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def thread_task(tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[(str, Any)]], seed: Optional[int], results: TestResultSet, kwargs: Any) -> None:
'A single task, that threads do.\n\n Pretty similar to the default one-thread flow, but includes communication with the main thread via the events queue.\n '
prepared_auth = get_requests_auth(auth, auth_type)
with get_session(prepared_auth, headers) as session:
_run_task(network_test, tasks_queue, events_queue, schema, checks, settings, seed, results, session=session, **kwargs) | 8,466,409,781,368,943,000 | A single task, that threads do.
Pretty similar to the default one-thread flow, but includes communication with the main thread via the events queue. | src/schemathesis/runner/__init__.py | thread_task | hlobit/schemathesis | python | def thread_task(tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[(str, Any)]], seed: Optional[int], results: TestResultSet, kwargs: Any) -> None:
'A single task, that threads do.\n\n Pretty similar to the default one-thread flow, but includes communication with the main thread via the events queue.\n '
prepared_auth = get_requests_auth(auth, auth_type)
with get_session(prepared_auth, headers) as session:
_run_task(network_test, tasks_queue, events_queue, schema, checks, settings, seed, results, session=session, **kwargs) |
def stop_worker(thread_id: int) -> None:
'Raise an error in a thread so it is possible to asynchronously stop thread execution.'
ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_long(thread_id), ctypes.py_object(SystemExit)) | -599,246,663,343,350,700 | Raise an error in a thread so it is possible to asynchronously stop thread execution. | src/schemathesis/runner/__init__.py | stop_worker | hlobit/schemathesis | python | def stop_worker(thread_id: int) -> None:
ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_long(thread_id), ctypes.py_object(SystemExit)) |
def execute_from_schema(schema: BaseSchema, checks: Iterable[Callable], *, workers_num: int=1, hypothesis_options: Optional[Dict[(str, Any)]]=None, auth: Optional[RawAuth]=None, auth_type: Optional[str]=None, headers: Optional[Dict[(str, Any)]]=None, request_timeout: Optional[int]=None, seed: Optional[int]=None) -> Generator[(events.ExecutionEvent, None, None)]:
'Execute tests for the given schema.\n\n Provides the main testing loop and preparation step.\n '
runner: BaseRunner
if (workers_num > 1):
if schema.app:
runner = ThreadPoolWSGIRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, workers_num=workers_num)
else:
runner = ThreadPoolRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout)
elif schema.app:
runner = SingleThreadWSGIRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed)
else:
runner = SingleThreadRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout)
(yield from runner.execute()) | 3,084,922,587,949,720,600 | Execute tests for the given schema.
Provides the main testing loop and preparation step. | src/schemathesis/runner/__init__.py | execute_from_schema | hlobit/schemathesis | python | def execute_from_schema(schema: BaseSchema, checks: Iterable[Callable], *, workers_num: int=1, hypothesis_options: Optional[Dict[(str, Any)]]=None, auth: Optional[RawAuth]=None, auth_type: Optional[str]=None, headers: Optional[Dict[(str, Any)]]=None, request_timeout: Optional[int]=None, seed: Optional[int]=None) -> Generator[(events.ExecutionEvent, None, None)]:
'Execute tests for the given schema.\n\n Provides the main testing loop and preparation step.\n '
runner: BaseRunner
if (workers_num > 1):
if schema.app:
runner = ThreadPoolWSGIRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, workers_num=workers_num)
else:
runner = ThreadPoolRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout)
elif schema.app:
runner = SingleThreadWSGIRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed)
else:
runner = SingleThreadRunner(schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout)
(yield from runner.execute()) |
def run_test(schema: BaseSchema, endpoint: Endpoint, test: Union[(Callable, InvalidSchema)], checks: Iterable[Callable], results: TestResultSet, **kwargs: Any) -> Generator[(events.ExecutionEvent, None, None)]:
'A single test run with all error handling needed.'
result = TestResult(endpoint=endpoint)
(yield events.BeforeExecution(results=results, schema=schema, endpoint=endpoint))
hypothesis_output: List[str] = []
try:
if isinstance(test, InvalidSchema):
status = Status.error
result.add_error(test)
else:
with capture_hypothesis_output() as hypothesis_output:
test(checks, result, **kwargs)
status = Status.success
except AssertionError:
status = Status.failure
except hypothesis.errors.Flaky:
status = Status.error
result.mark_errored()
if result.checks:
flaky_example = result.checks[(- 1)].example
else:
flaky_example = None
result.add_error(hypothesis.errors.Flaky('Tests on this endpoint produce unreliable results: \nFalsified on the first call but did not on a subsequent one'), flaky_example)
except hypothesis.errors.Unsatisfiable:
status = Status.error
result.add_error(hypothesis.errors.Unsatisfiable('Unable to satisfy schema parameters for this endpoint'))
except KeyboardInterrupt:
(yield events.Interrupted(results=results, schema=schema))
return
except Exception as error:
status = Status.error
result.add_error(error)
result.seed = (getattr(test, '_hypothesis_internal_use_seed', None) or getattr(test, '_hypothesis_internal_use_generated_seed', None))
results.append(result)
(yield events.AfterExecution(results=results, schema=schema, endpoint=endpoint, status=status, hypothesis_output=hypothesis_output)) | 4,248,110,188,219,264,000 | A single test run with all error handling needed. | src/schemathesis/runner/__init__.py | run_test | hlobit/schemathesis | python | def run_test(schema: BaseSchema, endpoint: Endpoint, test: Union[(Callable, InvalidSchema)], checks: Iterable[Callable], results: TestResultSet, **kwargs: Any) -> Generator[(events.ExecutionEvent, None, None)]:
result = TestResult(endpoint=endpoint)
(yield events.BeforeExecution(results=results, schema=schema, endpoint=endpoint))
hypothesis_output: List[str] = []
try:
if isinstance(test, InvalidSchema):
status = Status.error
result.add_error(test)
else:
with capture_hypothesis_output() as hypothesis_output:
test(checks, result, **kwargs)
status = Status.success
except AssertionError:
status = Status.failure
except hypothesis.errors.Flaky:
status = Status.error
result.mark_errored()
if result.checks:
flaky_example = result.checks[(- 1)].example
else:
flaky_example = None
result.add_error(hypothesis.errors.Flaky('Tests on this endpoint produce unreliable results: \nFalsified on the first call but did not on a subsequent one'), flaky_example)
except hypothesis.errors.Unsatisfiable:
status = Status.error
result.add_error(hypothesis.errors.Unsatisfiable('Unable to satisfy schema parameters for this endpoint'))
except KeyboardInterrupt:
(yield events.Interrupted(results=results, schema=schema))
return
except Exception as error:
status = Status.error
result.add_error(error)
result.seed = (getattr(test, '_hypothesis_internal_use_seed', None) or getattr(test, '_hypothesis_internal_use_generated_seed', None))
results.append(result)
(yield events.AfterExecution(results=results, schema=schema, endpoint=endpoint, status=status, hypothesis_output=hypothesis_output)) |
def prepare(schema_uri: str, checks: Iterable[Callable]=DEFAULT_CHECKS, workers_num: int=1, api_options: Optional[Dict[(str, Any)]]=None, loader_options: Optional[Dict[(str, Any)]]=None, hypothesis_options: Optional[Dict[(str, Any)]]=None, loader: Callable=from_uri, seed: Optional[int]=None) -> Generator[(events.ExecutionEvent, None, None)]:
'Prepare a generator that will run test cases against the given API definition.'
api_options = (api_options or {})
loader_options = (loader_options or {})
if ('base_url' not in loader_options):
loader_options['base_url'] = get_base_url(schema_uri)
schema = loader(schema_uri, **loader_options)
return execute_from_schema(schema, checks, hypothesis_options=hypothesis_options, seed=seed, workers_num=workers_num, **api_options) | 5,755,991,865,344,927,000 | Prepare a generator that will run test cases against the given API definition. | src/schemathesis/runner/__init__.py | prepare | hlobit/schemathesis | python | def prepare(schema_uri: str, checks: Iterable[Callable]=DEFAULT_CHECKS, workers_num: int=1, api_options: Optional[Dict[(str, Any)]]=None, loader_options: Optional[Dict[(str, Any)]]=None, hypothesis_options: Optional[Dict[(str, Any)]]=None, loader: Callable=from_uri, seed: Optional[int]=None) -> Generator[(events.ExecutionEvent, None, None)]:
api_options = (api_options or {})
loader_options = (loader_options or {})
if ('base_url' not in loader_options):
loader_options['base_url'] = get_base_url(schema_uri)
schema = loader(schema_uri, **loader_options)
return execute_from_schema(schema, checks, hypothesis_options=hypothesis_options, seed=seed, workers_num=workers_num, **api_options) |
def network_test(case: Case, checks: Iterable[Callable], result: TestResult, session: requests.Session, request_timeout: Optional[int]) -> None:
'A single test body that will be executed against the target.'
timeout = prepare_timeout(request_timeout)
response = case.call(session=session, timeout=timeout)
_run_checks(case, checks, result, response) | 788,102,240,383,414,400 | A single test body that will be executed against the target. | src/schemathesis/runner/__init__.py | network_test | hlobit/schemathesis | python | def network_test(case: Case, checks: Iterable[Callable], result: TestResult, session: requests.Session, request_timeout: Optional[int]) -> None:
timeout = prepare_timeout(request_timeout)
response = case.call(session=session, timeout=timeout)
_run_checks(case, checks, result, response) |
def prepare_timeout(timeout: Optional[int]) -> Optional[float]:
'Request timeout is in milliseconds, but `requests` uses seconds'
output: Optional[Union[(int, float)]] = timeout
if (timeout is not None):
output = (timeout / 1000)
return output | 2,219,370,182,692,796,400 | Request timeout is in milliseconds, but `requests` uses seconds | src/schemathesis/runner/__init__.py | prepare_timeout | hlobit/schemathesis | python | def prepare_timeout(timeout: Optional[int]) -> Optional[float]:
output: Optional[Union[(int, float)]] = timeout
if (timeout is not None):
output = (timeout / 1000)
return output |
def execute(self) -> Generator[(events.ExecutionEvent, None, None)]:
'Common logic for all runners.'
