructing a C{Failure} with a string as its exception value raises
a C{TypeError}, as this is no longer supported as of Python 2.6. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_stringExceptionConstruction | XZH950926/meizitu | python | def test_stringExceptionConstruction(self):
'\n Constructing a C{Failure} with a string as its exception value raises\n a C{TypeError}, as this is no longer supported as of Python 2.6.\n '
exc = self.assertRaises(TypeError, failure.Failure, 'ono!')
self.assertIn('Strings are not supported by Failure', str(exc)) |
def test_ConstructionFails(self):
'\n Creating a Failure with no arguments causes it to try to discover the\n current interpreter exception state. If no such state exists, creating\n the Failure should raise a synchronous exception.\n '
if (sys.version_info < (3, 0)):
sys.exc_clear()
self.assertRaises(failure.NoCurrentExceptionError, failure.Failure) | 5,796,733,913,821,718,000 | Creating a Failure with no arguments causes it to try to discover the
current interpreter exception state. If no such state exists, creating
the Failure should raise a synchronous exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_ConstructionFails | XZH950926/meizitu | python | def test_ConstructionFails(self):
'\n Creating a Failure with no arguments causes it to try to discover the\n current interpreter exception state. If no such state exists, creating\n the Failure should raise a synchronous exception.\n '
if (sys.version_info < (3, 0)):
sys.exc_clear()
self.assertRaises(failure.NoCurrentExceptionError, failure.Failure) |
def test_getTracebackObject(self):
'\n If the C{Failure} has not been cleaned, then C{getTracebackObject}\n returns the traceback object that captured in its constructor.\n '
f = getDivisionFailure()
self.assertEqual(f.getTracebackObject(), f.tb) | 2,543,048,896,884,482,000 | If the C{Failure} has not been cleaned, then C{getTracebackObject}
returns the traceback object that captured in its constructor. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_getTracebackObject | XZH950926/meizitu | python | def test_getTracebackObject(self):
'\n If the C{Failure} has not been cleaned, then C{getTracebackObject}\n returns the traceback object that captured in its constructor.\n '
f = getDivisionFailure()
self.assertEqual(f.getTracebackObject(), f.tb) |
def test_getTracebackObjectFromCaptureVars(self):
'\n C{captureVars=True} has no effect on the result of\n C{getTracebackObject}.\n '
try:
(1 / 0)
except ZeroDivisionError:
noVarsFailure = failure.Failure()
varsFailure = failure.Failure(captureVars=True)
self.assertEqual(noVarsFailure.getTracebackObject(), varsFailure.tb) | -2,149,188,538,576,567,600 | C{captureVars=True} has no effect on the result of
C{getTracebackObject}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_getTracebackObjectFromCaptureVars | XZH950926/meizitu | python | def test_getTracebackObjectFromCaptureVars(self):
'\n C{captureVars=True} has no effect on the result of\n C{getTracebackObject}.\n '
try:
(1 / 0)
except ZeroDivisionError:
noVarsFailure = failure.Failure()
varsFailure = failure.Failure(captureVars=True)
self.assertEqual(noVarsFailure.getTracebackObject(), varsFailure.tb) |
def test_getTracebackObjectFromClean(self):
'\n If the Failure has been cleaned, then C{getTracebackObject} returns an\n object that looks the same to L{traceback.extract_tb}.\n '
f = getDivisionFailure()
expected = traceback.extract_tb(f.getTracebackObject())
f.cleanFailure()
observed = traceback.extract_tb(f.getTracebackObject())
self.assertIsNotNone(expected)
self.assertEqual(expected, observed) | -7,771,214,461,307,343,000 | If the Failure has been cleaned, then C{getTracebackObject} returns an
object that looks the same to L{traceback.extract_tb}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_getTracebackObjectFromClean | XZH950926/meizitu | python | def test_getTracebackObjectFromClean(self):
'\n If the Failure has been cleaned, then C{getTracebackObject} returns an\n object that looks the same to L{traceback.extract_tb}.\n '
f = getDivisionFailure()
expected = traceback.extract_tb(f.getTracebackObject())
f.cleanFailure()
observed = traceback.extract_tb(f.getTracebackObject())
self.assertIsNotNone(expected)
self.assertEqual(expected, observed) |
def test_getTracebackObjectFromCaptureVarsAndClean(self):
'\n If the Failure was created with captureVars, then C{getTracebackObject}\n returns an object that looks the same to L{traceback.extract_tb}.\n '
f = getDivisionFailure(captureVars=True)
expected = traceback.extract_tb(f.getTracebackObject())
f.cleanFailure()
observed = traceback.extract_tb(f.getTracebackObject())
self.assertEqual(expected, observed) | -3,117,233,068,195,542,000 | If the Failure was created with captureVars, then C{getTracebackObject}
returns an object that looks the same to L{traceback.extract_tb}. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_getTracebackObjectFromCaptureVarsAndClean | XZH950926/meizitu | python | def test_getTracebackObjectFromCaptureVarsAndClean(self):
'\n If the Failure was created with captureVars, then C{getTracebackObject}\n returns an object that looks the same to L{traceback.extract_tb}.\n '
f = getDivisionFailure(captureVars=True)
expected = traceback.extract_tb(f.getTracebackObject())
f.cleanFailure()
observed = traceback.extract_tb(f.getTracebackObject())
self.assertEqual(expected, observed) |
def test_getTracebackObjectWithoutTraceback(self):
'\n L{failure.Failure}s need not be constructed with traceback objects. If\n a C{Failure} has no traceback information at all, C{getTracebackObject}\n just returns None.\n\n None is a good value, because traceback.extract_tb(None) -> [].\n '
f = failure.Failure(Exception('some error'))
self.assertIsNone(f.getTracebackObject()) | 4,546,643,067,208,967,000 | L{failure.Failure}s need not be constructed with traceback objects. If
a C{Failure} has no traceback information at all, C{getTracebackObject}
just returns None.
None is a good value, because traceback.extract_tb(None) -> []. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_getTracebackObjectWithoutTraceback | XZH950926/meizitu | python | def test_getTracebackObjectWithoutTraceback(self):
'\n L{failure.Failure}s need not be constructed with traceback objects. If\n a C{Failure} has no traceback information at all, C{getTracebackObject}\n just returns None.\n\n None is a good value, because traceback.extract_tb(None) -> [].\n '
f = failure.Failure(Exception('some error'))
self.assertIsNone(f.getTracebackObject()) |
def test_tracebackFromExceptionInPython3(self):
"\n If a L{failure.Failure} is constructed with an exception but no\n traceback in Python 3, the traceback will be extracted from the\n exception's C{__traceback__} attribute.\n "
try:
(1 / 0)
except:
(klass, exception, tb) = sys.exc_info()
f = failure.Failure(exception)
self.assertIs(f.tb, tb) | -6,128,524,984,914,566,000 | If a L{failure.Failure} is constructed with an exception but no
traceback in Python 3, the traceback will be extracted from the
exception's C{__traceback__} attribute. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_tracebackFromExceptionInPython3 | XZH950926/meizitu | python | def test_tracebackFromExceptionInPython3(self):
"\n If a L{failure.Failure} is constructed with an exception but no\n traceback in Python 3, the traceback will be extracted from the\n exception's C{__traceback__} attribute.\n "
try:
(1 / 0)
except:
(klass, exception, tb) = sys.exc_info()
f = failure.Failure(exception)
self.assertIs(f.tb, tb) |
def test_cleanFailureRemovesTracebackInPython3(self):
'\n L{failure.Failure.cleanFailure} sets the C{__traceback__} attribute of\n the exception to L{None} in Python 3.\n '
f = getDivisionFailure()
self.assertIsNotNone(f.tb)
self.assertIs(f.value.__traceback__, f.tb)
f.cleanFailure()
self.assertIsNone(f.value.__traceback__) | 5,851,350,304,530,237,000 | L{failure.Failure.cleanFailure} sets the C{__traceback__} attribute of
the exception to L{None} in Python 3. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_cleanFailureRemovesTracebackInPython3 | XZH950926/meizitu | python | def test_cleanFailureRemovesTracebackInPython3(self):
'\n L{failure.Failure.cleanFailure} sets the C{__traceback__} attribute of\n the exception to L{None} in Python 3.\n '
f = getDivisionFailure()
self.assertIsNotNone(f.tb)
self.assertIs(f.value.__traceback__, f.tb)
f.cleanFailure()
self.assertIsNone(f.value.__traceback__) |
def test_repr(self):
'\n The C{repr} of a L{failure.Failure} shows the type and string\n representation of the underlying exception.\n '
f = getDivisionFailure()
typeName = reflect.fullyQualifiedName(ZeroDivisionError)
self.assertEqual(repr(f), ('<twisted.python.failure.Failure %s: division by zero>' % (typeName,))) | 58,509,812,011,884,360 | The C{repr} of a L{failure.Failure} shows the type and string
representation of the underlying exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_repr | XZH950926/meizitu | python | def test_repr(self):
'\n The C{repr} of a L{failure.Failure} shows the type and string\n representation of the underlying exception.\n '
f = getDivisionFailure()
typeName = reflect.fullyQualifiedName(ZeroDivisionError)
self.assertEqual(repr(f), ('<twisted.python.failure.Failure %s: division by zero>' % (typeName,))) |
def _brokenValueTest(self, detail):
'\n Construct a L{Failure} with an exception that raises an exception from\n its C{__str__} method and then call C{getTraceback} with the specified\n detail and verify that it returns a string.\n '
x = BrokenStr()
f = failure.Failure(x)
traceback = f.getTraceback(detail=detail)
self.assertIsInstance(traceback, str) | 5,467,890,743,580,913,000 | Construct a L{Failure} with an exception that raises an exception from
its C{__str__} method and then call C{getTraceback} with the specified
detail and verify that it returns a string. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | _brokenValueTest | XZH950926/meizitu | python | def _brokenValueTest(self, detail):
'\n Construct a L{Failure} with an exception that raises an exception from\n its C{__str__} method and then call C{getTraceback} with the specified\n detail and verify that it returns a string.\n '
x = BrokenStr()
f = failure.Failure(x)
traceback = f.getTraceback(detail=detail)
self.assertIsInstance(traceback, str) |
def test_brokenValueBriefDetail(self):
'\n A L{Failure} might wrap an exception with a C{__str__} method which\n raises an exception. In this case, calling C{getTraceback} on the\n failure with the C{"brief"} detail does not raise an exception.\n '
self._brokenValueTest('brief') | -1,727,253,668,860,962,600 | A L{Failure} might wrap an exception with a C{__str__} method which
raises an exception. In this case, calling C{getTraceback} on the
failure with the C{"brief"} detail does not raise an exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenValueBriefDetail | XZH950926/meizitu | python | def test_brokenValueBriefDetail(self):
'\n A L{Failure} might wrap an exception with a C{__str__} method which\n raises an exception. In this case, calling C{getTraceback} on the\n failure with the C{"brief"} detail does not raise an exception.\n '
self._brokenValueTest('brief') |
def test_brokenValueDefaultDetail(self):
'\n Like test_brokenValueBriefDetail, but for the C{"default"} detail case.\n '
self._brokenValueTest('default') | -260,785,803,897,932,000 | Like test_brokenValueBriefDetail, but for the C{"default"} detail case. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenValueDefaultDetail | XZH950926/meizitu | python | def test_brokenValueDefaultDetail(self):
'\n \n '
self._brokenValueTest('default') |
def test_brokenValueVerboseDetail(self):
'\n Like test_brokenValueBriefDetail, but for the C{"default"} detail case.\n '
self._brokenValueTest('verbose') | 8,684,888,862,353,874,000 | Like test_brokenValueBriefDetail, but for the C{"default"} detail case. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenValueVerboseDetail | XZH950926/meizitu | python | def test_brokenValueVerboseDetail(self):
'\n \n '
self._brokenValueTest('verbose') |
def _brokenTypeTest(self, detail):
'\n Construct a L{Failure} with an exception type that raises an exception\n from its C{__str__} method and then call C{getTraceback} with the\n specified detail and verify that it returns a string.\n '
f = failure.Failure(BrokenExceptionType())
traceback = f.getTraceback(detail=detail)
self.assertIsInstance(traceback, str) | -6,259,474,326,034,104,000 | Construct a L{Failure} with an exception type that raises an exception
from its C{__str__} method and then call C{getTraceback} with the
specified detail and verify that it returns a string. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | _brokenTypeTest | XZH950926/meizitu | python | def _brokenTypeTest(self, detail):
'\n Construct a L{Failure} with an exception type that raises an exception\n from its C{__str__} method and then call C{getTraceback} with the\n specified detail and verify that it returns a string.\n '
f = failure.Failure(BrokenExceptionType())
traceback = f.getTraceback(detail=detail)
self.assertIsInstance(traceback, str) |
def test_brokenTypeBriefDetail(self):
'\n A L{Failure} might wrap an exception the type object of which has a\n C{__str__} method which raises an exception. In this case, calling\n C{getTraceback} on the failure with the C{"brief"} detail does not raise\n an exception.\n '
self._brokenTypeTest('brief') | 4,389,251,325,825,251,000 | A L{Failure} might wrap an exception the type object of which has a
C{__str__} method which raises an exception. In this case, calling
C{getTraceback} on the failure with the C{"brief"} detail does not raise
an exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenTypeBriefDetail | XZH950926/meizitu | python | def test_brokenTypeBriefDetail(self):
'\n A L{Failure} might wrap an exception the type object of which has a\n C{__str__} method which raises an exception. In this case, calling\n C{getTraceback} on the failure with the C{"brief"} detail does not raise\n an exception.\n '
self._brokenTypeTest('brief') |
def test_brokenTypeDefaultDetail(self):
'\n Like test_brokenTypeBriefDetail, but for the C{"default"} detail case.\n '
self._brokenTypeTest('default') | -5,164,781,728,596,901,000 | Like test_brokenTypeBriefDetail, but for the C{"default"} detail case. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenTypeDefaultDetail | XZH950926/meizitu | python | def test_brokenTypeDefaultDetail(self):
'\n \n '
self._brokenTypeTest('default') |
def test_brokenTypeVerboseDetail(self):
'\n Like test_brokenTypeBriefDetail, but for the C{"verbose"} detail case.\n '
self._brokenTypeTest('verbose') | 7,688,043,825,222,599,000 | Like test_brokenTypeBriefDetail, but for the C{"verbose"} detail case. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_brokenTypeVerboseDetail | XZH950926/meizitu | python | def test_brokenTypeVerboseDetail(self):
'\n \n '
self._brokenTypeTest('verbose') |
def test_findNoFailureInExceptionHandler(self):
'\n Within an exception handler, _findFailure should return\n L{None} in case no Failure is associated with the current\n exception.\n '
try:
(1 / 0)
except:
self.assertIsNone(failure.Failure._findFailure())
else:
self.fail('No exception raised from 1/0!?') | 970,034,958,631,364,700 | Within an exception handler, _findFailure should return
L{None} in case no Failure is associated with the current
exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_findNoFailureInExceptionHandler | XZH950926/meizitu | python | def test_findNoFailureInExceptionHandler(self):
'\n Within an exception handler, _findFailure should return\n L{None} in case no Failure is associated with the current\n exception.\n '
try:
(1 / 0)
except:
self.assertIsNone(failure.Failure._findFailure())
else:
self.fail('No exception raised from 1/0!?') |
def test_findNoFailure(self):
'\n Outside of an exception handler, _findFailure should return None.\n '
if (sys.version_info < (3, 0)):
sys.exc_clear()
self.assertIsNone(sys.exc_info()[(- 1)])
self.assertIsNone(failure.Failure._findFailure()) | -2,677,873,085,622,452,700 | Outside of an exception handler, _findFailure should return None. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_findNoFailure | XZH950926/meizitu | python | def test_findNoFailure(self):