results = TestResultSet()
initialized = events.Initialized(results=results, schema=self.schema, checks=self.checks, hypothesis_settings=self.hypothesis_settings)
(yield initialized)
(yield from self._execute(results))
(yield events.Finished(results=results, schema=self.schema, running_time=(time.time() - initialized.start_time))) | -4,920,484,788,315,777,000 | Common logic for all runners. | src/schemathesis/runner/__init__.py | execute | hlobit/schemathesis | python | def execute(self) -> Generator[(events.ExecutionEvent, None, None)]:
results = TestResultSet()
initialized = events.Initialized(results=results, schema=self.schema, checks=self.checks, hypothesis_settings=self.hypothesis_settings)
(yield initialized)
(yield from self._execute(results))
(yield events.Finished(results=results, schema=self.schema, running_time=(time.time() - initialized.start_time))) |
def _execute(self, results: TestResultSet) -> Generator[(events.ExecutionEvent, None, None)]:
'All events come from a queue where different workers push their events.'
tasks_queue = self._get_tasks_queue()
events_queue: Queue = Queue()
workers = self._init_workers(tasks_queue, events_queue, results)
def stop_workers() -> None:
for worker in workers:
ident = cast(int, worker.ident)
stop_worker(ident)
worker.join()
is_finished = False
try:
while (not is_finished):
time.sleep(0.001)
is_finished = all(((not worker.is_alive()) for worker in workers))
while (not events_queue.empty()):
event = events_queue.get()
(yield event)
if isinstance(event, events.Interrupted):
raise ThreadInterrupted
except ThreadInterrupted:
stop_workers()
except KeyboardInterrupt:
stop_workers()
(yield events.Interrupted(results=results, schema=self.schema)) | -649,910,977,915,766,500 | All events come from a queue where different workers push their events. | src/schemathesis/runner/__init__.py | _execute | hlobit/schemathesis | python | def _execute(self, results: TestResultSet) -> Generator[(events.ExecutionEvent, None, None)]:
tasks_queue = self._get_tasks_queue()
events_queue: Queue = Queue()
workers = self._init_workers(tasks_queue, events_queue, results)
def stop_workers() -> None:
for worker in workers:
ident = cast(int, worker.ident)
stop_worker(ident)
worker.join()
is_finished = False
try:
while (not is_finished):
time.sleep(0.001)
is_finished = all(((not worker.is_alive()) for worker in workers))
while (not events_queue.empty()):
event = events_queue.get()
(yield event)
if isinstance(event, events.Interrupted):
raise ThreadInterrupted
except ThreadInterrupted:
stop_workers()
except KeyboardInterrupt:
stop_workers()
(yield events.Interrupted(results=results, schema=self.schema)) |
def _get_tasks_queue(self) -> Queue:
'All endpoints are distributed among all workers via a queue.'
tasks_queue: Queue = Queue()
tasks_queue.queue.extend(self.schema.get_all_endpoints())
return tasks_queue | 8,114,420,042,134,657,000 | All endpoints are distributed among all workers via a queue. | src/schemathesis/runner/__init__.py | _get_tasks_queue | hlobit/schemathesis | python | def _get_tasks_queue(self) -> Queue:
tasks_queue: Queue = Queue()
tasks_queue.queue.extend(self.schema.get_all_endpoints())
return tasks_queue |
def _init_workers(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> List[threading.Thread]:
'Initialize & start workers that will execute tests.'
workers = [threading.Thread(target=self._get_task(), kwargs=self._get_worker_kwargs(tasks_queue, events_queue, results)) for _ in range(self.workers_num)]
for worker in workers:
worker.start()
return workers | 2,309,366,165,997,026,300 | Initialize & start workers that will execute tests. | src/schemathesis/runner/__init__.py | _init_workers | hlobit/schemathesis | python | def _init_workers(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> List[threading.Thread]:
workers = [threading.Thread(target=self._get_task(), kwargs=self._get_worker_kwargs(tasks_queue, events_queue, results)) for _ in range(self.workers_num)]
for worker in workers:
worker.start()
return workers |
def __str__(self):
' print out some basic information about the BC object '
string = ('BCs: -x: %s +x: %s ' % (self.xlb, self.xrb))
return string | 469,757,932,280,750,660 | print out some basic information about the BC object | multigrid/patch1d.py | __str__ | python-hydro/hydro_examples | python | def __str__(self):
' '
string = ('BCs: -x: %s +x: %s ' % (self.xlb, self.xrb))
return string |
def __init__(self, nx, ng=1, xmin=0.0, xmax=1.0):
'\n The class constructor function.\n\n The only data that we require is the number of points that\n make up the mesh.\n\n We optionally take the extrema of the domain, number of ghost\n cells (assume 1)\n '
self.nx = nx
self.ng = ng
self.qx = ((2 * ng) + nx)
self.xmin = xmin
self.xmax = xmax
self.ilo = ng
self.ihi = ((ng + nx) - 1)
self.dx = ((xmax - xmin) / nx)
self.xl = (((numpy.arange((nx + (2 * ng))) - ng) * self.dx) + xmin)
self.xr = ((((numpy.arange((nx + (2 * ng))) + 1.0) - ng) * self.dx) + xmin)
self.x = (0.5 * (self.xl + self.xr)) | 1,316,086,383,784,868,400 | The class constructor function.
The only data that we require is the number of points that
make up the mesh.
We optionally take the extrema of the domain, number of ghost
cells (assume 1) | multigrid/patch1d.py | __init__ | python-hydro/hydro_examples | python | def __init__(self, nx, ng=1, xmin=0.0, xmax=1.0):
'\n The class constructor function.\n\n The only data that we require is the number of points that\n make up the mesh.\n\n We optionally take the extrema of the domain, number of ghost\n cells (assume 1)\n '
self.nx = nx
self.ng = ng
self.qx = ((2 * ng) + nx)
self.xmin = xmin
self.xmax = xmax
self.ilo = ng
self.ihi = ((ng + nx) - 1)
self.dx = ((xmax - xmin) / nx)
self.xl = (((numpy.arange((nx + (2 * ng))) - ng) * self.dx) + xmin)
self.xr = ((((numpy.arange((nx + (2 * ng))) + 1.0) - ng) * self.dx) + xmin)
self.x = (0.5 * (self.xl + self.xr)) |
def __str__(self):
' print out some basic information about the grid object '
return '1-d grid: nx = {}, ng = {}'.format(self.nx, self.ng) | -7,226,611,515,420,033,000 | print out some basic information about the grid object | multigrid/patch1d.py | __str__ | python-hydro/hydro_examples | python | def __str__(self):
' '
return '1-d grid: nx = {}, ng = {}'.format(self.nx, self.ng) |
def register_var(self, name, bc_object):
'\n register a variable with CellCenterData1d object. Here we pass in a\n BCObject that describes the boundary conditions for that\n variable.\n '
if (self.initialized == 1):
sys.exit('ERROR: grid already initialized')
self.vars.append(name)
self.nvar += 1
self.BCs[name] = bc_object | 2,683,589,669,001,459,000 | register a variable with CellCenterData1d object. Here we pass in a
BCObject that describes the boundary conditions for that
variable. | multigrid/patch1d.py | register_var | python-hydro/hydro_examples | python | def register_var(self, name, bc_object):
'\n register a variable with CellCenterData1d object. Here we pass in a\n BCObject that describes the boundary conditions for that\n variable.\n '
if (self.initialized == 1):
sys.exit('ERROR: grid already initialized')
self.vars.append(name)
self.nvar += 1
self.BCs[name] = bc_object |
def create(self):
'\n called after all the variables are registered and allocates\n the storage for the state data\n '
if (self.initialized == 1):
sys.exit('ERROR: grid already initialized')
self.data = numpy.zeros((self.nvar, self.grid.qx), dtype=self.dtype)
self.initialized = 1 | -7,108,092,911,557,650,000 | called after all the variables are registered and allocates
the storage for the state data | multigrid/patch1d.py | create | python-hydro/hydro_examples | python | def create(self):
'\n called after all the variables are registered and allocates\n the storage for the state data\n '
if (self.initialized == 1):
sys.exit('ERROR: grid already initialized')
self.data = numpy.zeros((self.nvar, self.grid.qx), dtype=self.dtype)
self.initialized = 1 |
def __str__(self):
' print out some basic information about the ccData2d object '
if (self.initialized == 0):
mystr = 'CellCenterData1d object not yet initialized'
return mystr
mystr = (('cc data: nx = {}, ng = {}\n'.format(self.grid.nx, self.grid.ng) + ' nvars = {}\n'.format(self.nvar)) + 'variables: \n')
ilo = self.grid.ilo
ihi = self.grid.ihi
for n in range(self.nvar):
mystr += ('%16s: min: %15.10f max: %15.10f\n' % (self.vars[n], numpy.min(self.data[n, ilo:(ihi + 1)]), numpy.max(self.data[n, ilo:(ihi + 1)])))