'\n \n '
if (sys.version_info < (3, 0)):
sys.exc_clear()
self.assertIsNone(sys.exc_info()[(- 1)])
self.assertIsNone(failure.Failure._findFailure()) |
def test_findFailure(self):
'\n Within an exception handler, it should be possible to find the\n original Failure that caused the current exception (if it was\n caused by raiseException).\n '
f = getDivisionFailure()
f.cleanFailure()
try:
f.raiseException()
except:
self.assertEqual(failure.Failure._findFailure(), f)
else:
self.fail('No exception raised from raiseException!?') | 8,117,886,591,194,119,000 | Within an exception handler, it should be possible to find the
original Failure that caused the current exception (if it was
caused by raiseException). | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_findFailure | XZH950926/meizitu | python | def test_findFailure(self):
'\n Within an exception handler, it should be possible to find the\n original Failure that caused the current exception (if it was\n caused by raiseException).\n '
f = getDivisionFailure()
f.cleanFailure()
try:
f.raiseException()
except:
self.assertEqual(failure.Failure._findFailure(), f)
else:
self.fail('No exception raised from raiseException!?') |
def test_failureConstructionFindsOriginalFailure(self):
'\n When a Failure is constructed in the context of an exception\n handler that is handling an exception raised by\n raiseException, the new Failure should be chained to that\n original Failure.\n '
f = getDivisionFailure()
f.cleanFailure()
try:
f.raiseException()
except:
newF = failure.Failure()
self.assertEqual(f.getTraceback(), newF.getTraceback())
else:
self.fail('No exception raised from raiseException!?') | 6,428,602,133,564,756,000 | When a Failure is constructed in the context of an exception
handler that is handling an exception raised by
raiseException, the new Failure should be chained to that
original Failure. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failureConstructionFindsOriginalFailure | XZH950926/meizitu | python | def test_failureConstructionFindsOriginalFailure(self):
'\n When a Failure is constructed in the context of an exception\n handler that is handling an exception raised by\n raiseException, the new Failure should be chained to that\n original Failure.\n '
f = getDivisionFailure()
f.cleanFailure()
try:
f.raiseException()
except:
newF = failure.Failure()
self.assertEqual(f.getTraceback(), newF.getTraceback())
else:
self.fail('No exception raised from raiseException!?') |
def test_failureConstructionWithMungedStackSucceeds(self):
'\n Pyrex and Cython are known to insert fake stack frames so as to give\n more Python-like tracebacks. These stack frames with empty code objects\n should not break extraction of the exception.\n '
try:
raiser.raiseException()
except raiser.RaiserException:
f = failure.Failure()
self.assertTrue(f.check(raiser.RaiserException))
else:
self.fail('No exception raised from extension?!') | 6,947,278,950,813,051,000 | Pyrex and Cython are known to insert fake stack frames so as to give
more Python-like tracebacks. These stack frames with empty code objects
should not break extraction of the exception. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failureConstructionWithMungedStackSucceeds | XZH950926/meizitu | python | def test_failureConstructionWithMungedStackSucceeds(self):
'\n Pyrex and Cython are known to insert fake stack frames so as to give\n more Python-like tracebacks. These stack frames with empty code objects\n should not break extraction of the exception.\n '
try:
raiser.raiseException()
except raiser.RaiserException:
f = failure.Failure()
self.assertTrue(f.check(raiser.RaiserException))
else:
self.fail('No exception raised from extension?!') |
def test_singleFrame(self):
'\n A C{_Traceback} object constructed with a single frame should be able\n to be passed to L{traceback.extract_tb}, and we should get a singleton\n list containing a (filename, lineno, methodname, line) tuple.\n '
tb = failure._Traceback([['method', 'filename.py', 123, {}, {}]])
self.assertEqual(traceback.extract_tb(tb), [_tb('filename.py', 123, 'method', None)]) | -1,393,764,764,254,306,000 | A C{_Traceback} object constructed with a single frame should be able
to be passed to L{traceback.extract_tb}, and we should get a singleton
list containing a (filename, lineno, methodname, line) tuple. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_singleFrame | XZH950926/meizitu | python | def test_singleFrame(self):
'\n A C{_Traceback} object constructed with a single frame should be able\n to be passed to L{traceback.extract_tb}, and we should get a singleton\n list containing a (filename, lineno, methodname, line) tuple.\n '
tb = failure._Traceback([['method', 'filename.py', 123, {}, {}]])
self.assertEqual(traceback.extract_tb(tb), [_tb('filename.py', 123, 'method', None)]) |
def test_manyFrames(self):
'\n A C{_Traceback} object constructed with multiple frames should be able\n to be passed to L{traceback.extract_tb}, and we should get a list\n containing a tuple for each frame.\n '
tb = failure._Traceback([['method1', 'filename.py', 123, {}, {}], ['method2', 'filename.py', 235, {}, {}]])
self.assertEqual(traceback.extract_tb(tb), [_tb('filename.py', 123, 'method1', None), _tb('filename.py', 235, 'method2', None)]) | -3,130,121,095,846,734,300 | A C{_Traceback} object constructed with multiple frames should be able
to be passed to L{traceback.extract_tb}, and we should get a list
containing a tuple for each frame. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_manyFrames | XZH950926/meizitu | python | def test_manyFrames(self):
'\n A C{_Traceback} object constructed with multiple frames should be able\n to be passed to L{traceback.extract_tb}, and we should get a list\n containing a tuple for each frame.\n '
tb = failure._Traceback([['method1', 'filename.py', 123, {}, {}], ['method2', 'filename.py', 235, {}, {}]])
self.assertEqual(traceback.extract_tb(tb), [_tb('filename.py', 123, 'method1', None), _tb('filename.py', 235, 'method2', None)]) |
def test_fakeFrameAttributes(self):
'\n L{_Frame} instances have the C{f_globals} and C{f_locals} attributes\n bound to C{dict} instance. They also have the C{f_code} attribute\n bound to something like a code object.\n '
frame = failure._Frame('dummyname', 'dummyfilename')
self.assertIsInstance(frame.f_globals, dict)
self.assertIsInstance(frame.f_locals, dict)
self.assertIsInstance(frame.f_code, failure._Code) | -2,995,569,665,752,570,400 | L{_Frame} instances have the C{f_globals} and C{f_locals} attributes
bound to C{dict} instance. They also have the C{f_code} attribute
bound to something like a code object. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_fakeFrameAttributes | XZH950926/meizitu | python | def test_fakeFrameAttributes(self):
'\n L{_Frame} instances have the C{f_globals} and C{f_locals} attributes\n bound to C{dict} instance. They also have the C{f_code} attribute\n bound to something like a code object.\n '
frame = failure._Frame('dummyname', 'dummyfilename')
self.assertIsInstance(frame.f_globals, dict)
self.assertIsInstance(frame.f_locals, dict)
self.assertIsInstance(frame.f_code, failure._Code) |
def setUp(self):
"\n Override pdb.post_mortem so we can make sure it's called.\n "
post_mortem = pdb.post_mortem
origInit = failure.Failure.__init__
def restore():
pdb.post_mortem = post_mortem
failure.Failure.__init__ = origInit
self.addCleanup(restore)
self.result = []
pdb.post_mortem = self.result.append
failure.startDebugMode() | 6,436,035,593,134,350,000 | Override pdb.post_mortem so we can make sure it's called. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | setUp | XZH950926/meizitu | python | def setUp(self):
"\n \n "
post_mortem = pdb.post_mortem
origInit = failure.Failure.__init__
def restore():
pdb.post_mortem = post_mortem
failure.Failure.__init__ = origInit
self.addCleanup(restore)
self.result = []
pdb.post_mortem = self.result.append
failure.startDebugMode() |
def test_regularFailure(self):
'\n If startDebugMode() is called, calling Failure() will first call\n pdb.post_mortem with the traceback.\n '
try:
(1 / 0)
except:
(typ, exc, tb) = sys.exc_info()
f = failure.Failure()
self.assertEqual(self.result, [tb])
self.assertFalse(f.captureVars) | -740,433,790,781,489,700 | If startDebugMode() is called, calling Failure() will first call
pdb.post_mortem with the traceback. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_regularFailure | XZH950926/meizitu | python | def test_regularFailure(self):
'\n If startDebugMode() is called, calling Failure() will first call\n pdb.post_mortem with the traceback.\n '
try:
(1 / 0)
except:
(typ, exc, tb) = sys.exc_info()
f = failure.Failure()
self.assertEqual(self.result, [tb])
self.assertFalse(f.captureVars) |
def test_captureVars(self):
'\n If startDebugMode() is called, passing captureVars to Failure() will\n not blow up.\n '
try:
(1 / 0)
except:
(typ, exc, tb) = sys.exc_info()
f = failure.Failure(captureVars=True)
self.assertEqual(self.result, [tb])
self.assertTrue(f.captureVars) | -213,307,526,298,185,900 | If startDebugMode() is called, passing captureVars to Failure() will
not blow up. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_captureVars | XZH950926/meizitu | python | def test_captureVars(self):
'\n If startDebugMode() is called, passing captureVars to Failure() will\n not blow up.\n '
try:
(1 / 0)
except:
(typ, exc, tb) = sys.exc_info()
f = failure.Failure(captureVars=True)
self.assertEqual(self.result, [tb])
self.assertTrue(f.captureVars) |
def test_throwExceptionIntoGenerator(self):
'\n It should be possible to throw the exception that a Failure\n represents into a generator.\n '
stuff = []
def generator():
try:
(yield)
except:
stuff.append(sys.exc_info())
else:
self.fail('Yield should have yielded exception.')
g = generator()
f = getDivisionFailure()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(stuff[0][0], ZeroDivisionError)
self.assertIsInstance(stuff[0][1], ZeroDivisionError)
self.assertEqual(traceback.extract_tb(stuff[0][2])[(- 1)][(- 1)], '1/0') | -7,581,980,284,546,282,000 | It should be possible to throw the exception that a Failure
represents into a generator. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_throwExceptionIntoGenerator | XZH950926/meizitu | python | def test_throwExceptionIntoGenerator(self):
'\n It should be possible to throw the exception that a Failure\n represents into a generator.\n '
stuff = []
def generator():
try:
(yield)
except:
stuff.append(sys.exc_info())
else:
self.fail('Yield should have yielded exception.')
g = generator()
f = getDivisionFailure()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(stuff[0][0], ZeroDivisionError)
self.assertIsInstance(stuff[0][1], ZeroDivisionError)
self.assertEqual(traceback.extract_tb(stuff[0][2])[(- 1)][(- 1)], '1/0') |
def test_findFailureInGenerator(self):
'\n Within an exception handler, it should be possible to find the\n original Failure that caused the current exception (if it was\n caused by throwExceptionIntoGenerator).\n '
f = getDivisionFailure()
f.cleanFailure()
foundFailures = []
def generator():
try:
(yield)
except:
foundFailures.append(failure.Failure._findFailure())
else:
self.fail('No exception sent to generator')
g = generator()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(foundFailures, [f]) | -5,148,197,409,422,034,000 | Within an exception handler, it should be possible to find the
original Failure that caused the current exception (if it was
caused by throwExceptionIntoGenerator). | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_findFailureInGenerator | XZH950926/meizitu | python | def test_findFailureInGenerator(self):
'\n Within an exception handler, it should be possible to find the\n original Failure that caused the current exception (if it was\n caused by throwExceptionIntoGenerator).\n '
f = getDivisionFailure()
f.cleanFailure()
foundFailures = []
def generator():
try:
(yield)
except:
foundFailures.append(failure.Failure._findFailure())
else:
self.fail('No exception sent to generator')
g = generator()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(foundFailures, [f]) |
def test_failureConstructionFindsOriginalFailure(self):
'\n When a Failure is constructed in the context of an exception\n handler that is handling an exception raised by\n throwExceptionIntoGenerator, the new Failure should be chained to that\n original Failure.\n '
f = getDivisionFailure()
f.cleanFailure()
newFailures = []
def generator():
try:
(yield)
except:
newFailures.append(failure.Failure())
else:
self.fail('No exception sent to generator')
g = generator()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(len(newFailures), 1)
self.assertEqual(newFailures[0].getTraceback(), f.getTraceback()) | 4,226,784,292,072,206,000 | When a Failure is constructed in the context of an exception
handler that is handling an exception raised by
throwExceptionIntoGenerator, the new Failure should be chained to that
original Failure. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_failureConstructionFindsOriginalFailure | XZH950926/meizitu | python | def test_failureConstructionFindsOriginalFailure(self):
'\n When a Failure is constructed in the context of an exception\n handler that is handling an exception raised by\n throwExceptionIntoGenerator, the new Failure should be chained to that\n original Failure.\n '
f = getDivisionFailure()
f.cleanFailure()
newFailures = []
def generator():
try:
(yield)
except:
newFailures.append(failure.Failure())
else:
self.fail('No exception sent to generator')
g = generator()
next(g)
self._throwIntoGenerator(f, g)
self.assertEqual(len(newFailures), 1)
self.assertEqual(newFailures[0].getTraceback(), f.getTraceback()) |
def test_ambiguousFailureInGenerator(self):
'\n When a generator reraises a different exception,\n L{Failure._findFailure} inside the generator should find the reraised\n exception rather than original one.\n '
def generator():
try:
try:
(yield)
except:
[][1]
except:
self.assertIsInstance(failure.Failure().value, IndexError)
g = generator()
next(g)
f = getDivisionFailure()
self._throwIntoGenerator(f, g) | -2,900,656,017,749,723,600 | When a generator reraises a different exception,
L{Failure._findFailure} inside the generator should find the reraised
exception rather than original one. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_ambiguousFailureInGenerator | XZH950926/meizitu | python | def test_ambiguousFailureInGenerator(self):
'\n When a generator reraises a different exception,\n L{Failure._findFailure} inside the generator should find the reraised\n exception rather than original one.\n '
def generator():
try:
try:
(yield)
except:
[][1]
except:
self.assertIsInstance(failure.Failure().value, IndexError)
g = generator()
next(g)
f = getDivisionFailure()
self._throwIntoGenerator(f, g) |
def test_ambiguousFailureFromGenerator(self):
'\n When a generator reraises a different exception,\n L{Failure._findFailure} above the generator should find the reraised\n exception rather than original one.\n '
def generator():
try:
(yield)
except:
[][1]
g = generator()
next(g)
f = getDivisionFailure()
try:
self._throwIntoGenerator(f, g)
except:
self.assertIsInstance(failure.Failure().value, IndexError) | 2,801,333,408,657,701,400 | When a generator reraises a different exception,
L{Failure._findFailure} above the generator should find the reraised
exception rather than original one. | fang/Twisted-18.4.0/src/twisted/test/test_failure.py | test_ambiguousFailureFromGenerator | XZH950926/meizitu | python | def test_ambiguousFailureFromGenerator(self):
'\n When a generator reraises a different exception,\n L{Failure._findFailure} above the generator should find the reraised\n exception rather than original one.\n '
def generator():
try:
(yield)
except:
[][1]
g = generator()
next(g)
f = getDivisionFailure()
try:
self._throwIntoGenerator(f, g)
except:
self.assertIsInstance(failure.Failure().value, IndexError) |
def __init__(self, ad: AppDaemon):
'Constructor.\n\n Args:\n ad: Reference to the AppDaemon object\n '
self.AD = ad
self.logger = ad.logging.get_child('_events') | 6,886,154,475,808,460,000 | Constructor.