mystr += ('%16s BCs: -x: %-12s +x: %-12s \n' % (' ', self.BCs[self.vars[n]].xlb, self.BCs[self.vars[n]].xrb))
return mystr | 8,466,030,980,728,462,000 | print out some basic information about the ccData2d object | multigrid/patch1d.py | __str__ | python-hydro/hydro_examples | python | def __str__(self):
' '
if (self.initialized == 0):
mystr = 'CellCenterData1d object not yet initialized'
return mystr
mystr = (('cc data: nx = {}, ng = {}\n'.format(self.grid.nx, self.grid.ng) + ' nvars = {}\n'.format(self.nvar)) + 'variables: \n')
ilo = self.grid.ilo
ihi = self.grid.ihi
for n in range(self.nvar):
mystr += ('%16s: min: %15.10f max: %15.10f\n' % (self.vars[n], numpy.min(self.data[n, ilo:(ihi + 1)]), numpy.max(self.data[n, ilo:(ihi + 1)])))
mystr += ('%16s BCs: -x: %-12s +x: %-12s \n' % (' ', self.BCs[self.vars[n]].xlb, self.BCs[self.vars[n]].xrb))
return mystr |
def get_var(self, name):
'\n return a data array the variable described by name. Any changes\n made to this are automatically reflected in the CellCenterData1d\n object.\n '
n = self.vars.index(name)
return self.data[n, :] | -905,601,490,949,444,400 | return a data array the variable described by name. Any changes
made to this are automatically reflected in the CellCenterData1d
object. | multigrid/patch1d.py | get_var | python-hydro/hydro_examples | python | def get_var(self, name):
'\n return a data array the variable described by name. Any changes\n made to this are automatically reflected in the CellCenterData1d\n object.\n '
n = self.vars.index(name)
return self.data[n, :] |
def fill_BC_all(self):
'\n fill boundary conditions on all variables\n '
for name in self.vars:
self.fill_BC(name) | -5,289,033,638,672,699,000 | fill boundary conditions on all variables | multigrid/patch1d.py | fill_BC_all | python-hydro/hydro_examples | python | def fill_BC_all(self):
'\n \n '
for name in self.vars:
self.fill_BC(name) |
def fill_BC(self, name):
'\n fill the boundary conditions. This operates on a single state\n variable at a time, to allow for maximum flexibility\n\n we do periodic, reflect-even, reflect-odd, and outflow\n\n each variable name has a corresponding bc_object stored in the\n ccData2d object -- we refer to this to figure out the action\n to take at each boundary.\n '
n = self.vars.index(name)
if ((self.BCs[name].xlb == 'outflow') or (self.BCs[name].xlb == 'neumann')):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, self.grid.ilo)]
elif (self.BCs[name].xlb == 'reflect-even'):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, (((2 * self.grid.ng) - i) - 1))]
elif (self.BCs[name].xlb in ['reflect-odd', 'dirichlet']):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = (- self.data[(n, (((2 * self.grid.ng) - i) - 1))])
elif (self.BCs[name].xlb == 'periodic'):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, (((self.grid.ihi - self.grid.ng) + i) + 1))]
if ((self.BCs[name].xrb == 'outflow') or (self.BCs[name].xrb == 'neumann')):
for i in range((self.grid.ihi + 1), (self.grid.nx + (2 * self.grid.ng))):
self.data[(n, i)] = self.data[(n, self.grid.ihi)]
elif (self.BCs[name].xrb == 'reflect-even'):
for i in range(0, self.grid.ng):
i_bnd = ((self.grid.ihi + 1) + i)
i_src = (self.grid.ihi - i)
self.data[(n, i_bnd)] = self.data[(n, i_src)]
elif (self.BCs[name].xrb in ['reflect-odd', 'dirichlet']):
for i in range(0, self.grid.ng):
i_bnd = ((self.grid.ihi + 1) + i)
i_src = (self.grid.ihi - i)
self.data[(n, i_bnd)] = (- self.data[(n, i_src)])
elif (self.BCs[name].xrb == 'periodic'):
for i in range((self.grid.ihi + 1), ((2 * self.grid.ng) + self.grid.nx)):
self.data[(n, i)] = self.data[(n, (((i - self.grid.ihi) - 1) + self.grid.ng))] | -1,690,921,127,078,352,400 | fill the boundary conditions. This operates on a single state
variable at a time, to allow for maximum flexibility
we do periodic, reflect-even, reflect-odd, and outflow
each variable name has a corresponding bc_object stored in the
ccData2d object -- we refer to this to figure out the action
to take at each boundary. | multigrid/patch1d.py | fill_BC | python-hydro/hydro_examples | python | def fill_BC(self, name):
'\n fill the boundary conditions. This operates on a single state\n variable at a time, to allow for maximum flexibility\n\n we do periodic, reflect-even, reflect-odd, and outflow\n\n each variable name has a corresponding bc_object stored in the\n ccData2d object -- we refer to this to figure out the action\n to take at each boundary.\n '
n = self.vars.index(name)
if ((self.BCs[name].xlb == 'outflow') or (self.BCs[name].xlb == 'neumann')):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, self.grid.ilo)]
elif (self.BCs[name].xlb == 'reflect-even'):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, (((2 * self.grid.ng) - i) - 1))]
elif (self.BCs[name].xlb in ['reflect-odd', 'dirichlet']):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = (- self.data[(n, (((2 * self.grid.ng) - i) - 1))])
elif (self.BCs[name].xlb == 'periodic'):
for i in range(0, self.grid.ilo):
self.data[(n, i)] = self.data[(n, (((self.grid.ihi - self.grid.ng) + i) + 1))]
if ((self.BCs[name].xrb == 'outflow') or (self.BCs[name].xrb == 'neumann')):
for i in range((self.grid.ihi + 1), (self.grid.nx + (2 * self.grid.ng))):
self.data[(n, i)] = self.data[(n, self.grid.ihi)]
elif (self.BCs[name].xrb == 'reflect-even'):
for i in range(0, self.grid.ng):
i_bnd = ((self.grid.ihi + 1) + i)
i_src = (self.grid.ihi - i)
self.data[(n, i_bnd)] = self.data[(n, i_src)]
elif (self.BCs[name].xrb in ['reflect-odd', 'dirichlet']):
for i in range(0, self.grid.ng):
i_bnd = ((self.grid.ihi + 1) + i)
i_src = (self.grid.ihi - i)
self.data[(n, i_bnd)] = (- self.data[(n, i_src)])
elif (self.BCs[name].xrb == 'periodic'):
for i in range((self.grid.ihi + 1), ((2 * self.grid.ng) + self.grid.nx)):
self.data[(n, i)] = self.data[(n, (((i - self.grid.ihi) - 1) + self.grid.ng))] |
def restrict(self, varname):
'\n restrict the variable varname to a coarser grid (factor of 2\n coarser) and return an array with the resulting data (and same\n number of ghostcells)\n '
fG = self.grid
fData = self.get_var(varname)
ng_c = fG.ng
nx_c = (fG.nx // 2)
cData = numpy.zeros(((2 * ng_c) + nx_c), dtype=self.dtype)
ilo_c = ng_c
ihi_c = ((ng_c + nx_c) - 1)
cData[ilo_c:(ihi_c + 1)] = (0.5 * (fData[fG.ilo:(fG.ihi + 1):2] + fData[(fG.ilo + 1):(fG.ihi + 1):2]))
return cData | 7,760,059,069,005,052,000 | restrict the variable varname to a coarser grid (factor of 2
coarser) and return an array with the resulting data (and same
number of ghostcells) | multigrid/patch1d.py | restrict | python-hydro/hydro_examples | python | def restrict(self, varname):
'\n restrict the variable varname to a coarser grid (factor of 2\n coarser) and return an array with the resulting data (and same\n number of ghostcells)\n '
fG = self.grid
fData = self.get_var(varname)
ng_c = fG.ng
nx_c = (fG.nx // 2)
cData = numpy.zeros(((2 * ng_c) + nx_c), dtype=self.dtype)
ilo_c = ng_c
ihi_c = ((ng_c + nx_c) - 1)
cData[ilo_c:(ihi_c + 1)] = (0.5 * (fData[fG.ilo:(fG.ihi + 1):2] + fData[(fG.ilo + 1):(fG.ihi + 1):2]))
return cData |
def prolong(self, varname):
"\n prolong the data in the current (coarse) grid to a finer\n (factor of 2 finer) grid. Return an array with the resulting\n data (and same number of ghostcells).\n\n We will reconstruct the data in the zone from the\n zone-averaged variables using the centered-difference slopes\n\n (x)\n f(x,y) = m x/dx + <f>\n\n When averaged over the parent cell, this reproduces <f>.\n\n Each zone's reconstrution will be averaged over 2 children.\n\n | | | | |\n | <f> | --> | | |\n | | | 1 | 2 |\n +-----------+ +-----+-----+\n\n We will fill each of the finer resolution zones by filling all\n the 1's together, using a stride 2 into the fine array. Then\n the 2's, this allows us to operate in a vector\n fashion. All operations will use the same slopes for their\n respective parents.\n\n "
cG = self.grid
cData = self.get_var(varname)
ng_f = cG.ng
nx_f = (cG.nx * 2)
fData = numpy.zeros(((2 * ng_f) + nx_f), dtype=self.dtype)
ilo_f = ng_f
ihi_f = ((ng_f + nx_f) - 1)
m_x = cG.scratch_array()
m_x[cG.ilo:(cG.ihi + 1)] = (0.5 * (cData[(cG.ilo + 1):(cG.ihi + 2)] - cData[(cG.ilo - 1):cG.ihi]))
fData[ilo_f:(ihi_f + 1):2] = (cData[cG.ilo:(cG.ihi + 1)] - (0.25 * m_x[cG.ilo:(cG.ihi + 1)]))
fData[(ilo_f + 1):(ihi_f + 1):2] = (cData[cG.ilo:(cG.ihi + 1)] + (0.25 * m_x[cG.ilo:(cG.ihi + 1)]))
return fData | 3,561,002,186,046,957,600 | prolong the data in the current (coarse) grid to a finer
(factor of 2 finer) grid. Return an array with the resulting
data (and same number of ghostcells).