Args:
ad: Reference to the AppDaemon object | appdaemon/events.py | __init__ | DTTerastar/appdaemon | python | def __init__(self, ad: AppDaemon):
'Constructor.\n\n Args:\n ad: Reference to the AppDaemon object\n '
self.AD = ad
self.logger = ad.logging.get_child('_events') |
async def add_event_callback(self, name, namespace, cb, event, **kwargs):
'Adds a callback for an event which is called internally by apps.\n\n Args:\n name (str): Name of the app.\n namespace (str): Namespace of the event.\n cb: Callback function.\n event (str): Name of the event.\n **kwargs: List of values to filter on, and additional arguments to pass to the callback.\n\n Returns:\n ``None`` or the reference to the callback handle.\n\n '
if (self.AD.threading.validate_pin(name, kwargs) is True):
if ('pin' in kwargs):
pin_app = kwargs['pin_app']
else:
pin_app = self.AD.app_management.objects[name]['pin_app']
if ('pin_thread' in kwargs):
pin_thread = kwargs['pin_thread']
pin_app = True
else:
pin_thread = self.AD.app_management.objects[name]['pin_thread']
async with self.AD.callbacks.callbacks_lock:
if (name not in self.AD.callbacks.callbacks):
self.AD.callbacks.callbacks[name] = {}
handle = uuid.uuid4().hex
self.AD.callbacks.callbacks[name][handle] = {'name': name, 'id': self.AD.app_management.objects[name]['id'], 'type': 'event', 'function': cb, 'namespace': namespace, 'event': event, 'pin_app': pin_app, 'pin_thread': pin_thread, 'kwargs': kwargs}
if ('timeout' in kwargs):
exec_time = ((await self.AD.sched.get_now()) + datetime.timedelta(seconds=int(kwargs['timeout'])))
kwargs['__timeout'] = (await self.AD.sched.insert_schedule(name, exec_time, None, False, None, __event_handle=handle))
(await self.AD.state.add_entity('admin', 'event_callback.{}'.format(handle), 'active', {'app': name, 'event_name': event, 'function': cb.__name__, 'pinned': pin_app, 'pinned_thread': pin_thread, 'fired': 0, 'executed': 0, 'kwargs': kwargs}))
return handle
else:
return None | -440,333,686,961,987,840 | Adds a callback for an event which is called internally by apps.
Args:
name (str): Name of the app.
namespace (str): Namespace of the event.
cb: Callback function.
event (str): Name of the event.
**kwargs: List of values to filter on, and additional arguments to pass to the callback.
Returns:
``None`` or the reference to the callback handle. | appdaemon/events.py | add_event_callback | DTTerastar/appdaemon | python | async def add_event_callback(self, name, namespace, cb, event, **kwargs):
'Adds a callback for an event which is called internally by apps.\n\n Args:\n name (str): Name of the app.\n namespace (str): Namespace of the event.\n cb: Callback function.\n event (str): Name of the event.\n **kwargs: List of values to filter on, and additional arguments to pass to the callback.\n\n Returns:\n ``None`` or the reference to the callback handle.\n\n '
if (self.AD.threading.validate_pin(name, kwargs) is True):
if ('pin' in kwargs):
pin_app = kwargs['pin_app']
else:
pin_app = self.AD.app_management.objects[name]['pin_app']
if ('pin_thread' in kwargs):
pin_thread = kwargs['pin_thread']
pin_app = True
else:
pin_thread = self.AD.app_management.objects[name]['pin_thread']
async with self.AD.callbacks.callbacks_lock:
if (name not in self.AD.callbacks.callbacks):
self.AD.callbacks.callbacks[name] = {}
handle = uuid.uuid4().hex
self.AD.callbacks.callbacks[name][handle] = {'name': name, 'id': self.AD.app_management.objects[name]['id'], 'type': 'event', 'function': cb, 'namespace': namespace, 'event': event, 'pin_app': pin_app, 'pin_thread': pin_thread, 'kwargs': kwargs}
if ('timeout' in kwargs):
exec_time = ((await self.AD.sched.get_now()) + datetime.timedelta(seconds=int(kwargs['timeout'])))
kwargs['__timeout'] = (await self.AD.sched.insert_schedule(name, exec_time, None, False, None, __event_handle=handle))
(await self.AD.state.add_entity('admin', 'event_callback.{}'.format(handle), 'active', {'app': name, 'event_name': event, 'function': cb.__name__, 'pinned': pin_app, 'pinned_thread': pin_thread, 'fired': 0, 'executed': 0, 'kwargs': kwargs}))
return handle
else:
return None |
async def cancel_event_callback(self, name, handle):
'Cancels an event callback.\n\n Args:\n name (str): Name of the app or module.\n handle: Previously supplied callback handle for the callback.\n\n Returns:\n None.\n\n '
executed = False
async with self.AD.callbacks.callbacks_lock:
if ((name in self.AD.callbacks.callbacks) and (handle in self.AD.callbacks.callbacks[name])):
del self.AD.callbacks.callbacks[name][handle]
(await self.AD.state.remove_entity('admin', 'event_callback.{}'.format(handle)))
executed = True
if ((name in self.AD.callbacks.callbacks) and (self.AD.callbacks.callbacks[name] == {})):
del self.AD.callbacks.callbacks[name]
if (not executed):
self.logger.warning("Invalid callback handle '{}' in cancel_event_callback() from app {}".format(handle, name))
return executed | 895,694,378,937,561,600 | Cancels an event callback.
Args:
name (str): Name of the app or module.
handle: Previously supplied callback handle for the callback.
Returns:
None. | appdaemon/events.py | cancel_event_callback | DTTerastar/appdaemon | python | async def cancel_event_callback(self, name, handle):
'Cancels an event callback.\n\n Args:\n name (str): Name of the app or module.\n handle: Previously supplied callback handle for the callback.\n\n Returns:\n None.\n\n '
executed = False
async with self.AD.callbacks.callbacks_lock:
if ((name in self.AD.callbacks.callbacks) and (handle in self.AD.callbacks.callbacks[name])):
del self.AD.callbacks.callbacks[name][handle]
(await self.AD.state.remove_entity('admin', 'event_callback.{}'.format(handle)))
executed = True
if ((name in self.AD.callbacks.callbacks) and (self.AD.callbacks.callbacks[name] == {})):
del self.AD.callbacks.callbacks[name]
if (not executed):
self.logger.warning("Invalid callback handle '{}' in cancel_event_callback() from app {}".format(handle, name))
return executed |
async def info_event_callback(self, name, handle):
'Gets the information of an event callback.\n\n Args:\n name (str): Name of the app or subsystem.\n handle: Previously supplied handle for the callback.\n\n Returns:\n A dictionary of callback entries or rise a ``ValueError`` if an invalid handle is provided.\n\n '
async with self.AD.callbacks.callbacks_lock:
if ((name in self.AD.callbacks.callbacks) and (handle in self.AD.callbacks.callbacks[name])):
callback = self.AD.callbacks.callbacks[name][handle]
return (callback['event'], callback['kwargs'].copy())
else:
raise ValueError('Invalid handle: {}'.format(handle)) | -1,162,147,695,080,955,400 | Gets the information of an event callback.
Args:
name (str): Name of the app or subsystem.
handle: Previously supplied handle for the callback.
Returns:
A dictionary of callback entries or rise a ``ValueError`` if an invalid handle is provided. | appdaemon/events.py | info_event_callback | DTTerastar/appdaemon | python | async def info_event_callback(self, name, handle):
'Gets the information of an event callback.\n\n Args:\n name (str): Name of the app or subsystem.\n handle: Previously supplied handle for the callback.\n\n Returns:\n A dictionary of callback entries or rise a ``ValueError`` if an invalid handle is provided.\n\n '
async with self.AD.callbacks.callbacks_lock:
if ((name in self.AD.callbacks.callbacks) and (handle in self.AD.callbacks.callbacks[name])):
callback = self.AD.callbacks.callbacks[name][handle]
return (callback['event'], callback['kwargs'].copy())
else:
raise ValueError('Invalid handle: {}'.format(handle)) |
async def fire_event(self, namespace, event, **kwargs):
'Fires an event.\n\n If the namespace does not have a plugin associated with it, the event will be fired locally.\n If a plugin is associated, the firing of the event will be delegated to the plugin, under the\n understanding that when the event is fired, the plugin will notify appdaemon that it occurred,\n usually via the system the plugin is communicating with.\n\n Args:\n namespace (str): Namespace for the event to be fired in.\n event (str): Name of the event.\n **kwargs: Arguments to associate with the event.\n\n Returns:\n None.\n\n '
self.logger.debug('fire_plugin_event() %s %s %s', namespace, event, kwargs)
plugin = (await self.AD.plugins.get_plugin_object(namespace))
if hasattr(plugin, 'fire_plugin_event'):
(await plugin.fire_plugin_event(event, namespace, **kwargs))
else:
(await self.AD.events.process_event(namespace, {'event_type': event, 'data': kwargs})) | 1,890,551,796,475,495,000 | Fires an event.
If the namespace does not have a plugin associated with it, the event will be fired locally.
If a plugin is associated, the firing of the event will be delegated to the plugin, under the
understanding that when the event is fired, the plugin will notify appdaemon that it occurred,
usually via the system the plugin is communicating with.
Args:
namespace (str): Namespace for the event to be fired in.
event (str): Name of the event.
**kwargs: Arguments to associate with the event.
Returns:
None. | appdaemon/events.py | fire_event | DTTerastar/appdaemon | python | async def fire_event(self, namespace, event, **kwargs):
'Fires an event.\n\n If the namespace does not have a plugin associated with it, the event will be fired locally.\n If a plugin is associated, the firing of the event will be delegated to the plugin, under the\n understanding that when the event is fired, the plugin will notify appdaemon that it occurred,\n usually via the system the plugin is communicating with.\n\n Args:\n namespace (str): Namespace for the event to be fired in.\n event (str): Name of the event.\n **kwargs: Arguments to associate with the event.\n\n Returns:\n None.\n\n '
self.logger.debug('fire_plugin_event() %s %s %s', namespace, event, kwargs)
plugin = (await self.AD.plugins.get_plugin_object(namespace))
if hasattr(plugin, 'fire_plugin_event'):
(await plugin.fire_plugin_event(event, namespace, **kwargs))
else:
(await self.AD.events.process_event(namespace, {'event_type': event, 'data': kwargs})) |
async def process_event(self, namespace, data):
'Processes an event that has been received either locally or from a plugin.\n\n Args:\n namespace (str): Namespace the event was fired in.\n data: Data associated with the event.\n\n Returns:\n None.\n\n '
try:
self.logger.debug('Event type:%s:', data['event_type'])
self.logger.debug(data['data'])
if ((self.AD.sched is not None) and (self.AD.sched.realtime is False) and (namespace != 'admin')):
(await self.AD.sched.kick())
if (data['event_type'] == 'state_changed'):
if (('entity_id' in data['data']) and ('new_state' in data['data'])):
if (data['data']['new_state'] is None):
return
entity_id = data['data']['entity_id']
self.AD.state.set_state_simple(namespace, entity_id, data['data']['new_state'])
if ((self.AD.apps is True) and (namespace != 'admin')):
(await self.AD.state.process_state_callbacks(namespace, data))
else:
self.logger.warning("Malformed 'state_changed' event: %s", data['data'])
return
if (data['event_type'] == '__AD_LOG_EVENT'):
if (await self.has_log_callback(data['data']['app_name'])):
self.logger.debug('Discarding event for loop avoidance')
return
(await self.AD.logging.process_log_callbacks(namespace, data))
if (self.AD.apps is True):
(await self.process_event_callbacks(namespace, data))
if (self.AD.http is not None):
if (data['event_type'] == 'state_changed'):
if (data['data']['new_state'] == data['data']['old_state']):
return
if ('ts' in data['data']):
ts = data['data'].pop('ts')
mydata = deepcopy(data)
data['data']['ts'] = ts
else:
mydata = deepcopy(data)
(await self.AD.http.stream_update(namespace, mydata))
except Exception:
self.logger.warning(('-' * 60))
self.logger.warning('Unexpected error during process_event()')
self.logger.warning(('-' * 60))
self.logger.warning(traceback.format_exc())
self.logger.warning(('-' * 60)) | 6,674,242,740,453,871,000 | Processes an event that has been received either locally or from a plugin.
Args:
namespace (str): Namespace the event was fired in.
data: Data associated with the event.
Returns:
None. | appdaemon/events.py | process_event | DTTerastar/appdaemon | python | async def process_event(self, namespace, data):
'Processes an event that has been received either locally or from a plugin.\n\n Args:\n namespace (str): Namespace the event was fired in.\n data: Data associated with the event.\n\n Returns:\n None.\n\n '
try:
self.logger.debug('Event type:%s:', data['event_type'])
self.logger.debug(data['data'])
if ((self.AD.sched is not None) and (self.AD.sched.realtime is False) and (namespace != 'admin')):
(await self.AD.sched.kick())
if (data['event_type'] == 'state_changed'):
if (('entity_id' in data['data']) and ('new_state' in data['data'])):
if (data['data']['new_state'] is None):
return
entity_id = data['data']['entity_id']
self.AD.state.set_state_simple(namespace, entity_id, data['data']['new_state'])
if ((self.AD.apps is True) and (namespace != 'admin')):
(await self.AD.state.process_state_callbacks(namespace, data))
else:
self.logger.warning("Malformed 'state_changed' event: %s", data['data'])
return
if (data['event_type'] == '__AD_LOG_EVENT'):
if (await self.has_log_callback(data['data']['app_name'])):
self.logger.debug('Discarding event for loop avoidance')
return
(await self.AD.logging.process_log_callbacks(namespace, data))
if (self.AD.apps is True):
(await self.process_event_callbacks(namespace, data))
if (self.AD.http is not None):
if (data['event_type'] == 'state_changed'):
if (data['data']['new_state'] == data['data']['old_state']):
return
if ('ts' in data['data']):
ts = data['data'].pop('ts')
mydata = deepcopy(data)
data['data']['ts'] = ts
else:
mydata = deepcopy(data)
(await self.AD.http.stream_update(namespace, mydata))
except Exception:
self.logger.warning(('-' * 60))
self.logger.warning('Unexpected error during process_event()')
self.logger.warning(('-' * 60))
self.logger.warning(traceback.format_exc())
self.logger.warning(('-' * 60)) |
async def has_log_callback(self, name):
'Returns ``True`` if the app has a log callback, ``False`` otherwise.\n\n Used to prevent callback loops. In the calling logic, if this function returns\n ``True`` the resulting logging event will be suppressed.\n\n Args:\n name (str): Name of the app.\n\n '
has_log_callback = False
if (name == 'AppDaemon._stream'):
has_log_callback = True
else:
async with self.AD.callbacks.callbacks_lock:
for callback in self.AD.callbacks.callbacks:
for _uuid in self.AD.callbacks.callbacks[callback]:
cb = self.AD.callbacks.callbacks[callback][_uuid]
if ((cb['name'] == name) and (cb['type'] == 'event') and (cb['event'] == '__AD_LOG_EVENT')):
has_log_callback = True
elif ((cb['name'] == name) and (cb['type'] == 'log')):
has_log_callback = True
return has_log_callback | 1,133,120,197,770,422,000 | Returns ``True`` if the app has a log callback, ``False`` otherwise.