We will reconstruct the data in the zone from the
zone-averaged variables using the centered-difference slopes
(x)
f(x,y) = m x/dx + <f>
When averaged over the parent cell, this reproduces <f>.
Each zone's reconstrution will be averaged over 2 children.
| | | | |
| <f> | --> | | |
| | | 1 | 2 |
+-----------+ +-----+-----+
We will fill each of the finer resolution zones by filling all
the 1's together, using a stride 2 into the fine array. Then
the 2's, this allows us to operate in a vector
fashion. All operations will use the same slopes for their
respective parents. | multigrid/patch1d.py | prolong | python-hydro/hydro_examples | python | def prolong(self, varname):
"\n prolong the data in the current (coarse) grid to a finer\n (factor of 2 finer) grid. Return an array with the resulting\n data (and same number of ghostcells).\n\n We will reconstruct the data in the zone from the\n zone-averaged variables using the centered-difference slopes\n\n (x)\n f(x,y) = m x/dx + <f>\n\n When averaged over the parent cell, this reproduces <f>.\n\n Each zone's reconstrution will be averaged over 2 children.\n\n | | | | |\n | <f> | --> | | |\n | | | 1 | 2 |\n +-----------+ +-----+-----+\n\n We will fill each of the finer resolution zones by filling all\n the 1's together, using a stride 2 into the fine array. Then\n the 2's, this allows us to operate in a vector\n fashion. All operations will use the same slopes for their\n respective parents.\n\n "
cG = self.grid
cData = self.get_var(varname)
ng_f = cG.ng
nx_f = (cG.nx * 2)
fData = numpy.zeros(((2 * ng_f) + nx_f), dtype=self.dtype)
ilo_f = ng_f
ihi_f = ((ng_f + nx_f) - 1)
m_x = cG.scratch_array()
m_x[cG.ilo:(cG.ihi + 1)] = (0.5 * (cData[(cG.ilo + 1):(cG.ihi + 2)] - cData[(cG.ilo - 1):cG.ihi]))
fData[ilo_f:(ihi_f + 1):2] = (cData[cG.ilo:(cG.ihi + 1)] - (0.25 * m_x[cG.ilo:(cG.ihi + 1)]))
fData[(ilo_f + 1):(ihi_f + 1):2] = (cData[cG.ilo:(cG.ihi + 1)] + (0.25 * m_x[cG.ilo:(cG.ihi + 1)]))
return fData |
def __init__(self, *, host: str='datastore.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Sequence[str]=None, channel: grpc.Channel=None, api_mtls_endpoint: str=None, client_cert_source: Callable[([], Tuple[(bytes, bytes)])]=None, ssl_channel_credentials: grpc.ChannelCredentials=None, client_cert_source_for_mtls: Callable[([], Tuple[(bytes, bytes)])]=None, quota_project_id: Optional[str]=None, client_info: gapic_v1.client_info.ClientInfo=DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool]=False) -> None:
"Instantiate the transport.\n\n Args:\n host (Optional[str]):\n The hostname to connect to.\n credentials (Optional[google.auth.credentials.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify the application to the service; if none\n are specified, the client will attempt to ascertain the\n credentials from the environment.\n This argument is ignored if ``channel`` is provided.\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is ignored if ``channel`` is provided.\n scopes (Optional(Sequence[str])): A list of scopes. This argument is\n ignored if ``channel`` is provided.\n channel (Optional[grpc.Channel]): A ``Channel`` instance through\n which to make calls.\n api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint.\n If provided, it overrides the ``host`` argument and tries to create\n a mutual TLS channel with client SSL credentials from\n ``client_cert_source`` or application default SSL credentials.\n client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]):\n Deprecated. A callback to provide client SSL certificate bytes and\n private key bytes, both in PEM format. It is ignored if\n ``api_mtls_endpoint`` is None.\n ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials\n for the grpc channel. It is ignored if ``channel`` is provided.\n client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]):\n A callback to provide client certificate bytes and private key bytes,\n both in PEM format. It is used to configure a mutual TLS channel. It is\n ignored if ``channel`` or ``ssl_channel_credentials`` is provided.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n client_info (google.api_core.gapic_v1.client_info.ClientInfo):\n The client info used to send a user-agent string along with\n API requests. If ``None``, then default info will be used.\n Generally, you only need to set this if you're developing\n your own client library.\n always_use_jwt_access (Optional[bool]): Whether self signed JWT should\n be used for service account credentials.\n\n Raises:\n google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport\n creation failed for any reason.\n google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``\n and ``credentials_file`` are passed.\n "
self._grpc_channel = None
self._ssl_channel_credentials = ssl_channel_credentials
self._stubs: Dict[(str, Callable)] = {}
if api_mtls_endpoint:
warnings.warn('api_mtls_endpoint is deprecated', DeprecationWarning)
if client_cert_source:
warnings.warn('client_cert_source is deprecated', DeprecationWarning)
if channel:
credentials = False
self._grpc_channel = channel
self._ssl_channel_credentials = None
elif api_mtls_endpoint:
host = api_mtls_endpoint
if client_cert_source:
(cert, key) = client_cert_source()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(certificate_chain=cert, private_key=key)
else:
self._ssl_channel_credentials = SslCredentials().ssl_credentials
elif (client_cert_source_for_mtls and (not ssl_channel_credentials)):
(cert, key) = client_cert_source_for_mtls()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(certificate_chain=cert, private_key=key)
super().__init__(host=host, credentials=credentials, credentials_file=credentials_file, scopes=scopes, quota_project_id=quota_project_id, client_info=client_info, always_use_jwt_access=always_use_jwt_access)
if (not self._grpc_channel):
self._grpc_channel = type(self).create_channel(self._host, credentials=self._credentials, credentials_file=credentials_file, scopes=self._scopes, ssl_credentials=self._ssl_channel_credentials, quota_project_id=quota_project_id, options=[('grpc.max_send_message_length', (- 1)), ('grpc.max_receive_message_length', (- 1))])
self._prep_wrapped_messages(client_info) | -5,410,718,539,390,989,000 | Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
This argument is ignored if ``channel`` is provided.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is ignored if ``channel`` is provided.
scopes (Optional(Sequence[str])): A list of scopes. This argument is
ignored if ``channel`` is provided.
channel (Optional[grpc.Channel]): A ``Channel`` instance through
which to make calls.
api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint.
If provided, it overrides the ``host`` argument and tries to create
a mutual TLS channel with client SSL credentials from
``client_cert_source`` or application default SSL credentials.
client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]):
Deprecated. A callback to provide client SSL certificate bytes and
private key bytes, both in PEM format. It is ignored if
``api_mtls_endpoint`` is None.
ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials
for the grpc channel. It is ignored if ``channel`` is provided.
client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]):
A callback to provide client certificate bytes and private key bytes,
both in PEM format. It is used to configure a mutual TLS channel. It is
ignored if ``channel`` or ``ssl_channel_credentials`` is provided.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
always_use_jwt_access (Optional[bool]): Whether self signed JWT should
be used for service account credentials.
Raises:
google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
creation failed for any reason.