Used to prevent callback loops. In the calling logic, if this function returns
``True`` the resulting logging event will be suppressed.
Args:
name (str): Name of the app. | appdaemon/events.py | has_log_callback | DTTerastar/appdaemon | python | async def has_log_callback(self, name):
'Returns ``True`` if the app has a log callback, ``False`` otherwise.\n\n Used to prevent callback loops. In the calling logic, if this function returns\n ``True`` the resulting logging event will be suppressed.\n\n Args:\n name (str): Name of the app.\n\n '
has_log_callback = False
if (name == 'AppDaemon._stream'):
has_log_callback = True
else:
async with self.AD.callbacks.callbacks_lock:
for callback in self.AD.callbacks.callbacks:
for _uuid in self.AD.callbacks.callbacks[callback]:
cb = self.AD.callbacks.callbacks[callback][_uuid]
if ((cb['name'] == name) and (cb['type'] == 'event') and (cb['event'] == '__AD_LOG_EVENT')):
has_log_callback = True
elif ((cb['name'] == name) and (cb['type'] == 'log')):
has_log_callback = True
return has_log_callback |
async def process_event_callbacks(self, namespace, data):
'Processes a pure event callback.\n\n Locate any callbacks that may be registered for this event, check for filters and if appropriate,\n dispatch the event for further checking and eventual action.\n\n Args:\n namespace (str): Namespace of the event.\n data: Data associated with the event.\n\n Returns:\n None.\n\n '
self.logger.debug('process_event_callbacks() %s %s', namespace, data)
removes = []
async with self.AD.callbacks.callbacks_lock:
for name in self.AD.callbacks.callbacks.keys():
for uuid_ in self.AD.callbacks.callbacks[name]:
callback = self.AD.callbacks.callbacks[name][uuid_]
if ((callback['namespace'] == namespace) or (callback['namespace'] == 'global') or (namespace == 'global')):
if (('event' in callback) and (((callback['event'] is None) and (data['event_type'][:2] != '__')) or (data['event_type'] == callback['event']))):
_run = True
for key in callback['kwargs']:
if ((key in data['data']) and (callback['kwargs'][key] != data['data'][key])):
_run = False
if (data['event_type'] == '__AD_LOG_EVENT'):
if (('log' in callback['kwargs']) and (callback['kwargs']['log'] != data['data']['log_type'])):
_run = False
if _run:
if (name in self.AD.app_management.objects):
executed = (await self.AD.threading.dispatch_worker(name, {'id': uuid_, 'name': name, 'objectid': self.AD.app_management.objects[name]['id'], 'type': 'event', 'event': data['event_type'], 'function': callback['function'], 'data': data['data'], 'pin_app': callback['pin_app'], 'pin_thread': callback['pin_thread'], 'kwargs': callback['kwargs']}))
if (executed is True):
remove = callback['kwargs'].get('oneshot', False)
if (remove is True):
removes.append({'name': name, 'uuid': uuid_})
for remove in removes:
(await self.cancel_event_callback(remove['name'], remove['uuid'])) | 1,583,161,895,843,194,000 | Processes a pure event callback.
Locate any callbacks that may be registered for this event, check for filters and if appropriate,
dispatch the event for further checking and eventual action.
Args:
namespace (str): Namespace of the event.
data: Data associated with the event.
Returns:
None. | appdaemon/events.py | process_event_callbacks | DTTerastar/appdaemon | python | async def process_event_callbacks(self, namespace, data):
'Processes a pure event callback.\n\n Locate any callbacks that may be registered for this event, check for filters and if appropriate,\n dispatch the event for further checking and eventual action.\n\n Args:\n namespace (str): Namespace of the event.\n data: Data associated with the event.\n\n Returns:\n None.\n\n '
self.logger.debug('process_event_callbacks() %s %s', namespace, data)
removes = []
async with self.AD.callbacks.callbacks_lock:
for name in self.AD.callbacks.callbacks.keys():
for uuid_ in self.AD.callbacks.callbacks[name]:
callback = self.AD.callbacks.callbacks[name][uuid_]
if ((callback['namespace'] == namespace) or (callback['namespace'] == 'global') or (namespace == 'global')):
if (('event' in callback) and (((callback['event'] is None) and (data['event_type'][:2] != '__')) or (data['event_type'] == callback['event']))):
_run = True
for key in callback['kwargs']:
if ((key in data['data']) and (callback['kwargs'][key] != data['data'][key])):
_run = False
if (data['event_type'] == '__AD_LOG_EVENT'):
if (('log' in callback['kwargs']) and (callback['kwargs']['log'] != data['data']['log_type'])):
_run = False
if _run:
if (name in self.AD.app_management.objects):
executed = (await self.AD.threading.dispatch_worker(name, {'id': uuid_, 'name': name, 'objectid': self.AD.app_management.objects[name]['id'], 'type': 'event', 'event': data['event_type'], 'function': callback['function'], 'data': data['data'], 'pin_app': callback['pin_app'], 'pin_thread': callback['pin_thread'], 'kwargs': callback['kwargs']}))
if (executed is True):
remove = callback['kwargs'].get('oneshot', False)
if (remove is True):
removes.append({'name': name, 'uuid': uuid_})
for remove in removes:
(await self.cancel_event_callback(remove['name'], remove['uuid'])) |
def source_regex_locations(self):
' Test that restricting source expressions to files & to functions. '
exe = os.path.join(os.getcwd(), 'a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
target_files = lldb.SBFileSpecList()
target_files.Append(lldb.SBFileSpec('a.c'))
func_names = lldb.SBStringList()
func_names.AppendString('a_func')
source_regex = 'Set . breakpoint here'
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, func_names)
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 1), ('a.c in a_func should give one breakpoint, got %d.' % num_locations))
loc = main_break.GetLocationAtIndex(0)
self.assertTrue(loc.IsValid(), 'Got a valid location.')
address = loc.GetAddress()
self.assertTrue(address.IsValid(), 'Got a valid address from the location.')
a_func_line = line_number('a.c', 'Set A breakpoint here')
line_entry = address.GetLineEntry()
self.assertTrue(line_entry.IsValid(), 'Got a valid line entry.')
self.assertTrue((line_entry.line == a_func_line), 'Our line number matches the one lldbtest found.') | 9,138,448,826,118,702,000 | Test that restricting source expressions to files & to functions. | SymbolExtractorAndRenamer/lldb/packages/Python/lldbsuite/test/functionalities/breakpoint/source_regexp/TestSourceRegexBreakpoints.py | source_regex_locations | Polidea/SiriusObfuscator | python | def source_regex_locations(self):
' '
exe = os.path.join(os.getcwd(), 'a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
target_files = lldb.SBFileSpecList()
target_files.Append(lldb.SBFileSpec('a.c'))
func_names = lldb.SBStringList()
func_names.AppendString('a_func')
source_regex = 'Set . breakpoint here'
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, func_names)
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 1), ('a.c in a_func should give one breakpoint, got %d.' % num_locations))
loc = main_break.GetLocationAtIndex(0)
self.assertTrue(loc.IsValid(), 'Got a valid location.')
address = loc.GetAddress()
self.assertTrue(address.IsValid(), 'Got a valid address from the location.')
a_func_line = line_number('a.c', 'Set A breakpoint here')
line_entry = address.GetLineEntry()
self.assertTrue(line_entry.IsValid(), 'Got a valid line entry.')
self.assertTrue((line_entry.line == a_func_line), 'Our line number matches the one lldbtest found.') |
def source_regex_restrictions(self):
' Test that restricting source expressions to files & to functions. '
exe = os.path.join(os.getcwd(), 'a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
target_files = lldb.SBFileSpecList()
target_files.Append(lldb.SBFileSpec('main.c'))
source_regex = 'Set . breakpoint here'
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, lldb.SBStringList())
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 2), ('main.c should have 2 matches, got %d.' % num_locations))
target_files.Append(lldb.SBFileSpec('a.c'))
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, lldb.SBStringList())
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 4), ('main.c and a.c should have 4 matches, got %d.' % num_locations))
func_names = lldb.SBStringList()
func_names.AppendString('main_func')
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, func_names)
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 2), ('main_func in main.c and a.c should have 2 matches, got %d.' % num_locations)) | 8,368,960,995,021,304,000 | Test that restricting source expressions to files & to functions. | SymbolExtractorAndRenamer/lldb/packages/Python/lldbsuite/test/functionalities/breakpoint/source_regexp/TestSourceRegexBreakpoints.py | source_regex_restrictions | Polidea/SiriusObfuscator | python | def source_regex_restrictions(self):
' '
exe = os.path.join(os.getcwd(), 'a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
target_files = lldb.SBFileSpecList()
target_files.Append(lldb.SBFileSpec('main.c'))
source_regex = 'Set . breakpoint here'
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, lldb.SBStringList())
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 2), ('main.c should have 2 matches, got %d.' % num_locations))
target_files.Append(lldb.SBFileSpec('a.c'))
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, lldb.SBStringList())
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 4), ('main.c and a.c should have 4 matches, got %d.' % num_locations))
func_names = lldb.SBStringList()
func_names.AppendString('main_func')
main_break = target.BreakpointCreateBySourceRegex(source_regex, lldb.SBFileSpecList(), target_files, func_names)
num_locations = main_break.GetNumLocations()
self.assertTrue((num_locations == 2), ('main_func in main.c and a.c should have 2 matches, got %d.' % num_locations)) |
def new_connection(self, workerId: str, socket):
' Create a mapping structure to establish a bond between a workerId and a socket descriptor.\n\n Args:\n workerId: UUID string used to identify workers.\n socket: Socket descriptor that will be used to send/receive messages from this client.\n\n Returns:\n Worker: a worker instance with the corresponding workerId\n '
if (workerId not in self.connections):
self.connections[workerId] = Worker(workerId, socket)
else:
worker = self.connections[workerId]
if (worker.status == WORKER_PROPERTIES.OFFLINE):
worker._socket = socket
return self.connections[workerId] | 7,135,368,950,734,366,000 | Create a mapping structure to establish a bond between a workerId and a socket descriptor.
Args:
workerId: UUID string used to identify workers.
socket: Socket descriptor that will be used to send/receive messages from this client.
Returns:
Worker: a worker instance with the corresponding workerId | gridnetwork/events/socket_handler.py | new_connection | kuronosec/PyGridNetwork | python | def new_connection(self, workerId: str, socket):
' Create a mapping structure to establish a bond between a workerId and a socket descriptor.\n\n Args:\n workerId: UUID string used to identify workers.\n socket: Socket descriptor that will be used to send/receive messages from this client.\n\n Returns:\n Worker: a worker instance with the corresponding workerId\n '
if (workerId not in self.connections):
self.connections[workerId] = Worker(workerId, socket)
else:
worker = self.connections[workerId]
if (worker.status == WORKER_PROPERTIES.OFFLINE):
worker._socket = socket
return self.connections[workerId] |
def send_msg(self, workerId: str, message: str):
' Find the socket descriptor mapped by workerId and send them a message.\n\n Args:\n workerId: UUID string used to identify and retrieve a worker.\n message: Message to be send.\n '
socket = self.connections.get(workerId, None)
if socket:
socket.send(message) | -830,059,463,909,367,300 | Find the socket descriptor mapped by workerId and send them a message.
Args:
workerId: UUID string used to identify and retrieve a worker.
message: Message to be send. | gridnetwork/events/socket_handler.py | send_msg | kuronosec/PyGridNetwork | python | def send_msg(self, workerId: str, message: str):
' Find the socket descriptor mapped by workerId and send them a message.\n\n Args:\n workerId: UUID string used to identify and retrieve a worker.\n message: Message to be send.\n '
socket = self.connections.get(workerId, None)
if socket:
socket.send(message) |
def get(self, query):
'Retrieve a worker by its UUID string or its socket descriptor.'
if isinstance(query, str):
return self.connections.get(query, None)
else:
return self.__retrieve_worker_by_socket(query) | 5,881,453,422,708,374,000 | Retrieve a worker by its UUID string or its socket descriptor. | gridnetwork/events/socket_handler.py | get | kuronosec/PyGridNetwork | python | def get(self, query):
if isinstance(query, str):
return self.connections.get(query, None)
else:
return self.__retrieve_worker_by_socket(query) |
def remove(self, socket) -> str:
' Remove a socket descriptor from mapping structure. It will be used when the socket connection is closed.\n\n Args:\n socket: socket descriptor used to send/receive messages.\n\n Returns:\n workerId: Worker id linked to that connection.\n '
worker = self.__retrieve_worker_by_socket(socket)
if worker:
self.connections[worker._id]._socket = None
self.connections[worker._id].connected_nodes = []
return worker._id | -1,422,404,848,341,396,700 | Remove a socket descriptor from mapping structure. It will be used when the socket connection is closed.
Args:
socket: socket descriptor used to send/receive messages.
Returns:
workerId: Worker id linked to that connection. | gridnetwork/events/socket_handler.py | remove | kuronosec/PyGridNetwork | python | def remove(self, socket) -> str:
' Remove a socket descriptor from mapping structure. It will be used when the socket connection is closed.\n\n Args:\n socket: socket descriptor used to send/receive messages.\n\n Returns:\n workerId: Worker id linked to that connection.\n '
worker = self.__retrieve_worker_by_socket(socket)
if worker:
self.connections[worker._id]._socket = None
self.connections[worker._id].connected_nodes = []
return worker._id |
@property
def nodes(self) -> list:
'Return all the connected nodes as a list of tuples of (worker_id, worker)'
return list(self.connections.items()) | 6,549,633,896,146,046,000 | Return all the connected nodes as a list of tuples of (worker_id, worker) | gridnetwork/events/socket_handler.py | nodes | kuronosec/PyGridNetwork | python | @property
def nodes(self) -> list:
return list(self.connections.items()) |
def __len__(self) -> int:
' Number of connections handled by this server.\n\n Returns:\n length: number of connections handled by this server.\n '
return len(self.connections) | -5,961,706,619,804,543,000 | Number of connections handled by this server.
Returns:
length: number of connections handled by this server. | gridnetwork/events/socket_handler.py | __len__ | kuronosec/PyGridNetwork | python | def __len__(self) -> int:
' Number of connections handled by this server.\n\n Returns:\n length: number of connections handled by this server.\n '
return len(self.connections) |
def _get_vco(verts_orig, loop):
'\n Get vertex original coordinate from loop\n '
for vo in verts_orig:
if ((vo['vidx'] == loop.vert.index) and (vo['moved'] is False)):
return vo['vco']
return loop.vert.co | -7,784,488,194,725,501,000 | Get vertex original coordinate from loop | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | _get_vco | byteinc/Phasor | python | def _get_vco(verts_orig, loop):
'\n \n '
for vo in verts_orig:
if ((vo['vidx'] == loop.vert.index) and (vo['moved'] is False)):
return vo['vco']
return loop.vert.co |
def _get_link_loops(vert):
'\n Get loop linked to vertex\n '
link_loops = []
for f in vert.link_faces:
adj_loops = []
for loop in f.loops:
if (loop.vert == vert):
l = loop
else:
for e in loop.vert.link_edges:
if (e.other_vert(loop.vert) == vert):
adj_loops.append(loop)
if (len(adj_loops) < 2):
return None
link_loops.append({'l': l, 'l0': adj_loops[0], 'l1': adj_loops[1]})
return link_loops | -2,814,715,461,684,407,000 | Get loop linked to vertex | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | _get_link_loops | byteinc/Phasor | python | def _get_link_loops(vert):
'\n \n '
link_loops = []
for f in vert.link_faces:
adj_loops = []
for loop in f.loops:
if (loop.vert == vert):
l = loop
else:
for e in loop.vert.link_edges:
if (e.other_vert(loop.vert) == vert):
adj_loops.append(loop)
if (len(adj_loops) < 2):
return None
link_loops.append({'l': l, 'l0': adj_loops[0], 'l1': adj_loops[1]})
return link_loops |
def _get_ini_geom(link_loop, uv_layer, verts_orig, v_orig):
'\n Get initial geometory\n (Get interior angle of face in vertex/UV space)\n '
u = link_loop['l'][uv_layer].uv
v0 = _get_vco(verts_orig, link_loop['l0'])
u0 = link_loop['l0'][uv_layer].uv
v1 = _get_vco(verts_orig, link_loop['l1'])
u1 = link_loop['l1'][uv_layer].uv
v0v1 = (v1 - v0)
v0v = (v_orig['vco'] - v0)
v1v = (v_orig['vco'] - v1)
theta0 = v0v1.angle(v0v)
theta1 = v0v1.angle((- v1v))
if ((theta0 + theta1) > math.pi):
theta0 = v0v1.angle((- v0v))
theta1 = v0v1.angle(v1v)
u0u1 = (u1 - u0)
u0u = (u - u0)
u1u = (u - u1)
phi0 = u0u1.angle(u0u)
phi1 = u0u1.angle((- u1u))
if ((phi0 + phi1) > math.pi):
phi0 = u0u1.angle((- u0u))
phi1 = u0u1.angle(u1u)
dir0 = (u0u1.cross(u0u) > 0)
dir1 = (u0u1.cross(u1u) > 0)
return {'theta0': theta0, 'theta1': theta1, 'phi0': phi0, 'phi1': phi1, 'dir0': dir0, 'dir1': dir1} | 3,110,401,165,743,732,000 | Get initial geometory
(Get interior angle of face in vertex/UV space) | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | _get_ini_geom | byteinc/Phasor | python | def _get_ini_geom(link_loop, uv_layer, verts_orig, v_orig):
'\n Get initial geometory\n (Get interior angle of face in vertex/UV space)\n '
u = link_loop['l'][uv_layer].uv
v0 = _get_vco(verts_orig, link_loop['l0'])
u0 = link_loop['l0'][uv_layer].uv
v1 = _get_vco(verts_orig, link_loop['l1'])
u1 = link_loop['l1'][uv_layer].uv
v0v1 = (v1 - v0)
v0v = (v_orig['vco'] - v0)
v1v = (v_orig['vco'] - v1)
theta0 = v0v1.angle(v0v)
theta1 = v0v1.angle((- v1v))
if ((theta0 + theta1) > math.pi):
theta0 = v0v1.angle((- v0v))
theta1 = v0v1.angle(v1v)
u0u1 = (u1 - u0)
u0u = (u - u0)
u1u = (u - u1)
phi0 = u0u1.angle(u0u)
phi1 = u0u1.angle((- u1u))
if ((phi0 + phi1) > math.pi):
phi0 = u0u1.angle((- u0u))
phi1 = u0u1.angle(u1u)
dir0 = (u0u1.cross(u0u) > 0)
dir1 = (u0u1.cross(u1u) > 0)
return {'theta0': theta0, 'theta1': theta1, 'phi0': phi0, 'phi1': phi1, 'dir0': dir0, 'dir1': dir1} |
def _get_target_uv(link_loop, uv_layer, verts_orig, v, ini_geom):
'\n Get target UV coordinate\n '
v0 = _get_vco(verts_orig, link_loop['l0'])
lo0 = link_loop['l0']
v1 = _get_vco(verts_orig, link_loop['l1'])
lo1 = link_loop['l1']
v0v1 = (v1 - v0)
v0v = (v.co - v0)
v1v = (v.co - v1)
theta0 = v0v1.angle(v0v)
theta1 = v0v1.angle((- v1v))
if ((theta0 + theta1) > math.pi):
theta0 = v0v1.angle((- v0v))
theta1 = v0v1.angle(v1v)
phi0 = ((theta0 * ini_geom['phi0']) / ini_geom['theta0'])
phi1 = ((theta1 * ini_geom['phi1']) / ini_geom['theta1'])
uv0 = lo0[uv_layer].uv
uv1 = lo1[uv_layer].uv
(tuv0, tuv1) = _calc_tri_vert(uv0, uv1, phi0, phi1)
u0u1 = (uv1 - uv0)
u0u = (tuv0 - uv0)
u1u = (tuv0 - uv1)
dir0 = (u0u1.cross(u0u) > 0)
dir1 = (u0u1.cross(u1u) > 0)
if ((ini_geom['dir0'] != dir0) or (ini_geom['dir1'] != dir1)):
return tuv1
return tuv0 | 3,503,573,570,658,573,000 | Get target UV coordinate | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | _get_target_uv | byteinc/Phasor | python | def _get_target_uv(link_loop, uv_layer, verts_orig, v, ini_geom):
'\n \n '
v0 = _get_vco(verts_orig, link_loop['l0'])
lo0 = link_loop['l0']
v1 = _get_vco(verts_orig, link_loop['l1'])
lo1 = link_loop['l1']
v0v1 = (v1 - v0)
v0v = (v.co - v0)
v1v = (v.co - v1)
theta0 = v0v1.angle(v0v)
theta1 = v0v1.angle((- v1v))
if ((theta0 + theta1) > math.pi):
theta0 = v0v1.angle((- v0v))
theta1 = v0v1.angle(v1v)
phi0 = ((theta0 * ini_geom['phi0']) / ini_geom['theta0'])
phi1 = ((theta1 * ini_geom['phi1']) / ini_geom['theta1'])
uv0 = lo0[uv_layer].uv
uv1 = lo1[uv_layer].uv
(tuv0, tuv1) = _calc_tri_vert(uv0, uv1, phi0, phi1)
u0u1 = (uv1 - uv0)
u0u = (tuv0 - uv0)
u1u = (tuv0 - uv1)
dir0 = (u0u1.cross(u0u) > 0)
dir1 = (u0u1.cross(u1u) > 0)
if ((ini_geom['dir0'] != dir0) or (ini_geom['dir1'] != dir1)):
return tuv1
return tuv0 |
def _calc_tri_vert(v0, v1, angle0, angle1):
'\n Calculate rest coordinate from other coordinates and angle of end\n '
angle = ((math.pi - angle0) - angle1)
alpha = atan2((v1.y - v0.y), (v1.x - v0.x))
d = ((v1.x - v0.x) / cos(alpha))
a = ((d * sin(angle0)) / sin(angle))
b = ((d * sin(angle1)) / sin(angle))
s = (((a + b) + d) / 2.0)
if (fabs(d) < 1e-07):
xd = 0
yd = 0
else:
r = (((s * (s - a)) * (s - b)) * (s - d))
if (r < 0):
xd = 0
yd = 0
else:
xd = ((((b * b) - (a * a)) + (d * d)) / (2 * d))
yd = ((2 * sqrt(r)) / d)
x1 = (((xd * cos(alpha)) - (yd * sin(alpha))) + v0.x)
y1 = (((xd * sin(alpha)) + (yd * cos(alpha))) + v0.y)
x2 = (((xd * cos(alpha)) + (yd * sin(alpha))) + v0.x)
y2 = (((xd * sin(alpha)) - (yd * cos(alpha))) + v0.y)
return (Vector((x1, y1)), Vector((x2, y2))) | 240,756,091,218,326,300 | Calculate rest coordinate from other coordinates and angle of end | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | _calc_tri_vert | byteinc/Phasor | python | def _calc_tri_vert(v0, v1, angle0, angle1):
'\n \n '
angle = ((math.pi - angle0) - angle1)
alpha = atan2((v1.y - v0.y), (v1.x - v0.x))
d = ((v1.x - v0.x) / cos(alpha))
a = ((d * sin(angle0)) / sin(angle))
b = ((d * sin(angle1)) / sin(angle))
s = (((a + b) + d) / 2.0)
if (fabs(d) < 1e-07):
xd = 0
yd = 0
else:
r = (((s * (s - a)) * (s - b)) * (s - d))
if (r < 0):
xd = 0
yd = 0
else:
xd = ((((b * b) - (a * a)) + (d * d)) / (2 * d))
yd = ((2 * sqrt(r)) / d)
x1 = (((xd * cos(alpha)) - (yd * sin(alpha))) + v0.x)
y1 = (((xd * sin(alpha)) + (yd * cos(alpha))) + v0.y)
x2 = (((xd * cos(alpha)) + (yd * sin(alpha))) + v0.x)
y2 = (((xd * sin(alpha)) - (yd * cos(alpha))) + v0.y)
return (Vector((x1, y1)), Vector((x2, y2))) |
def __update_uv(self, context):
'\n Update UV when vertex coordinates are changed\n '
obj = context.active_object
bm = bmesh.from_edit_mesh(obj.data)
if (common.check_version(2, 73, 0) >= 0):
bm.verts.ensure_lookup_table()
bm.edges.ensure_lookup_table()
bm.faces.ensure_lookup_table()
if (not bm.loops.layers.uv):
self.report({'WARNING'}, 'Object must have more than one UV map')
return
uv_layer = bm.loops.layers.uv.verify()
verts = [v.index for v in bm.verts if v.select]
verts_orig = self.__intr_verts_orig
for (vidx, v_orig) in zip(verts, verts_orig):
if (vidx != v_orig['vidx']):
self.report({'ERROR'}, 'Internal Error')
return
v = bm.verts[vidx]
link_loops = _get_link_loops(v)
result = []
for ll in link_loops:
ini_geom = _get_ini_geom(ll, uv_layer, verts_orig, v_orig)
target_uv = _get_target_uv(ll, uv_layer, verts_orig, v, ini_geom)
result.append({'l': ll['l'], 'uv': target_uv})
ave = Vector((0.0, 0.0))
for r in result:
ave = (ave + r['uv'])
ave = (ave / len(result))
for r in result:
r['l'][uv_layer].uv = ave
v_orig['moved'] = True
bmesh.update_edit_mesh(obj.data)
common.redraw_all_areas()
self.__intr_verts_orig = [{'vidx': v.index, 'vco': v.co.copy(), 'moved': False} for v in bm.verts if v.select] | 9,013,071,885,757,533,000 | Update UV when vertex coordinates are changed | engine/2.80/scripts/addons/magic_uv/op/texture_lock.py | __update_uv | byteinc/Phasor | python | def __update_uv(self, context):
'\n \n '
obj = context.active_object
bm = bmesh.from_edit_mesh(obj.data)
if (common.check_version(2, 73, 0) >= 0):
bm.verts.ensure_lookup_table()
bm.edges.ensure_lookup_table()
bm.faces.ensure_lookup_table()
if (not bm.loops.layers.uv):
self.report({'WARNING'}, 'Object must have more than one UV map')
return
uv_layer = bm.loops.layers.uv.verify()
verts = [v.index for v in bm.verts if v.select]
verts_orig = self.__intr_verts_orig
for (vidx, v_orig) in zip(verts, verts_orig):
if (vidx != v_orig['vidx']):
self.report({'ERROR'}, 'Internal Error')
return
v = bm.verts[vidx]
link_loops = _get_link_loops(v)
result = []
for ll in link_loops:
ini_geom = _get_ini_geom(ll, uv_layer, verts_orig, v_orig)
target_uv = _get_target_uv(ll, uv_layer, verts_orig, v, ini_geom)
result.append({'l': ll['l'], 'uv': target_uv})
ave = Vector((0.0, 0.0))
for r in result:
ave = (ave + r['uv'])
ave = (ave / len(result))
for r in result:
r['l'][uv_layer].uv = ave
v_orig['moved'] = True
bmesh.update_edit_mesh(obj.data)
common.redraw_all_areas()
self.__intr_verts_orig = [{'vidx': v.index, 'vco': v.co.copy(), 'moved': False} for v in bm.verts if v.select] |
def create_figure_and_sliders(name, state_dim):
'\n Creating a window for the latent space visualization,\n and another one for the sliders to control it.\n\n :param name: name of model (str)\n :param state_dim: (int)\n :return:\n '
cv2.namedWindow(name, cv2.WINDOW_NORMAL)
cv2.resizeWindow(name, 500, 500)
cv2.namedWindow(('slider for ' + name))
for i in range(state_dim):
cv2.createTrackbar(str(i), ('slider for ' + name), 50, 100, (lambda a: None)) | -3,995,013,066,744,532,500 | Creating a window for the latent space visualization,
and another one for the sliders to control it.
:param name: name of model (str)
:param state_dim: (int)
:return: | ae/enjoy_latent.py | create_figure_and_sliders | araffin/aae-train-donkeycar | python | def create_figure_and_sliders(name, state_dim):
'\n Creating a window for the latent space visualization,\n and another one for the sliders to control it.\n\n :param name: name of model (str)\n :param state_dim: (int)\n :return:\n '
cv2.namedWindow(name, cv2.WINDOW_NORMAL)
cv2.resizeWindow(name, 500, 500)
cv2.namedWindow(('slider for ' + name))
for i in range(state_dim):
cv2.createTrackbar(str(i), ('slider for ' + name), 50, 100, (lambda a: None)) |
def nms(dets, thresh):
'Apply classic DPM-style greedy NMS.'
if (dets.shape[0] == 0):
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
order = scores.argsort()[::(- 1)]
ndets = dets.shape[0]
suppressed = np.zeros(ndets, dtype=np.int)
for _i in range(ndets):
i = order[_i]
if (suppressed[i] == 1):
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range((_i + 1), ndets):
j = order[_j]
if (suppressed[j] == 1):
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, ((xx2 - xx1) + 1))
h = max(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((iarea + areas[j]) - inter))
if (ovr >= thresh):
suppressed[j] = 1
keep = np.where((suppressed == 0))[0]
dets = dets[keep, :]
return dets | 80,342,114,398,799,000 | Apply classic DPM-style greedy NMS. | ppdet/modeling/post_process.py | nms | gbstack/PaddleDetection | python | def nms(dets, thresh):
if (dets.shape[0] == 0):
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
order = scores.argsort()[::(- 1)]
ndets = dets.shape[0]
suppressed = np.zeros(ndets, dtype=np.int)
for _i in range(ndets):
i = order[_i]
if (suppressed[i] == 1):
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range((_i + 1), ndets):
j = order[_j]
if (suppressed[j] == 1):
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, ((xx2 - xx1) + 1))
h = max(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((iarea + areas[j]) - inter))
if (ovr >= thresh):
suppressed[j] = 1
keep = np.where((suppressed == 0))[0]
dets = dets[keep, :]
return dets |
def forward(self, head_out, rois, im_shape, scale_factor):
'\n Decode the bbox and do NMS if needed. \n\n Args:\n head_out (tuple): bbox_pred and cls_prob of bbox_head output.\n rois (tuple): roi and rois_num of rpn_head output.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n bbox_pred (Tensor): The output prediction with shape [N, 6], including\n labels, scores and bboxes. The size of bboxes are corresponding\n to the input image, the bboxes may be used in other branch.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n '
if (self.nms is not None):
(bboxes, score) = self.decode(head_out, rois, im_shape, scale_factor)
(bbox_pred, bbox_num, _) = self.nms(bboxes, score, self.num_classes)
else:
(bbox_pred, bbox_num) = self.decode(head_out, rois, im_shape, scale_factor)
return (bbox_pred, bbox_num) | -2,970,551,212,598,684,000 | Decode the bbox and do NMS if needed.