google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``
and ``credentials_file`` are passed. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | __init__ | LaudateCorpus1/python-datastore | python | def __init__(self, *, host: str='datastore.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Sequence[str]=None, channel: grpc.Channel=None, api_mtls_endpoint: str=None, client_cert_source: Callable[([], Tuple[(bytes, bytes)])]=None, ssl_channel_credentials: grpc.ChannelCredentials=None, client_cert_source_for_mtls: Callable[([], Tuple[(bytes, bytes)])]=None, quota_project_id: Optional[str]=None, client_info: gapic_v1.client_info.ClientInfo=DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool]=False) -> None:
"Instantiate the transport.\n\n Args:\n host (Optional[str]):\n The hostname to connect to.\n credentials (Optional[google.auth.credentials.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify the application to the service; if none\n are specified, the client will attempt to ascertain the\n credentials from the environment.\n This argument is ignored if ``channel`` is provided.\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is ignored if ``channel`` is provided.\n scopes (Optional(Sequence[str])): A list of scopes. This argument is\n ignored if ``channel`` is provided.\n channel (Optional[grpc.Channel]): A ``Channel`` instance through\n which to make calls.\n api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint.\n If provided, it overrides the ``host`` argument and tries to create\n a mutual TLS channel with client SSL credentials from\n ``client_cert_source`` or application default SSL credentials.\n client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]):\n Deprecated. A callback to provide client SSL certificate bytes and\n private key bytes, both in PEM format. It is ignored if\n ``api_mtls_endpoint`` is None.\n ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials\n for the grpc channel. It is ignored if ``channel`` is provided.\n client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]):\n A callback to provide client certificate bytes and private key bytes,\n both in PEM format. It is used to configure a mutual TLS channel. It is\n ignored if ``channel`` or ``ssl_channel_credentials`` is provided.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n client_info (google.api_core.gapic_v1.client_info.ClientInfo):\n The client info used to send a user-agent string along with\n API requests. If ``None``, then default info will be used.\n Generally, you only need to set this if you're developing\n your own client library.\n always_use_jwt_access (Optional[bool]): Whether self signed JWT should\n be used for service account credentials.\n\n Raises:\n google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport\n creation failed for any reason.\n google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``\n and ``credentials_file`` are passed.\n "
self._grpc_channel = None
self._ssl_channel_credentials = ssl_channel_credentials
self._stubs: Dict[(str, Callable)] = {}
if api_mtls_endpoint:
warnings.warn('api_mtls_endpoint is deprecated', DeprecationWarning)
if client_cert_source:
warnings.warn('client_cert_source is deprecated', DeprecationWarning)
if channel:
credentials = False
self._grpc_channel = channel
self._ssl_channel_credentials = None
elif api_mtls_endpoint:
host = api_mtls_endpoint
if client_cert_source:
(cert, key) = client_cert_source()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(certificate_chain=cert, private_key=key)
else:
self._ssl_channel_credentials = SslCredentials().ssl_credentials
elif (client_cert_source_for_mtls and (not ssl_channel_credentials)):
(cert, key) = client_cert_source_for_mtls()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(certificate_chain=cert, private_key=key)
super().__init__(host=host, credentials=credentials, credentials_file=credentials_file, scopes=scopes, quota_project_id=quota_project_id, client_info=client_info, always_use_jwt_access=always_use_jwt_access)
if (not self._grpc_channel):
self._grpc_channel = type(self).create_channel(self._host, credentials=self._credentials, credentials_file=credentials_file, scopes=self._scopes, ssl_credentials=self._ssl_channel_credentials, quota_project_id=quota_project_id, options=[('grpc.max_send_message_length', (- 1)), ('grpc.max_receive_message_length', (- 1))])
self._prep_wrapped_messages(client_info) |
@classmethod
def create_channel(cls, host: str='datastore.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Optional[Sequence[str]]=None, quota_project_id: Optional[str]=None, **kwargs) -> grpc.Channel:
'Create and return a gRPC channel object.\n Args:\n host (Optional[str]): The host for the channel to use.\n credentials (Optional[~.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify this application to the service. If\n none are specified, the client will attempt to ascertain\n the credentials from the environment.\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is mutually exclusive with credentials.\n scopes (Optional[Sequence[str]]): A optional list of scopes needed for this\n service. These are only used when credentials are not specified and\n are passed to :func:`google.auth.default`.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n kwargs (Optional[dict]): Keyword arguments, which are passed to the\n channel creation.\n Returns:\n grpc.Channel: A gRPC channel object.\n\n Raises:\n google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``\n and ``credentials_file`` are passed.\n '
return grpc_helpers.create_channel(host, credentials=credentials, credentials_file=credentials_file, quota_project_id=quota_project_id, default_scopes=cls.AUTH_SCOPES, scopes=scopes, default_host=cls.DEFAULT_HOST, **kwargs) | -3,592,603,047,120,177,700 | Create and return a gRPC channel object.
Args:
host (Optional[str]): The host for the channel to use.
credentials (Optional[~.Credentials]): The
authorization credentials to attach to requests. These
credentials identify this application to the service. If
none are specified, the client will attempt to ascertain
the credentials from the environment.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is mutually exclusive with credentials.
scopes (Optional[Sequence[str]]): A optional list of scopes needed for this
service. These are only used when credentials are not specified and
are passed to :func:`google.auth.default`.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
kwargs (Optional[dict]): Keyword arguments, which are passed to the
channel creation.
Returns:
grpc.Channel: A gRPC channel object.
Raises:
google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``
and ``credentials_file`` are passed. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | create_channel | LaudateCorpus1/python-datastore | python | @classmethod
def create_channel(cls, host: str='datastore.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Optional[Sequence[str]]=None, quota_project_id: Optional[str]=None, **kwargs) -> grpc.Channel:
'Create and return a gRPC channel object.\n Args:\n host (Optional[str]): The host for the channel to use.\n credentials (Optional[~.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify this application to the service. If\n none are specified, the client will attempt to ascertain\n the credentials from the environment.\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is mutually exclusive with credentials.\n scopes (Optional[Sequence[str]]): A optional list of scopes needed for this\n service. These are only used when credentials are not specified and\n are passed to :func:`google.auth.default`.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n kwargs (Optional[dict]): Keyword arguments, which are passed to the\n channel creation.\n Returns:\n grpc.Channel: A gRPC channel object.\n\n Raises:\n google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``\n and ``credentials_file`` are passed.\n '
return grpc_helpers.create_channel(host, credentials=credentials, credentials_file=credentials_file, quota_project_id=quota_project_id, default_scopes=cls.AUTH_SCOPES, scopes=scopes, default_host=cls.DEFAULT_HOST, **kwargs) |
@property
def grpc_channel(self) -> grpc.Channel:
'Return the channel designed to connect to this service.\n '
return self._grpc_channel | -1,956,682,971,687,930,400 | Return the channel designed to connect to this service. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | grpc_channel | LaudateCorpus1/python-datastore | python | @property
def grpc_channel(self) -> grpc.Channel:
'\n '
return self._grpc_channel |
@property
def lookup(self) -> Callable[([datastore.LookupRequest], datastore.LookupResponse)]:
'Return a callable for the lookup method over gRPC.\n\n Looks up entities by key.\n\n Returns:\n Callable[[~.LookupRequest],\n ~.LookupResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('lookup' not in self._stubs):
self._stubs['lookup'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Lookup', request_serializer=datastore.LookupRequest.serialize, response_deserializer=datastore.LookupResponse.deserialize)
return self._stubs['lookup'] | 2,886,046,299,462,822,400 | Return a callable for the lookup method over gRPC.
Looks up entities by key.
Returns:
Callable[[~.LookupRequest],
~.LookupResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | lookup | LaudateCorpus1/python-datastore | python | @property
def lookup(self) -> Callable[([datastore.LookupRequest], datastore.LookupResponse)]:
'Return a callable for the lookup method over gRPC.\n\n Looks up entities by key.\n\n Returns:\n Callable[[~.LookupRequest],\n ~.LookupResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('lookup' not in self._stubs):
self._stubs['lookup'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Lookup', request_serializer=datastore.LookupRequest.serialize, response_deserializer=datastore.LookupResponse.deserialize)
return self._stubs['lookup'] |
@property
def run_query(self) -> Callable[([datastore.RunQueryRequest], datastore.RunQueryResponse)]:
'Return a callable for the run query method over gRPC.\n\n Queries for entities.\n\n Returns:\n Callable[[~.RunQueryRequest],\n ~.RunQueryResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('run_query' not in self._stubs):
self._stubs['run_query'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/RunQuery', request_serializer=datastore.RunQueryRequest.serialize, response_deserializer=datastore.RunQueryResponse.deserialize)
return self._stubs['run_query'] | -1,087,091,090,104,806,400 | Return a callable for the run query method over gRPC.
Queries for entities.
Returns:
Callable[[~.RunQueryRequest],
~.RunQueryResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | run_query | LaudateCorpus1/python-datastore | python | @property
def run_query(self) -> Callable[([datastore.RunQueryRequest], datastore.RunQueryResponse)]:
'Return a callable for the run query method over gRPC.\n\n Queries for entities.\n\n Returns:\n Callable[[~.RunQueryRequest],\n ~.RunQueryResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('run_query' not in self._stubs):
self._stubs['run_query'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/RunQuery', request_serializer=datastore.RunQueryRequest.serialize, response_deserializer=datastore.RunQueryResponse.deserialize)
return self._stubs['run_query'] |
@property
def begin_transaction(self) -> Callable[([datastore.BeginTransactionRequest], datastore.BeginTransactionResponse)]:
'Return a callable for the begin transaction method over gRPC.\n\n Begins a new transaction.\n\n Returns:\n Callable[[~.BeginTransactionRequest],\n ~.BeginTransactionResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('begin_transaction' not in self._stubs):
self._stubs['begin_transaction'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/BeginTransaction', request_serializer=datastore.BeginTransactionRequest.serialize, response_deserializer=datastore.BeginTransactionResponse.deserialize)
return self._stubs['begin_transaction'] | 6,109,222,605,587,897,000 | Return a callable for the begin transaction method over gRPC.
Begins a new transaction.
Returns:
Callable[[~.BeginTransactionRequest],
~.BeginTransactionResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | begin_transaction | LaudateCorpus1/python-datastore | python | @property
def begin_transaction(self) -> Callable[([datastore.BeginTransactionRequest], datastore.BeginTransactionResponse)]:
'Return a callable for the begin transaction method over gRPC.\n\n Begins a new transaction.\n\n Returns:\n Callable[[~.BeginTransactionRequest],\n ~.BeginTransactionResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('begin_transaction' not in self._stubs):
self._stubs['begin_transaction'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/BeginTransaction', request_serializer=datastore.BeginTransactionRequest.serialize, response_deserializer=datastore.BeginTransactionResponse.deserialize)
return self._stubs['begin_transaction'] |
@property
def commit(self) -> Callable[([datastore.CommitRequest], datastore.CommitResponse)]:
'Return a callable for the commit method over gRPC.\n\n Commits a transaction, optionally creating, deleting\n or modifying some entities.\n\n Returns:\n Callable[[~.CommitRequest],\n ~.CommitResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('commit' not in self._stubs):
self._stubs['commit'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Commit', request_serializer=datastore.CommitRequest.serialize, response_deserializer=datastore.CommitResponse.deserialize)
return self._stubs['commit'] | 4,309,621,428,806,862,000 | Return a callable for the commit method over gRPC.
Commits a transaction, optionally creating, deleting
or modifying some entities.