Args:
head_out (tuple): bbox_pred and cls_prob of bbox_head output.
rois (tuple): roi and rois_num of rpn_head output.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
bbox_pred (Tensor): The output prediction with shape [N, 6], including
labels, scores and bboxes. The size of bboxes are corresponding
to the input image, the bboxes may be used in other branch.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N. | ppdet/modeling/post_process.py | forward | gbstack/PaddleDetection | python | def forward(self, head_out, rois, im_shape, scale_factor):
'\n Decode the bbox and do NMS if needed. \n\n Args:\n head_out (tuple): bbox_pred and cls_prob of bbox_head output.\n rois (tuple): roi and rois_num of rpn_head output.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n bbox_pred (Tensor): The output prediction with shape [N, 6], including\n labels, scores and bboxes. The size of bboxes are corresponding\n to the input image, the bboxes may be used in other branch.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n '
if (self.nms is not None):
(bboxes, score) = self.decode(head_out, rois, im_shape, scale_factor)
(bbox_pred, bbox_num, _) = self.nms(bboxes, score, self.num_classes)
else:
(bbox_pred, bbox_num) = self.decode(head_out, rois, im_shape, scale_factor)
return (bbox_pred, bbox_num) |
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
'\n Rescale, clip and filter the bbox from the output of NMS to \n get final prediction. \n \n Notes:\n Currently only support bs = 1.\n\n Args:\n bboxes (Tensor): The output bboxes with shape [N, 6] after decode\n and NMS, including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n pred_result (Tensor): The final prediction results with shape [N, 6]\n including labels, scores and bboxes.\n '
if (bboxes.shape[0] == 0):
bboxes = self.fake_bboxes
bbox_num = self.fake_bbox_num
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
origin_shape_list = []
scale_factor_list = []
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:(i + 1), :], [bbox_num[i], 2])
(scale_y, scale_x) = (scale_factor[i][0], scale_factor[i][1])
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 4])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
self.origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
pred_label = bboxes[:, 0:1]
pred_score = bboxes[:, 1:2]
pred_bbox = bboxes[:, 2:]
scaled_bbox = (pred_bbox / scale_factor_list)
origin_h = self.origin_shape_list[:, 0]
origin_w = self.origin_shape_list[:, 1]
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2], axis=(- 1))
keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
keep_mask = paddle.unsqueeze(keep_mask, [1])
pred_label = paddle.where(keep_mask, pred_label, (paddle.ones_like(pred_label) * (- 1)))
pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
return pred_result | 3,043,091,144,749,047,000 | Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Notes:
Currently only support bs = 1.
Args:
bboxes (Tensor): The output bboxes with shape [N, 6] after decode
and NMS, including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
pred_result (Tensor): The final prediction results with shape [N, 6]
including labels, scores and bboxes. | ppdet/modeling/post_process.py | get_pred | gbstack/PaddleDetection | python | def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
'\n Rescale, clip and filter the bbox from the output of NMS to \n get final prediction. \n \n Notes:\n Currently only support bs = 1.\n\n Args:\n bboxes (Tensor): The output bboxes with shape [N, 6] after decode\n and NMS, including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n pred_result (Tensor): The final prediction results with shape [N, 6]\n including labels, scores and bboxes.\n '
if (bboxes.shape[0] == 0):
bboxes = self.fake_bboxes
bbox_num = self.fake_bbox_num
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
origin_shape_list = []
scale_factor_list = []
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:(i + 1), :], [bbox_num[i], 2])
(scale_y, scale_x) = (scale_factor[i][0], scale_factor[i][1])
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 4])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
self.origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
pred_label = bboxes[:, 0:1]
pred_score = bboxes[:, 1:2]
pred_bbox = bboxes[:, 2:]
scaled_bbox = (pred_bbox / scale_factor_list)
origin_h = self.origin_shape_list[:, 0]
origin_w = self.origin_shape_list[:, 1]
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2], axis=(- 1))
keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
keep_mask = paddle.unsqueeze(keep_mask, [1])
pred_label = paddle.where(keep_mask, pred_label, (paddle.ones_like(pred_label) * (- 1)))
pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
return pred_result |
def paste_mask(self, masks, boxes, im_h, im_w):
'\n Paste the mask prediction to the original image.\n '
(x0, y0, x1, y1) = paddle.split(boxes, 4, axis=1)
masks = paddle.unsqueeze(masks, [0, 1])
img_y = (paddle.arange(0, im_h, dtype='float32') + 0.5)
img_x = (paddle.arange(0, im_w, dtype='float32') + 0.5)
img_y = ((((img_y - y0) / (y1 - y0)) * 2) - 1)
img_x = ((((img_x - x0) / (x1 - x0)) * 2) - 1)
img_x = paddle.unsqueeze(img_x, [1])
img_y = paddle.unsqueeze(img_y, [2])
N = boxes.shape[0]
gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]])
gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]])
grid = paddle.stack([gx, gy], axis=3)
img_masks = F.grid_sample(masks, grid, align_corners=False)
return img_masks[:, 0] | 8,498,504,445,264,646,000 | Paste the mask prediction to the original image. | ppdet/modeling/post_process.py | paste_mask | gbstack/PaddleDetection | python | def paste_mask(self, masks, boxes, im_h, im_w):
'\n \n '
(x0, y0, x1, y1) = paddle.split(boxes, 4, axis=1)
masks = paddle.unsqueeze(masks, [0, 1])
img_y = (paddle.arange(0, im_h, dtype='float32') + 0.5)
img_x = (paddle.arange(0, im_w, dtype='float32') + 0.5)
img_y = ((((img_y - y0) / (y1 - y0)) * 2) - 1)
img_x = ((((img_x - x0) / (x1 - x0)) * 2) - 1)
img_x = paddle.unsqueeze(img_x, [1])
img_y = paddle.unsqueeze(img_y, [2])
N = boxes.shape[0]
gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]])
gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]])
grid = paddle.stack([gx, gy], axis=3)
img_masks = F.grid_sample(masks, grid, align_corners=False)
return img_masks[:, 0] |
def __call__(self, mask_out, bboxes, bbox_num, origin_shape):
'\n Decode the mask_out and paste the mask to the origin image.\n\n Args:\n mask_out (Tensor): mask_head output with shape [N, 28, 28].\n bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode\n and NMS, including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n origin_shape (Tensor): The origin shape of the input image, the tensor\n shape is [N, 2], and each row is [h, w].\n Returns:\n pred_result (Tensor): The final prediction mask results with shape\n [N, h, w] in binary mask style.\n '
num_mask = mask_out.shape[0]
origin_shape = paddle.cast(origin_shape, 'int32')
pred_result = paddle.zeros([num_mask, origin_shape[0][0], origin_shape[0][1]], dtype='int32')
if ((bbox_num == 1) and (bboxes[0][0] == (- 1))):
return pred_result
pred_result = []
for i in range(bboxes.shape[0]):
(im_h, im_w) = (origin_shape[i][0], origin_shape[i][1])
pred_mask = self.paste_mask(mask_out[i], bboxes[i:(i + 1), 2:], im_h, im_w)
pred_mask = (pred_mask >= self.binary_thresh)
pred_mask = paddle.cast(pred_mask, 'int32')
pred_result.append(pred_mask)
pred_result = paddle.concat(pred_result)
return pred_result | -6,449,566,251,477,395,000 | Decode the mask_out and paste the mask to the origin image.
Args:
mask_out (Tensor): mask_head output with shape [N, 28, 28].
bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
and NMS, including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
origin_shape (Tensor): The origin shape of the input image, the tensor
shape is [N, 2], and each row is [h, w].
Returns:
pred_result (Tensor): The final prediction mask results with shape
[N, h, w] in binary mask style. | ppdet/modeling/post_process.py | __call__ | gbstack/PaddleDetection | python | def __call__(self, mask_out, bboxes, bbox_num, origin_shape):
'\n Decode the mask_out and paste the mask to the origin image.\n\n Args:\n mask_out (Tensor): mask_head output with shape [N, 28, 28].\n bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode\n and NMS, including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [1], and is N.\n origin_shape (Tensor): The origin shape of the input image, the tensor\n shape is [N, 2], and each row is [h, w].\n Returns:\n pred_result (Tensor): The final prediction mask results with shape\n [N, h, w] in binary mask style.\n '
num_mask = mask_out.shape[0]
origin_shape = paddle.cast(origin_shape, 'int32')
pred_result = paddle.zeros([num_mask, origin_shape[0][0], origin_shape[0][1]], dtype='int32')
if ((bbox_num == 1) and (bboxes[0][0] == (- 1))):
return pred_result
pred_result = []
for i in range(bboxes.shape[0]):
(im_h, im_w) = (origin_shape[i][0], origin_shape[i][1])
pred_mask = self.paste_mask(mask_out[i], bboxes[i:(i + 1), 2:], im_h, im_w)
pred_mask = (pred_mask >= self.binary_thresh)
pred_mask = paddle.cast(pred_mask, 'int32')
pred_result.append(pred_mask)
pred_result = paddle.concat(pred_result)
return pred_result |
def __call__(self, fcos_head_outs, scale_factor):
'\n Decode the bbox and do NMS in FCOS.\n '
(locations, cls_logits, bboxes_reg, centerness) = fcos_head_outs
(bboxes, score) = self.decode(locations, cls_logits, bboxes_reg, centerness, scale_factor)
(bbox_pred, bbox_num, _) = self.nms(bboxes, score)
return (bbox_pred, bbox_num) | -9,044,059,880,495,126,000 | Decode the bbox and do NMS in FCOS. | ppdet/modeling/post_process.py | __call__ | gbstack/PaddleDetection | python | def __call__(self, fcos_head_outs, scale_factor):
'\n \n '
(locations, cls_logits, bboxes_reg, centerness) = fcos_head_outs
(bboxes, score) = self.decode(locations, cls_logits, bboxes_reg, centerness, scale_factor)
(bbox_pred, bbox_num, _) = self.nms(bboxes, score)
return (bbox_pred, bbox_num) |
def forward(self, pred_scores, pred_bboxes):
'\n pred_scores : [N, M] score\n pred_bboxes : [N, 5] xc, yc, w, h, a\n im_shape : [N, 2] im_shape\n scale_factor : [N, 2] scale_factor\n '
pred_ploys0 = rbox2poly(pred_bboxes)
pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
pred_scores0 = paddle.transpose(pred_scores, [1, 0])
pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
(pred_cls_score_bbox, bbox_num, _) = self.nms(pred_ploys, pred_scores, self.num_classes)
if ((pred_cls_score_bbox.shape[0] <= 0) or (pred_cls_score_bbox.shape[1] <= 1)):
pred_cls_score_bbox = self.fake_pred_cls_score_bbox
bbox_num = self.fake_bbox_num
pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [(- 1), 10])
return (pred_cls_score_bbox, bbox_num) | -2,744,508,617,960,272,400 | pred_scores : [N, M] score
pred_bboxes : [N, 5] xc, yc, w, h, a
im_shape : [N, 2] im_shape
scale_factor : [N, 2] scale_factor | ppdet/modeling/post_process.py | forward | gbstack/PaddleDetection | python | def forward(self, pred_scores, pred_bboxes):
'\n pred_scores : [N, M] score\n pred_bboxes : [N, 5] xc, yc, w, h, a\n im_shape : [N, 2] im_shape\n scale_factor : [N, 2] scale_factor\n '
pred_ploys0 = rbox2poly(pred_bboxes)
pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
pred_scores0 = paddle.transpose(pred_scores, [1, 0])
pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
(pred_cls_score_bbox, bbox_num, _) = self.nms(pred_ploys, pred_scores, self.num_classes)
if ((pred_cls_score_bbox.shape[0] <= 0) or (pred_cls_score_bbox.shape[1] <= 1)):
pred_cls_score_bbox = self.fake_pred_cls_score_bbox
bbox_num = self.fake_bbox_num
pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [(- 1), 10])
return (pred_cls_score_bbox, bbox_num) |
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
'\n Rescale, clip and filter the bbox from the output of NMS to\n get final prediction.\n Args:\n bboxes(Tensor): bboxes [N, 10]\n bbox_num(Tensor): bbox_num\n im_shape(Tensor): [1 2]\n scale_factor(Tensor): [1 2]\n Returns:\n bbox_pred(Tensor): The output is the prediction with shape [N, 8]\n including labels, scores and bboxes. The size of\n bboxes are corresponding to the original image.\n '
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
origin_shape_list = []
scale_factor_list = []
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:(i + 1), :], [bbox_num[i], 2])
(scale_y, scale_x) = (scale_factor[i][0], scale_factor[i][1])
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 8])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
pred_label_score = bboxes[:, 0:2]
pred_bbox = bboxes[:, 2:]
pred_bbox = pred_bbox.reshape([(- 1), 8])
scaled_bbox = (pred_bbox / scale_factor_list)
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
bboxes = scaled_bbox
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], (origin_w - 1)), zeros)
y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], (origin_h - 1)), zeros)
x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], (origin_w - 1)), zeros)
y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], (origin_h - 1)), zeros)
x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], (origin_w - 1)), zeros)
y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], (origin_h - 1)), zeros)
x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], (origin_w - 1)), zeros)
y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], (origin_h - 1)), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=(- 1))
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
return pred_result | -2,542,100,866,437,608,400 | Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Args:
bboxes(Tensor): bboxes [N, 10]
bbox_num(Tensor): bbox_num
im_shape(Tensor): [1 2]
scale_factor(Tensor): [1 2]
Returns:
bbox_pred(Tensor): The output is the prediction with shape [N, 8]
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image. | ppdet/modeling/post_process.py | get_pred | gbstack/PaddleDetection | python | def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
'\n Rescale, clip and filter the bbox from the output of NMS to\n get final prediction.\n Args:\n bboxes(Tensor): bboxes [N, 10]\n bbox_num(Tensor): bbox_num\n im_shape(Tensor): [1 2]\n scale_factor(Tensor): [1 2]\n Returns:\n bbox_pred(Tensor): The output is the prediction with shape [N, 8]\n including labels, scores and bboxes. The size of\n bboxes are corresponding to the original image.\n '
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
origin_shape_list = []
scale_factor_list = []
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:(i + 1), :], [bbox_num[i], 2])
(scale_y, scale_x) = (scale_factor[i][0], scale_factor[i][1])
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 8])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
pred_label_score = bboxes[:, 0:2]
pred_bbox = bboxes[:, 2:]
pred_bbox = pred_bbox.reshape([(- 1), 8])
scaled_bbox = (pred_bbox / scale_factor_list)
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
bboxes = scaled_bbox
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], (origin_w - 1)), zeros)
y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], (origin_h - 1)), zeros)
x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], (origin_w - 1)), zeros)
y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], (origin_h - 1)), zeros)
x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], (origin_w - 1)), zeros)
y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], (origin_h - 1)), zeros)
x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], (origin_w - 1)), zeros)
y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], (origin_h - 1)), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=(- 1))
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
return pred_result |
def forward(self, head_out, anchors):
"\n Decode the bbox and do NMS for JDE model. \n\n Args:\n head_out (list): Bbox_pred and cls_prob of bbox_head output.\n anchors (list): Anchors of JDE model.\n\n Returns:\n boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'. \n bbox_pred (Tensor): The output is the prediction with shape [N, 6]\n including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction of each batch with shape [N].\n nms_keep_idx (Tensor): The index of kept bboxes after NMS. \n "
(boxes_idx, yolo_boxes_scores) = self.decode(head_out, anchors)
if (len(boxes_idx) == 0):
boxes_idx = self.fake_boxes_idx
yolo_boxes_out = self.fake_yolo_boxes_out
yolo_scores_out = self.fake_yolo_scores_out
else:
yolo_boxes = paddle.gather_nd(yolo_boxes_scores, boxes_idx)
yolo_boxes_out = paddle.reshape(yolo_boxes[:, :4], shape=[1, len(boxes_idx), 4])
yolo_scores_out = paddle.reshape(yolo_boxes[:, 4:5], shape=[1, 1, len(boxes_idx)])
boxes_idx = boxes_idx[:, 1:]
if self.return_idx:
(bbox_pred, bbox_num, nms_keep_idx) = self.nms(yolo_boxes_out, yolo_scores_out, self.num_classes)
if (bbox_pred.shape[0] == 0):
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
nms_keep_idx = self.fake_nms_keep_idx
return (boxes_idx, bbox_pred, bbox_num, nms_keep_idx)
else:
(bbox_pred, bbox_num, _) = self.nms(yolo_boxes_out, yolo_scores_out, self.num_classes)
if (bbox_pred.shape[0] == 0):
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
return (_, bbox_pred, bbox_num, _) | 1,565,260,054,661,251,800 | Decode the bbox and do NMS for JDE model.
Args:
head_out (list): Bbox_pred and cls_prob of bbox_head output.
anchors (list): Anchors of JDE model.
Returns:
boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'.
bbox_pred (Tensor): The output is the prediction with shape [N, 6]
including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction of each batch with shape [N].
nms_keep_idx (Tensor): The index of kept bboxes after NMS. | ppdet/modeling/post_process.py | forward | gbstack/PaddleDetection | python | def forward(self, head_out, anchors):
"\n Decode the bbox and do NMS for JDE model. \n\n Args:\n head_out (list): Bbox_pred and cls_prob of bbox_head output.\n anchors (list): Anchors of JDE model.\n\n Returns:\n boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'. \n bbox_pred (Tensor): The output is the prediction with shape [N, 6]\n including labels, scores and bboxes.\n bbox_num (Tensor): The number of prediction of each batch with shape [N].\n nms_keep_idx (Tensor): The index of kept bboxes after NMS. \n "
(boxes_idx, yolo_boxes_scores) = self.decode(head_out, anchors)
if (len(boxes_idx) == 0):
boxes_idx = self.fake_boxes_idx
yolo_boxes_out = self.fake_yolo_boxes_out
yolo_scores_out = self.fake_yolo_scores_out
else:
yolo_boxes = paddle.gather_nd(yolo_boxes_scores, boxes_idx)
yolo_boxes_out = paddle.reshape(yolo_boxes[:, :4], shape=[1, len(boxes_idx), 4])
yolo_scores_out = paddle.reshape(yolo_boxes[:, 4:5], shape=[1, 1, len(boxes_idx)])
boxes_idx = boxes_idx[:, 1:]
if self.return_idx:
(bbox_pred, bbox_num, nms_keep_idx) = self.nms(yolo_boxes_out, yolo_scores_out, self.num_classes)
if (bbox_pred.shape[0] == 0):
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
nms_keep_idx = self.fake_nms_keep_idx
return (boxes_idx, bbox_pred, bbox_num, nms_keep_idx)
else:
(bbox_pred, bbox_num, _) = self.nms(yolo_boxes_out, yolo_scores_out, self.num_classes)
if (bbox_pred.shape[0] == 0):
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
return (_, bbox_pred, bbox_num, _) |
def __call__(self, head_out, im_shape, scale_factor):
'\n Decode the bbox.\n\n Args:\n head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n bbox_pred (Tensor): The output prediction with shape [N, 6], including\n labels, scores and bboxes. The size of bboxes are corresponding\n to the input image, the bboxes may be used in other branch.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [bs], and is N.\n '
(bboxes, logits, masks) = head_out
bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
(img_h, img_w) = origin_shape.unbind(1)
origin_shape = paddle.stack([img_w, img_h, img_w, img_h], axis=(- 1)).unsqueeze(0)
bbox_pred *= origin_shape
scores = (F.sigmoid(logits) if self.use_focal_loss else F.softmax(logits)[:, :, :(- 1)])
if (not self.use_focal_loss):
(scores, labels) = (scores.max((- 1)), scores.argmax((- 1)))
if (scores.shape[1] > self.num_top_queries):
(scores, index) = paddle.topk(scores, self.num_top_queries, axis=(- 1))
labels = paddle.stack([paddle.gather(l, i) for (l, i) in zip(labels, index)])
bbox_pred = paddle.stack([paddle.gather(b, i) for (b, i) in zip(bbox_pred, index)])
else:
(scores, index) = paddle.topk(scores.reshape([logits.shape[0], (- 1)]), self.num_top_queries, axis=(- 1))
labels = (index % logits.shape[2])
index = (index // logits.shape[2])
bbox_pred = paddle.stack([paddle.gather(b, i) for (b, i) in zip(bbox_pred, index)])
bbox_pred = paddle.concat([labels.unsqueeze((- 1)).astype('float32'), scores.unsqueeze((- 1)), bbox_pred], axis=(- 1))
bbox_num = paddle.to_tensor(bbox_pred.shape[1], dtype='int32').tile([bbox_pred.shape[0]])
bbox_pred = bbox_pred.reshape([(- 1), 6])
return (bbox_pred, bbox_num) | -4,586,366,687,494,497,300 | Decode the bbox.
Args:
head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
bbox_pred (Tensor): The output prediction with shape [N, 6], including
labels, scores and bboxes. The size of bboxes are corresponding
to the input image, the bboxes may be used in other branch.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [bs], and is N. | ppdet/modeling/post_process.py | __call__ | gbstack/PaddleDetection | python | def __call__(self, head_out, im_shape, scale_factor):
'\n Decode the bbox.\n\n Args:\n head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.\n im_shape (Tensor): The shape of the input image.\n scale_factor (Tensor): The scale factor of the input image.\n Returns:\n bbox_pred (Tensor): The output prediction with shape [N, 6], including\n labels, scores and bboxes. The size of bboxes are corresponding\n to the input image, the bboxes may be used in other branch.\n bbox_num (Tensor): The number of prediction boxes of each batch with\n shape [bs], and is N.\n '
(bboxes, logits, masks) = head_out
bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
origin_shape = paddle.floor(((im_shape / scale_factor) + 0.5))
(img_h, img_w) = origin_shape.unbind(1)
origin_shape = paddle.stack([img_w, img_h, img_w, img_h], axis=(- 1)).unsqueeze(0)
bbox_pred *= origin_shape
scores = (F.sigmoid(logits) if self.use_focal_loss else F.softmax(logits)[:, :, :(- 1)])
if (not self.use_focal_loss):
(scores, labels) = (scores.max((- 1)), scores.argmax((- 1)))
if (scores.shape[1] > self.num_top_queries):
(scores, index) = paddle.topk(scores, self.num_top_queries, axis=(- 1))
labels = paddle.stack([paddle.gather(l, i) for (l, i) in zip(labels, index)])
bbox_pred = paddle.stack([paddle.gather(b, i) for (b, i) in zip(bbox_pred, index)])
else:
(scores, index) = paddle.topk(scores.reshape([logits.shape[0], (- 1)]), self.num_top_queries, axis=(- 1))
labels = (index % logits.shape[2])
index = (index // logits.shape[2])
bbox_pred = paddle.stack([paddle.gather(b, i) for (b, i) in zip(bbox_pred, index)])
bbox_pred = paddle.concat([labels.unsqueeze((- 1)).astype('float32'), scores.unsqueeze((- 1)), bbox_pred], axis=(- 1))
bbox_num = paddle.to_tensor(bbox_pred.shape[1], dtype='int32').tile([bbox_pred.shape[0]])
bbox_pred = bbox_pred.reshape([(- 1), 6])
return (bbox_pred, bbox_num) |
def __call__(self, box_cls, box_pred, scale_factor_wh, img_whwh):
'\n Arguments:\n box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).\n The tensor predicts the classification probability for each proposal.\n box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).\n The tensor predicts 4-vector (x,y,w,h) box\n regression values for every proposal\n scale_factor_wh (Tensor): tensors of shape [batch_size, 2] the scalor of per img\n img_whwh (Tensor): tensors of shape [batch_size, 4]\n Returns:\n bbox_pred (Tensor): tensors of shape [num_boxes, 6] Each row has 6 values:\n [label, confidence, xmin, ymin, xmax, ymax]\n bbox_num (Tensor): tensors of shape [batch_size] the number of RoIs in each image.\n '
assert (len(box_cls) == len(scale_factor_wh) == len(img_whwh))
img_wh = img_whwh[:, :2]
scores = F.sigmoid(box_cls)
labels = paddle.arange(0, self.num_classes).unsqueeze(0).tile([self.num_proposals, 1]).flatten(start_axis=0, stop_axis=1)
classes_all = []
scores_all = []
boxes_all = []
for (i, (scores_per_image, box_pred_per_image)) in enumerate(zip(scores, box_pred)):
(scores_per_image, topk_indices) = scores_per_image.flatten(0, 1).topk(self.num_proposals, sorted=False)
labels_per_image = paddle.gather(labels, topk_indices, axis=0)
box_pred_per_image = box_pred_per_image.reshape([(- 1), 1, 4]).tile([1, self.num_classes, 1]).reshape([(- 1), 4])
box_pred_per_image = paddle.gather(box_pred_per_image, topk_indices, axis=0)
classes_all.append(labels_per_image)
scores_all.append(scores_per_image)
boxes_all.append(box_pred_per_image)
bbox_num = paddle.zeros([len(scale_factor_wh)], dtype='int32')
boxes_final = []
for i in range(len(scale_factor_wh)):
classes = classes_all[i]
boxes = boxes_all[i]
scores = scores_all[i]
boxes[:, 0::2] = (paddle.clip(boxes[:, 0::2], min=0, max=img_wh[i][0]) / scale_factor_wh[i][0])
boxes[:, 1::2] = (paddle.clip(boxes[:, 1::2], min=0, max=img_wh[i][1]) / scale_factor_wh[i][1])
(boxes_w, boxes_h) = ((boxes[:, 2] - boxes[:, 0]).numpy(), (boxes[:, 3] - boxes[:, 1]).numpy())
keep = ((boxes_w > 1.0) & (boxes_h > 1.0))
if (keep.sum() == 0):
bboxes = paddle.zeros([1, 6]).astype('float32')
else:
boxes = paddle.to_tensor(boxes.numpy()[keep]).astype('float32')
classes = paddle.to_tensor(classes.numpy()[keep]).astype('float32').unsqueeze((- 1))
scores = paddle.to_tensor(scores.numpy()[keep]).astype('float32').unsqueeze((- 1))
bboxes = paddle.concat([classes, scores, boxes], axis=(- 1))
boxes_final.append(bboxes)
bbox_num[i] = bboxes.shape[0]
bbox_pred = paddle.concat(boxes_final)
return (bbox_pred, bbox_num) | -8,142,696,661,722,245,000 | Arguments:
box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).
The tensor predicts the classification probability for each proposal.
box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).
The tensor predicts 4-vector (x,y,w,h) box
regression values for every proposal
scale_factor_wh (Tensor): tensors of shape [batch_size, 2] the scalor of per img
img_whwh (Tensor): tensors of shape [batch_size, 4]
Returns:
bbox_pred (Tensor): tensors of shape [num_boxes, 6] Each row has 6 values:
[label, confidence, xmin, ymin, xmax, ymax]
bbox_num (Tensor): tensors of shape [batch_size] the number of RoIs in each image. | ppdet/modeling/post_process.py | __call__ | gbstack/PaddleDetection | python | def __call__(self, box_cls, box_pred, scale_factor_wh, img_whwh):
'\n Arguments:\n box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).\n The tensor predicts the classification probability for each proposal.\n box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).\n The tensor predicts 4-vector (x,y,w,h) box\n regression values for every proposal\n scale_factor_wh (Tensor): tensors of shape [batch_size, 2] the scalor of per img\n img_whwh (Tensor): tensors of shape [batch_size, 4]\n Returns:\n bbox_pred (Tensor): tensors of shape [num_boxes, 6] Each row has 6 values:\n [label, confidence, xmin, ymin, xmax, ymax]\n bbox_num (Tensor): tensors of shape [batch_size] the number of RoIs in each image.\n '
assert (len(box_cls) == len(scale_factor_wh) == len(img_whwh))
img_wh = img_whwh[:, :2]
scores = F.sigmoid(box_cls)
labels = paddle.arange(0, self.num_classes).unsqueeze(0).tile([self.num_proposals, 1]).flatten(start_axis=0, stop_axis=1)
classes_all = []
scores_all = []
boxes_all = []
for (i, (scores_per_image, box_pred_per_image)) in enumerate(zip(scores, box_pred)):
(scores_per_image, topk_indices) = scores_per_image.flatten(0, 1).topk(self.num_proposals, sorted=False)
labels_per_image = paddle.gather(labels, topk_indices, axis=0)
box_pred_per_image = box_pred_per_image.reshape([(- 1), 1, 4]).tile([1, self.num_classes, 1]).reshape([(- 1), 4])
box_pred_per_image = paddle.gather(box_pred_per_image, topk_indices, axis=0)
classes_all.append(labels_per_image)
scores_all.append(scores_per_image)
boxes_all.append(box_pred_per_image)
bbox_num = paddle.zeros([len(scale_factor_wh)], dtype='int32')
boxes_final = []
for i in range(len(scale_factor_wh)):
classes = classes_all[i]
boxes = boxes_all[i]
scores = scores_all[i]
boxes[:, 0::2] = (paddle.clip(boxes[:, 0::2], min=0, max=img_wh[i][0]) / scale_factor_wh[i][0])
boxes[:, 1::2] = (paddle.clip(boxes[:, 1::2], min=0, max=img_wh[i][1]) / scale_factor_wh[i][1])
(boxes_w, boxes_h) = ((boxes[:, 2] - boxes[:, 0]).numpy(), (boxes[:, 3] - boxes[:, 1]).numpy())
keep = ((boxes_w > 1.0) & (boxes_h > 1.0))
if (keep.sum() == 0):
bboxes = paddle.zeros([1, 6]).astype('float32')
else:
boxes = paddle.to_tensor(boxes.numpy()[keep]).astype('float32')
classes = paddle.to_tensor(classes.numpy()[keep]).astype('float32').unsqueeze((- 1))
scores = paddle.to_tensor(scores.numpy()[keep]).astype('float32').unsqueeze((- 1))
bboxes = paddle.concat([classes, scores, boxes], axis=(- 1))
boxes_final.append(bboxes)
bbox_num[i] = bboxes.shape[0]
bbox_pred = paddle.concat(boxes_final)
return (bbox_pred, bbox_num) |
def __init__(self, lst, start_x, start_y, height, width, headers=None) -> None:
'Lst is 2-d. i th list in lst is content of i+1 tab\n\n Each string in lst should not be of more length than width\n scroling is available only in vertical direction\n '
self.start_x = start_x
self.start_y = start_y
self.height = height
self.width = width
self.currentpos = 0
self.currenttab = 0
self.lst = lst
curses.curs_set(0)
newwin = curses.newwin(height, width, start_y, start_x)
newwin.border(0)
newwin.refresh()
self.display() | 3,092,889,274,640,904,000 | Lst is 2-d. i th list in lst is content of i+1 tab
Each string in lst should not be of more length than width
scroling is available only in vertical direction | src/client/ui/widget/displaylist.py | __init__ | Tubular-Terriers/code-jam | python | def __init__(self, lst, start_x, start_y, height, width, headers=None) -> None:
'Lst is 2-d. i th list in lst is content of i+1 tab\n\n Each string in lst should not be of more length than width\n scroling is available only in vertical direction\n '
self.start_x = start_x
self.start_y = start_y
self.height = height
self.width = width
self.currentpos = 0
self.currenttab = 0
self.lst = lst
curses.curs_set(0)
newwin = curses.newwin(height, width, start_y, start_x)
newwin.border(0)
newwin.refresh()
self.display() |
def authenticated_view(request):
'\n This view can be used to test requests with an authenticated user. Create a\n user with a default username, save it and then use this user to log in.\n Always returns a 200.\n '
user = User(username='Jane Doe')
user.save()
login(request, user)
return HttpResponse(status=200) | -7,649,399,858,114,833,000 | This view can be used to test requests with an authenticated user. Create a
user with a default username, save it and then use this user to log in.