Returns:
Callable[[~.CommitRequest],
~.CommitResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | commit | LaudateCorpus1/python-datastore | python | @property
def commit(self) -> Callable[([datastore.CommitRequest], datastore.CommitResponse)]:
'Return a callable for the commit method over gRPC.\n\n Commits a transaction, optionally creating, deleting\n or modifying some entities.\n\n Returns:\n Callable[[~.CommitRequest],\n ~.CommitResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('commit' not in self._stubs):
self._stubs['commit'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Commit', request_serializer=datastore.CommitRequest.serialize, response_deserializer=datastore.CommitResponse.deserialize)
return self._stubs['commit'] |
@property
def rollback(self) -> Callable[([datastore.RollbackRequest], datastore.RollbackResponse)]:
'Return a callable for the rollback method over gRPC.\n\n Rolls back a transaction.\n\n Returns:\n Callable[[~.RollbackRequest],\n ~.RollbackResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('rollback' not in self._stubs):
self._stubs['rollback'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Rollback', request_serializer=datastore.RollbackRequest.serialize, response_deserializer=datastore.RollbackResponse.deserialize)
return self._stubs['rollback'] | 8,620,746,177,282,813,000 | Return a callable for the rollback method over gRPC.
Rolls back a transaction.
Returns:
Callable[[~.RollbackRequest],
~.RollbackResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | rollback | LaudateCorpus1/python-datastore | python | @property
def rollback(self) -> Callable[([datastore.RollbackRequest], datastore.RollbackResponse)]:
'Return a callable for the rollback method over gRPC.\n\n Rolls back a transaction.\n\n Returns:\n Callable[[~.RollbackRequest],\n ~.RollbackResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('rollback' not in self._stubs):
self._stubs['rollback'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/Rollback', request_serializer=datastore.RollbackRequest.serialize, response_deserializer=datastore.RollbackResponse.deserialize)
return self._stubs['rollback'] |
@property
def allocate_ids(self) -> Callable[([datastore.AllocateIdsRequest], datastore.AllocateIdsResponse)]:
'Return a callable for the allocate ids method over gRPC.\n\n Allocates IDs for the given keys, which is useful for\n referencing an entity before it is inserted.\n\n Returns:\n Callable[[~.AllocateIdsRequest],\n ~.AllocateIdsResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('allocate_ids' not in self._stubs):
self._stubs['allocate_ids'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/AllocateIds', request_serializer=datastore.AllocateIdsRequest.serialize, response_deserializer=datastore.AllocateIdsResponse.deserialize)
return self._stubs['allocate_ids'] | 4,482,291,332,690,070,500 | Return a callable for the allocate ids method over gRPC.
Allocates IDs for the given keys, which is useful for
referencing an entity before it is inserted.
Returns:
Callable[[~.AllocateIdsRequest],
~.AllocateIdsResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | allocate_ids | LaudateCorpus1/python-datastore | python | @property
def allocate_ids(self) -> Callable[([datastore.AllocateIdsRequest], datastore.AllocateIdsResponse)]:
'Return a callable for the allocate ids method over gRPC.\n\n Allocates IDs for the given keys, which is useful for\n referencing an entity before it is inserted.\n\n Returns:\n Callable[[~.AllocateIdsRequest],\n ~.AllocateIdsResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n '
if ('allocate_ids' not in self._stubs):
self._stubs['allocate_ids'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/AllocateIds', request_serializer=datastore.AllocateIdsRequest.serialize, response_deserializer=datastore.AllocateIdsResponse.deserialize)
return self._stubs['allocate_ids'] |
@property
def reserve_ids(self) -> Callable[([datastore.ReserveIdsRequest], datastore.ReserveIdsResponse)]:
"Return a callable for the reserve ids method over gRPC.\n\n Prevents the supplied keys' IDs from being auto-\n llocated by Cloud Datastore.\n\n Returns:\n Callable[[~.ReserveIdsRequest],\n ~.ReserveIdsResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n "
if ('reserve_ids' not in self._stubs):
self._stubs['reserve_ids'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/ReserveIds', request_serializer=datastore.ReserveIdsRequest.serialize, response_deserializer=datastore.ReserveIdsResponse.deserialize)
return self._stubs['reserve_ids'] | 5,990,443,636,854,899,000 | Return a callable for the reserve ids method over gRPC.
Prevents the supplied keys' IDs from being auto-
llocated by Cloud Datastore.
Returns:
Callable[[~.ReserveIdsRequest],
~.ReserveIdsResponse]:
A function that, when called, will call the underlying RPC
on the server. | google/cloud/datastore_v1/services/datastore/transports/grpc.py | reserve_ids | LaudateCorpus1/python-datastore | python | @property
def reserve_ids(self) -> Callable[([datastore.ReserveIdsRequest], datastore.ReserveIdsResponse)]:
"Return a callable for the reserve ids method over gRPC.\n\n Prevents the supplied keys' IDs from being auto-\n llocated by Cloud Datastore.\n\n Returns:\n Callable[[~.ReserveIdsRequest],\n ~.ReserveIdsResponse]:\n A function that, when called, will call the underlying RPC\n on the server.\n "
if ('reserve_ids' not in self._stubs):
self._stubs['reserve_ids'] = self.grpc_channel.unary_unary('/google.datastore.v1.Datastore/ReserveIds', request_serializer=datastore.ReserveIdsRequest.serialize, response_deserializer=datastore.ReserveIdsResponse.deserialize)
return self._stubs['reserve_ids'] |
def _get_sample(sample_info):
" Get sample from SampleService\n sample_info - dict containing 'id' and 'version' of a sample\n "
headers = {'Authorization': config()['ws_token']}
params = {'id': sample_info['id']}
if sample_info.get('version'):
params['version'] = sample_info['version']
payload = {'method': 'SampleService.get_sample', 'id': '', 'params': [params], 'version': '1.1'}
resp = requests.post(url=config()['sample_service_url'], headers=headers, data=json.dumps(payload))
if (not resp.ok):
raise RuntimeError(f'Returned from sample service with status {resp.status_code} - {resp.text}')
resp_json = resp.json()
if resp_json.get('error'):
raise RuntimeError(f"Error from SampleService - {resp_json['error']}")
sample = resp_json['result'][0]
return sample | -5,702,704,638,798,731,000 | Get sample from SampleService
sample_info - dict containing 'id' and 'version' of a sample | src/index_runner/es_indexers/sample_set.py | _get_sample | slebras/index_runner | python | def _get_sample(sample_info):
" Get sample from SampleService\n sample_info - dict containing 'id' and 'version' of a sample\n "
headers = {'Authorization': config()['ws_token']}
params = {'id': sample_info['id']}
if sample_info.get('version'):
params['version'] = sample_info['version']
payload = {'method': 'SampleService.get_sample', 'id': , 'params': [params], 'version': '1.1'}
resp = requests.post(url=config()['sample_service_url'], headers=headers, data=json.dumps(payload))
if (not resp.ok):
raise RuntimeError(f'Returned from sample service with status {resp.status_code} - {resp.text}')
resp_json = resp.json()
if resp_json.get('error'):
raise RuntimeError(f"Error from SampleService - {resp_json['error']}")
sample = resp_json['result'][0]
return sample |
def _flatten_meta(meta, prefix=None):
' Flattens metadata fields in a Sample object. Fields are concatenated into a\n single string field to save into an Elasticsearch index\n meta - Sample Metadata to be flattened\n prefix - (optional) prefix for the metadata values. default=None\n '
new_meta = {}
for key in meta:
if prefix:
val = (prefix + ':')
else:
val = ''
if ('value' in meta[key]):
val += str(meta[key]['value'])
if ('units' in meta[key]):
val += (';' + str(meta[key]['units']))
new_meta[key] = val
return new_meta | 1,436,780,520,164,271,400 | Flattens metadata fields in a Sample object. Fields are concatenated into a
single string field to save into an Elasticsearch index
meta - Sample Metadata to be flattened
prefix - (optional) prefix for the metadata values. default=None | src/index_runner/es_indexers/sample_set.py | _flatten_meta | slebras/index_runner | python | def _flatten_meta(meta, prefix=None):
' Flattens metadata fields in a Sample object. Fields are concatenated into a\n single string field to save into an Elasticsearch index\n meta - Sample Metadata to be flattened\n prefix - (optional) prefix for the metadata values. default=None\n '
new_meta = {}
for key in meta:
if prefix:
val = (prefix + ':')
else:
val =
if ('value' in meta[key]):
val += str(meta[key]['value'])
if ('units' in meta[key]):
val += (';' + str(meta[key]['units']))
new_meta[key] = val
return new_meta |
def _combine_meta(meta, flattened_meta, idx):
' Combine newly flattened metadata with existing metadata. This Function is designed to keep the indexing\n of the different metadata fields consistent for each node within the sample node tree s.t. all the\n fields in index (idx) 0 will be from item 0 in the node tree. Empty string ("") entries are Empty and\n added simply so that the indexing of all fields line up.\n meta - existing metadata.\n flattened_meta - newly flattened metadata.\n idx - current index of ndoe_tree.\n '
for key in flattened_meta:
if (key in meta):
meta[key] += (['' for _ in range((idx - len(meta[key])))] + [flattened_meta[key]])
else:
meta[key] = (['' for _ in range(idx)] + [flattened_meta[key]])
return meta | -8,150,693,350,067,738,000 | Combine newly flattened metadata with existing metadata. This Function is designed to keep the indexing
of the different metadata fields consistent for each node within the sample node tree s.t. all the
fields in index (idx) 0 will be from item 0 in the node tree. Empty string ("") entries are Empty and
added simply so that the indexing of all fields line up.
meta - existing metadata.
flattened_meta - newly flattened metadata.
idx - current index of ndoe_tree. | src/index_runner/es_indexers/sample_set.py | _combine_meta | slebras/index_runner | python | def _combine_meta(meta, flattened_meta, idx):
' Combine newly flattened metadata with existing metadata. This Function is designed to keep the indexing\n of the different metadata fields consistent for each node within the sample node tree s.t. all the\n fields in index (idx) 0 will be from item 0 in the node tree. Empty string () entries are Empty and\n added simply so that the indexing of all fields line up.\n meta - existing metadata.\n flattened_meta - newly flattened metadata.\n idx - current index of ndoe_tree.\n '
for key in flattened_meta:
if (key in meta):
meta[key] += ([ for _ in range((idx - len(meta[key])))] + [flattened_meta[key]])
else:
meta[key] = ([ for _ in range(idx)] + [flattened_meta[key]])
return meta |
def index_sample_set(obj_data, ws_info, obj_data_v1):
'Indexer for KBaseSets.SampleSet object type'
info = obj_data['info']
if (not obj_data.get('data')):
raise Exception('no data in object')
data = obj_data['data']
workspace_id = info[6]
object_id = info[0]
version = info[4]
sample_set_id = f'{_NAMESPACE}::{workspace_id}:{object_id}'
ver_sample_set_id = f'{_VER_NAMESPACE}::{workspace_id}:{object_id}:{version}'
sample_set_index = {'_action': 'index', 'doc': {'description': data['description'], 'sample_ids': [s['id'] for s in data['samples']], 'sample_names': [s['name'] for s in data['samples']], 'sample_versions': [s['version'] for s in data['samples']]}, 'index': _SAMPLE_SET_INDEX_NAME, 'id': sample_set_id}
(yield sample_set_index)
ver_sample_set_index = dict(sample_set_index)
ver_sample_set_index['index'] = _VER_SAMPLE_SET_INDEX_NAME
ver_sample_set_index['id'] = ver_sample_set_id
(yield ver_sample_set_index)
for samp in data['samples']:
sample = _get_sample(samp)
sample_id = f"{_SAMPLE_NAMESPACE}::{sample['id']}:{sample['version']}"
if (len(sample['node_tree']) == 1):
meta_controlled = _flatten_meta(sample['node_tree'][0]['meta_controlled'])
meta_user = _flatten_meta(sample['node_tree'][0]['meta_user'])
meta_controlled['node_id'] = sample['node_tree'][0]['id']
else:
(meta_controlled, meta_user) = ({}, {})
for (idx, node) in enumerate(sample['node_tree']):
meta_controlled = _combine_meta(meta_controlled, _flatten_meta(node['meta_controlled']), idx)
meta_user = _combine_meta(meta_user, _flatten_meta(node['meta_user']), idx)
meta_controlled['node_id'] = node['id']
sample_index = {'_action': 'index', 'doc': {'save_date': sample['save_date'], 'sample_version': sample['version'], 'name': sample['name'], 'parent_id': sample_set_id, **meta_user, **meta_controlled}, 'index': _SAMPLE_INDEX_NAME, 'id': sample_id}
(yield sample_index) | 4,770,756,739,175,246,000 | Indexer for KBaseSets.SampleSet object type | src/index_runner/es_indexers/sample_set.py | index_sample_set | slebras/index_runner | python | def index_sample_set(obj_data, ws_info, obj_data_v1):
info = obj_data['info']
if (not obj_data.get('data')):
raise Exception('no data in object')
data = obj_data['data']
workspace_id = info[6]
object_id = info[0]
version = info[4]
sample_set_id = f'{_NAMESPACE}::{workspace_id}:{object_id}'
ver_sample_set_id = f'{_VER_NAMESPACE}::{workspace_id}:{object_id}:{version}'
sample_set_index = {'_action': 'index', 'doc': {'description': data['description'], 'sample_ids': [s['id'] for s in data['samples']], 'sample_names': [s['name'] for s in data['samples']], 'sample_versions': [s['version'] for s in data['samples']]}, 'index': _SAMPLE_SET_INDEX_NAME, 'id': sample_set_id}
(yield sample_set_index)
ver_sample_set_index = dict(sample_set_index)
ver_sample_set_index['index'] = _VER_SAMPLE_SET_INDEX_NAME
ver_sample_set_index['id'] = ver_sample_set_id
(yield ver_sample_set_index)
for samp in data['samples']:
sample = _get_sample(samp)
sample_id = f"{_SAMPLE_NAMESPACE}::{sample['id']}:{sample['version']}"
if (len(sample['node_tree']) == 1):
meta_controlled = _flatten_meta(sample['node_tree'][0]['meta_controlled'])
meta_user = _flatten_meta(sample['node_tree'][0]['meta_user'])
meta_controlled['node_id'] = sample['node_tree'][0]['id']
else:
(meta_controlled, meta_user) = ({}, {})
for (idx, node) in enumerate(sample['node_tree']):
meta_controlled = _combine_meta(meta_controlled, _flatten_meta(node['meta_controlled']), idx)
meta_user = _combine_meta(meta_user, _flatten_meta(node['meta_user']), idx)
meta_controlled['node_id'] = node['id']
sample_index = {'_action': 'index', 'doc': {'save_date': sample['save_date'], 'sample_version': sample['version'], 'name': sample['name'], 'parent_id': sample_set_id, **meta_user, **meta_controlled}, 'index': _SAMPLE_INDEX_NAME, 'id': sample_id}
(yield sample_index) |
def loginValid(func):
'\n :desc 闭包函数校验是否登录\n :param func:\n :return:\n '
def inner(request, *args, **kwargs):
email = request.COOKIES.get('user')
s_email = request.session.get('user')
if (email and s_email and (email == s_email)):
user = LoginUser.objects.filter(email=email).first()
if user:
return func(request, *args, **kwargs)
return HttpResponseRedirect('/Buyer/login/')
return inner | 4,984,156,748,269,809,000 | :desc 闭包函数校验是否登录
:param func:
:return: | Qshop/Buyer/views.py | loginValid | songdanlee/DjangoWorkSpace | python | def loginValid(func):
'\n :desc 闭包函数校验是否登录\n :param func:\n :return:\n '
def inner(request, *args, **kwargs):
email = request.COOKIES.get('user')
s_email = request.session.get('user')
if (email and s_email and (email == s_email)):
user = LoginUser.objects.filter(email=email).first()
if user:
return func(request, *args, **kwargs)
return HttpResponseRedirect('/Buyer/login/')
return inner |
@loginValid
def pay_order(request):
'\n get请求 商品详情页购买单个商品。传入商品id，数量。\n post请求 购物车购买多个商品。\n '
if (request.method == 'GET'):
num = request.GET.get('num')
id = request.GET.get('id')
if (num and id):
num = int(num)
id = int(id)
order = PayOrder()
order.order_number = str(time.time()).replace('.', '')
order.order_date = datetime.datetime.now()
order.order_user = LoginUser.objects.get(id=int(request.COOKIES.get('user_id')))
order.save()
good = Goods.objects.get(id=id)
order_info = OrderInfo()
order_info.order_id = order
order_info.goods_id = good.id
order_info.goods_picture = good.goods_picture
order_info.goods_name = good.goods_name
order_info.goods_count = num
order_info.goods_price = good.goods_price
order_info.goods_total_price = round((good.goods_price * num), 3)
order_info.store_id = good.goods_store
order_info.order_status = 0
order_info.save()
order.order_total = order_info.goods_total_price
order.save()
elif (request.method == 'POST'):
request_data = []
data = request.POST
data_item = request.POST.items()
for (key, value) in data_item:
if key.startswith('check_'):
id = int(key.split('_', 1)[1])
num = int(data.get(('count_' + str(id))))
request_data.append((id, num))
if request_data:
order = PayOrder()
order.order_number = str(time.time()).replace('.', '')
order.order_date = datetime.datetime.now()
order.order_user = LoginUser.objects.get(id=int(request.COOKIES.get('user_id')))
order.order_total = 0.0
order.goods_number = 0
order.save()
for (id, num) in request_data:
good = Goods.objects.get(id=id)
order_info = OrderInfo()
order_info.order_id = order
order_info.goods_id = good.id
order_info.goods_picture = good.goods_picture
order_info.goods_name = good.goods_name
order_info.goods_count = num
order_info.goods_price = good.goods_price
order_info.goods_total_price = round((good.goods_price * num), 3)
order_info.store_id = good.goods_store
order_info.order_status = 0
order_info.save()
order.order_total += order_info.goods_total_price
order.goods_number += 1
order.save()
return render(request, 'buyer/place_order.html', locals()) | 9,212,065,408,716,652,000 | get请求 商品详情页购买单个商品。传入商品id，数量。
post请求 购物车购买多个商品。 | Qshop/Buyer/views.py | pay_order | songdanlee/DjangoWorkSpace | python | @loginValid
def pay_order(request):
'\n get请求 商品详情页购买单个商品。传入商品id，数量。\n post请求 购物车购买多个商品。\n '
if (request.method == 'GET'):
num = request.GET.get('num')
id = request.GET.get('id')
if (num and id):
num = int(num)
id = int(id)
order = PayOrder()
order.order_number = str(time.time()).replace('.', )
order.order_date = datetime.datetime.now()
order.order_user = LoginUser.objects.get(id=int(request.COOKIES.get('user_id')))
order.save()
good = Goods.objects.get(id=id)
order_info = OrderInfo()
order_info.order_id = order
order_info.goods_id = good.id
order_info.goods_picture = good.goods_picture
order_info.goods_name = good.goods_name
order_info.goods_count = num
order_info.goods_price = good.goods_price