Always returns a 200. | tests/contrib/django/django_app/urls.py | authenticated_view | AlexandreYang/dd-trace-py | python | def authenticated_view(request):
'\n This view can be used to test requests with an authenticated user. Create a\n user with a default username, save it and then use this user to log in.\n Always returns a 200.\n '
user = User(username='Jane Doe')
user.save()
login(request, user)
return HttpResponse(status=200) |
def gelu(x):
'Gaussian Error Linear Unit.\n This is a smoother version of the RELU.\n Original paper: https://arxiv.org/abs/1606.08415\n Args:\n x: float Tensor to perform activation.\n Returns:\n `x` with the GELU activation applied.\n '
cdf = (0.5 * (1.0 + tf.tanh((np.sqrt((2 / np.pi)) * (x + (0.044715 * tf.pow(x, 3)))))))
return (x * cdf) | 3,645,099,331,411,212,300 | Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied. | transformers/modeling_tf_openai.py | gelu | richardbaihe/semantic_unwritten | python | def gelu(x):
'Gaussian Error Linear Unit.\n This is a smoother version of the RELU.\n Original paper: https://arxiv.org/abs/1606.08415\n Args:\n x: float Tensor to perform activation.\n Returns:\n `x` with the GELU activation applied.\n '
cdf = (0.5 * (1.0 + tf.tanh((np.sqrt((2 / np.pi)) * (x + (0.044715 * tf.pow(x, 3)))))))
return (x * cdf) |
@staticmethod
def causal_attention_mask(nd, ns, dtype):
"1's in the lower triangle, counting from the lower right corner.\n Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs.\n "
i = tf.range(nd)[:, None]
j = tf.range(ns)
m = (i >= ((j - ns) + nd))
return tf.cast(m, dtype) | 458,533,279,018,134,600 | 1's in the lower triangle, counting from the lower right corner.
Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs. | transformers/modeling_tf_openai.py | causal_attention_mask | richardbaihe/semantic_unwritten | python | @staticmethod
def causal_attention_mask(nd, ns, dtype):
"1's in the lower triangle, counting from the lower right corner.\n Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs.\n "
i = tf.range(nd)[:, None]
j = tf.range(ns)
m = (i >= ((j - ns) + nd))
return tf.cast(m, dtype) |
def _prune_heads(self, heads_to_prune):
' Prunes heads of the model.\n heads_to_prune: dict of {layer_num: list of heads to prune in this layer}\n '
raise NotImplementedError | -86,650,990,666,581,820 | Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} | transformers/modeling_tf_openai.py | _prune_heads | richardbaihe/semantic_unwritten | python | def _prune_heads(self, heads_to_prune):
' Prunes heads of the model.\n heads_to_prune: dict of {layer_num: list of heads to prune in this layer}\n '
raise NotImplementedError |
def get_pileup_vect(alignments, contig, pos, ref, alt):
'Create feature vector for selected variant\n\n Args:\n alignments(pysam.AlignmentFile) : Handle to alignmentfile\n contig(str) : contig to perform pileup\n pos(int) : zeros based position of variant to pileup\n ref(str) : reference base\n alt(str) : alternative base\n\n Returns\n total(int) : Total amount of bases overlapping with selected location\n ref_calls : Total amount of bases matching ref\n alt_calls : Total amount of bases matching alt\n other_calls : Total amount of bases matching neither ref nor alt\n '
total = 0
ref_calls = 0
alt_calls = 0
other_calls = 0
start = pos
stop = (pos + 1)
for pileupcolumn in pileup_truncated(alignments, contig, start, stop, stepper='all'):
for (i, pileupread) in enumerate(pileupcolumn.pileups):
if ((not pileupread.is_del) and (not pileupread.is_refskip)):
call = pileupread.alignment.query_sequence[pileupread.query_position]
if (call == ref):
ref_calls += 1
elif (call == alt):
alt_calls += 1
else:
other_calls += 1
other_calls += 1
return (total, ref_calls, alt_calls, other_calls) | 5,540,029,937,697,622,000 | Create feature vector for selected variant
Args:
alignments(pysam.AlignmentFile) : Handle to alignmentfile
contig(str) : contig to perform pileup
pos(int) : zeros based position of variant to pileup
ref(str) : reference base
alt(str) : alternative base
Returns
total(int) : Total amount of bases overlapping with selected location
ref_calls : Total amount of bases matching ref
alt_calls : Total amount of bases matching alt
other_calls : Total amount of bases matching neither ref nor alt | singlecellmultiomics/bamProcessing/bamFeatures.py | get_pileup_vect | BuysDB/SingleCellMultiOmics | python | def get_pileup_vect(alignments, contig, pos, ref, alt):
'Create feature vector for selected variant\n\n Args:\n alignments(pysam.AlignmentFile) : Handle to alignmentfile\n contig(str) : contig to perform pileup\n pos(int) : zeros based position of variant to pileup\n ref(str) : reference base\n alt(str) : alternative base\n\n Returns\n total(int) : Total amount of bases overlapping with selected location\n ref_calls : Total amount of bases matching ref\n alt_calls : Total amount of bases matching alt\n other_calls : Total amount of bases matching neither ref nor alt\n '
total = 0
ref_calls = 0
alt_calls = 0
other_calls = 0
start = pos
stop = (pos + 1)
for pileupcolumn in pileup_truncated(alignments, contig, start, stop, stepper='all'):
for (i, pileupread) in enumerate(pileupcolumn.pileups):
if ((not pileupread.is_del) and (not pileupread.is_refskip)):
call = pileupread.alignment.query_sequence[pileupread.query_position]
if (call == ref):
ref_calls += 1
elif (call == alt):
alt_calls += 1
else:
other_calls += 1
other_calls += 1
return (total, ref_calls, alt_calls, other_calls) |
def get_mapping_q_vect(alignments_handle, contig, pos, radius=150):
'Obtain histogram of mapping qualties, clipped at 60\n\n Args:\n alignments(pysam.AlignmentFile) : Handle to alignmentfile\n contig(str) : contig\n pos(int) : zeros based position of location to check mapping qualties\n radius(int) : radius to check around selected location\n\n Returns:\n mapping_qualities(list) : Histogram with 7 bins (0 to highest mapping quality)\n '
mapping_qualities = ([0] * 7)
for read in alignments_handle.fetch(contig, (pos - radius), (pos + radius)):
mapping_qualities[min(60, int((read.mapping_quality / 10)))] += 1
return mapping_qualities | 4,255,158,074,448,860,000 | Obtain histogram of mapping qualties, clipped at 60
Args:
alignments(pysam.AlignmentFile) : Handle to alignmentfile
contig(str) : contig
pos(int) : zeros based position of location to check mapping qualties
radius(int) : radius to check around selected location
Returns:
mapping_qualities(list) : Histogram with 7 bins (0 to highest mapping quality) | singlecellmultiomics/bamProcessing/bamFeatures.py | get_mapping_q_vect | BuysDB/SingleCellMultiOmics | python | def get_mapping_q_vect(alignments_handle, contig, pos, radius=150):
'Obtain histogram of mapping qualties, clipped at 60\n\n Args:\n alignments(pysam.AlignmentFile) : Handle to alignmentfile\n contig(str) : contig\n pos(int) : zeros based position of location to check mapping qualties\n radius(int) : radius to check around selected location\n\n Returns:\n mapping_qualities(list) : Histogram with 7 bins (0 to highest mapping quality)\n '
mapping_qualities = ([0] * 7)
for read in alignments_handle.fetch(contig, (pos - radius), (pos + radius)):
mapping_qualities[min(60, int((read.mapping_quality / 10)))] += 1
return mapping_qualities |
def __init__(self, default_parameter, trial_run_parameter, lower_is_better, objective, filename_objective, argo_ip, argo_port, k8_namespace, storage_strategy='keep', output_dir=''):
"Set init values\n\n Args:\n default_parameter (dict): Parameter that will be submitted with the argo workflow in a kind of input flags.\n rebuild_parameter (dict): Parameter that were genereted when creating the hp for sherpa.\n lower_is_better (bool): whether to minimize or maximize the objective\n objective (str): Name of the objective that will be optimized for. Must be a key/name from the metrics that were generated within a trial run.\n filename_objective (str): Filename of the file that contains the objective value which was created within a trial run.\n argo_ip (str): Argo server ip\n argp_port (str): Argo server port\n k8_namespace (str): Name of the kubernetes namespace where the trial container should be executed.\n storage_strategy (str, optional): wether to keep all, delete all or keep the files from the best run. Defaults to 'keep'.\n output_dir (str): needed for sherpa api\n "
if ('api_exec_token' in os.environ):
api_token = ('Bearer ' + os.environ['api_exec_token'])
else:
logging.error('No Authorization Token detected. Check Kubernetes Secrets and Argo Template!')
logging.info('Default Parameter: {}'.format(default_parameter))
self.submit_url = (((((('https://' + argo_ip) + ':') + argo_port) + '/api/v1/workflows/') + k8_namespace) + '/submit')
self.status_url = (((((('https://' + argo_ip) + ':') + argo_port) + '/api/v1/workflows/') + k8_namespace) + '/')
self.delete_url = self.status_url
self.client = Client()
self.best_metric = {'job_id': None, 'metric': None}
self.headers = {'Authorization': api_token}
self.killed_jobs = []
self.output_dir = output_dir
self.default_parameter = default_parameter
self.trial_run_parameter = trial_run_parameter
self.storage_strategy = storage_strategy
self.hostname = socket.gethostname()
self.trials = {}
self.run_name = self.default_parameter['run_name']
self.metrics_filename = filename_objective
self.objective = objective
self.lower_is_better = lower_is_better
self.output_path = self.default_parameter['output_path']
self.decode_status = {'Succeeded': _JobStatus.finished, 'Running': _JobStatus.running, 'Pending': _JobStatus.queued, 'Failed': _JobStatus.failed, 'Stopped': _JobStatus.killed, 'Other': _JobStatus.other} | -1,925,804,667,319,815,000 | Set init values
Args:
default_parameter (dict): Parameter that will be submitted with the argo workflow in a kind of input flags.
rebuild_parameter (dict): Parameter that were genereted when creating the hp for sherpa.
lower_is_better (bool): whether to minimize or maximize the objective
objective (str): Name of the objective that will be optimized for. Must be a key/name from the metrics that were generated within a trial run.
filename_objective (str): Filename of the file that contains the objective value which was created within a trial run.
argo_ip (str): Argo server ip
argp_port (str): Argo server port
k8_namespace (str): Name of the kubernetes namespace where the trial container should be executed.
storage_strategy (str, optional): wether to keep all, delete all or keep the files from the best run. Defaults to 'keep'.
output_dir (str): needed for sherpa api | argo_scheduler.py | __init__ | predictive-quality/ml-pipeline-blocks-hpo-sherpa | python | def __init__(self, default_parameter, trial_run_parameter, lower_is_better, objective, filename_objective, argo_ip, argo_port, k8_namespace, storage_strategy='keep', output_dir=):
"Set init values\n\n Args:\n default_parameter (dict): Parameter that will be submitted with the argo workflow in a kind of input flags.\n rebuild_parameter (dict): Parameter that were genereted when creating the hp for sherpa.\n lower_is_better (bool): whether to minimize or maximize the objective\n objective (str): Name of the objective that will be optimized for. Must be a key/name from the metrics that were generated within a trial run.\n filename_objective (str): Filename of the file that contains the objective value which was created within a trial run.\n argo_ip (str): Argo server ip\n argp_port (str): Argo server port\n k8_namespace (str): Name of the kubernetes namespace where the trial container should be executed.\n storage_strategy (str, optional): wether to keep all, delete all or keep the files from the best run. Defaults to 'keep'.\n output_dir (str): needed for sherpa api\n "
if ('api_exec_token' in os.environ):
api_token = ('Bearer ' + os.environ['api_exec_token'])
else:
logging.error('No Authorization Token detected. Check Kubernetes Secrets and Argo Template!')
logging.info('Default Parameter: {}'.format(default_parameter))
self.submit_url = (((((('https://' + argo_ip) + ':') + argo_port) + '/api/v1/workflows/') + k8_namespace) + '/submit')
self.status_url = (((((('https://' + argo_ip) + ':') + argo_port) + '/api/v1/workflows/') + k8_namespace) + '/')
self.delete_url = self.status_url
self.client = Client()
self.best_metric = {'job_id': None, 'metric': None}
self.headers = {'Authorization': api_token}
self.killed_jobs = []
self.output_dir = output_dir
self.default_parameter = default_parameter
self.trial_run_parameter = trial_run_parameter
self.storage_strategy = storage_strategy
self.hostname = socket.gethostname()
self.trials = {}
self.run_name = self.default_parameter['run_name']
self.metrics_filename = filename_objective
self.objective = objective
self.lower_is_better = lower_is_better
self.output_path = self.default_parameter['output_path']
self.decode_status = {'Succeeded': _JobStatus.finished, 'Running': _JobStatus.running, 'Pending': _JobStatus.queued, 'Failed': _JobStatus.failed, 'Stopped': _JobStatus.killed, 'Other': _JobStatus.other} |
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