order_info.goods_total_price = round((good.goods_price * num), 3)
order_info.store_id = good.goods_store
order_info.order_status = 0
order_info.save()
order.order_total = order_info.goods_total_price
order.save()
elif (request.method == 'POST'):
request_data = []
data = request.POST
data_item = request.POST.items()
for (key, value) in data_item:
if key.startswith('check_'):
id = int(key.split('_', 1)[1])
num = int(data.get(('count_' + str(id))))
request_data.append((id, num))
if request_data:
order = PayOrder()
order.order_number = str(time.time()).replace('.', )
order.order_date = datetime.datetime.now()
order.order_user = LoginUser.objects.get(id=int(request.COOKIES.get('user_id')))
order.order_total = 0.0
order.goods_number = 0
order.save()
for (id, num) in request_data:
good = Goods.objects.get(id=id)
order_info = OrderInfo()
order_info.order_id = order
order_info.goods_id = good.id
order_info.goods_picture = good.goods_picture
order_info.goods_name = good.goods_name
order_info.goods_count = num
order_info.goods_price = good.goods_price
order_info.goods_total_price = round((good.goods_price * num), 3)
order_info.store_id = good.goods_store
order_info.order_status = 0
order_info.save()
order.order_total += order_info.goods_total_price
order.goods_number += 1
order.save()
return render(request, 'buyer/place_order.html', locals()) |
@loginValid
def alipayOrder(request):
'\n 阿里支付，传入交易订单号，总金额\n '
order_number = request.GET.get('order_number')
total = request.GET.get('total')
alipay = AliPay(appid='2016101200667714', app_notify_url=None, app_private_key_string=alipay_private_key_string, alipay_public_key_string=alipay_public_key_string, sign_type='RSA2')
order_string = alipay.api_alipay_trade_page_pay(out_trade_no=order_number, total_amount=str(total), subject='生鲜交易', return_url='http://127.0.0.1:8000/Buyer/pay_result/', notify_url='http://127.0.0.1:8000/Buyer/pay_result/')
result = ('https://openapi.alipaydev.com/gateway.do?' + order_string)
return HttpResponseRedirect(result) | -6,322,852,548,281,581,000 | 阿里支付，传入交易订单号，总金额 | Qshop/Buyer/views.py | alipayOrder | songdanlee/DjangoWorkSpace | python | @loginValid
def alipayOrder(request):
'\n \n '
order_number = request.GET.get('order_number')
total = request.GET.get('total')
alipay = AliPay(appid='2016101200667714', app_notify_url=None, app_private_key_string=alipay_private_key_string, alipay_public_key_string=alipay_public_key_string, sign_type='RSA2')
order_string = alipay.api_alipay_trade_page_pay(out_trade_no=order_number, total_amount=str(total), subject='生鲜交易', return_url='http://127.0.0.1:8000/Buyer/pay_result/', notify_url='http://127.0.0.1:8000/Buyer/pay_result/')
result = ('https://openapi.alipaydev.com/gateway.do?' + order_string)
return HttpResponseRedirect(result) |
@loginValid
def pay_result(request):
'\n 支付结果页\n 如果有out_trade_no，支付成功，修改订单状态\n '
out_trade_no = request.GET.get('out_trade_no')
if out_trade_no:
payorder = PayOrder.objects.get(order_number=out_trade_no)
payorder.orderinfo_set.all().update(order_status=1)
return render(request, 'buyer/pay_result.html', locals()) | -7,832,803,620,661,841,000 | 支付结果页
如果有out_trade_no，支付成功，修改订单状态 | Qshop/Buyer/views.py | pay_result | songdanlee/DjangoWorkSpace | python | @loginValid
def pay_result(request):
'\n 支付结果页\n 如果有out_trade_no，支付成功，修改订单状态\n '
out_trade_no = request.GET.get('out_trade_no')
if out_trade_no:
payorder = PayOrder.objects.get(order_number=out_trade_no)
payorder.orderinfo_set.all().update(order_status=1)
return render(request, 'buyer/pay_result.html', locals()) |
@loginValid
def add_cart(request):
'\n 处理ajax 请求，添加商品到购物车 ，成功保存到数据库。\n 传入商品id，数量\n '
sendData = {'code': 200, 'data': ''}
if (request.method == 'POST'):
id = int(request.POST.get('goods_id'))
count = int(request.POST.get('count', 1))
goods = Goods.objects.get(id=id)
cart = Cart()
cart.goods_name = goods.goods_name
cart.goods_num = count
cart.goods_price = goods.goods_price
cart.goods_picture = goods.goods_picture
cart.goods_total = round((goods.goods_price * count), 3)
cart.goods_id = goods.id
cart.cart_user = request.COOKIES.get('user_id')
cart.save()
sendData['data'] = '加入购物车成功'
else:
sendData['code'] = 500
sendData['data'] = '请求方式错误'
return JsonResponse(sendData) | 348,866,261,690,652,700 | 处理ajax 请求，添加商品到购物车 ，成功保存到数据库。
传入商品id，数量 | Qshop/Buyer/views.py | add_cart | songdanlee/DjangoWorkSpace | python | @loginValid
def add_cart(request):
'\n 处理ajax 请求，添加商品到购物车 ，成功保存到数据库。\n 传入商品id，数量\n '
sendData = {'code': 200, 'data': }
if (request.method == 'POST'):
id = int(request.POST.get('goods_id'))
count = int(request.POST.get('count', 1))
goods = Goods.objects.get(id=id)
cart = Cart()
cart.goods_name = goods.goods_name
cart.goods_num = count
cart.goods_price = goods.goods_price
cart.goods_picture = goods.goods_picture
cart.goods_total = round((goods.goods_price * count), 3)
cart.goods_id = goods.id
cart.cart_user = request.COOKIES.get('user_id')
cart.save()
sendData['data'] = '加入购物车成功'
else:
sendData['code'] = 500
sendData['data'] = '请求方式错误'
return JsonResponse(sendData) |
@databench.on
def run(self):
'Run when button is pressed.'
inside = 0
for draws in range(1, self.data['samples']):
r1 = random.random()
r2 = random.random()
if (((r1 ** 2) + (r2 ** 2)) < 1.0):
inside += 1
if ((draws % 1000) != 0):
continue
(yield self.emit('log', {'draws': draws, 'inside': inside}))
p = (inside / draws)
pi = {'estimate': (4.0 * p), 'uncertainty': ((4.0 * math.sqrt(((draws * p) * (1.0 - p)))) / draws)}
(yield self.set_state(pi=pi))
(yield self.emit('log', {'action': 'done'})) | -4,587,078,849,969,560,000 | Run when button is pressed. | databench/analyses_packaged/dummypi/analysis.py | run | phillipaug/Data-Analysis-General-repository | python | @databench.on
def run(self):
inside = 0
for draws in range(1, self.data['samples']):
r1 = random.random()
r2 = random.random()
if (((r1 ** 2) + (r2 ** 2)) < 1.0):
inside += 1
if ((draws % 1000) != 0):
continue
(yield self.emit('log', {'draws': draws, 'inside': inside}))
p = (inside / draws)
pi = {'estimate': (4.0 * p), 'uncertainty': ((4.0 * math.sqrt(((draws * p) * (1.0 - p)))) / draws)}
(yield self.set_state(pi=pi))
(yield self.emit('log', {'action': 'done'})) |
def generate_ui_test_task(dependencies, engine='Klar', device='ARM'):
'\n :param str engine: Klar, Webview\n :param str device: ARM, X86\n :return: uiWebviewARMTestTaskId, uiWebviewARMTestTask\n '
if (engine is 'Klar'):
engine = 'geckoview'
assemble_engine = engine
elif (engine is 'Webview'):
engine = 'webview'
assemble_engine = 'Focus'
else:
raise Exception('ERROR: unknown engine type --> Aborting!')
task_name = '(Focus for Android) UI tests - {0} {1}'.format(engine, device)
task_description = 'Run UI tests for {0} {1} build for Android.'.format(engine, device)
build_dir = 'assemble{0}{1}Debug'.format(assemble_engine, device.capitalize())
build_dir_test = 'assemble{0}{1}DebugAndroidTest'.format(assemble_engine, device.capitalize())
print('BUILD_DIR: {0}'.format(build_dir))
print('BUILD_DIR_TEST: {0}'.format(build_dir_test))
device = device.lower()
return (taskcluster.slugId(), generate_task(name=task_name, description=task_description, command=((((((('echo "--" > .adjust_token && ./gradlew --no-daemon clean ' + build_dir) + ' ') + build_dir_test) + ' && ./tools/taskcluster/google-firebase-testlab-login.sh && tools/taskcluster/execute-firebase-tests.sh ') + device) + ' ') + engine), dependencies=dependencies, scopes=['secrets:get:project/focus/firebase'], routes=['notify.irc-channel.#android-ci.on-any'], artifacts={'public': {'type': 'directory', 'path': '/opt/focus-android/test_artifacts', 'expires': taskcluster.stringDate(taskcluster.fromNow('1 week'))}})) | 7,921,102,924,883,363,000 | :param str engine: Klar, Webview
:param str device: ARM, X86
:return: uiWebviewARMTestTaskId, uiWebviewARMTestTask | tools/taskcluster/schedule-master-build.py | generate_ui_test_task | kglazko/focus-android | python | def generate_ui_test_task(dependencies, engine='Klar', device='ARM'):
'\n :param str engine: Klar, Webview\n :param str device: ARM, X86\n :return: uiWebviewARMTestTaskId, uiWebviewARMTestTask\n '
if (engine is 'Klar'):
engine = 'geckoview'
assemble_engine = engine
elif (engine is 'Webview'):
engine = 'webview'
assemble_engine = 'Focus'
else:
raise Exception('ERROR: unknown engine type --> Aborting!')
task_name = '(Focus for Android) UI tests - {0} {1}'.format(engine, device)
task_description = 'Run UI tests for {0} {1} build for Android.'.format(engine, device)
build_dir = 'assemble{0}{1}Debug'.format(assemble_engine, device.capitalize())
build_dir_test = 'assemble{0}{1}DebugAndroidTest'.format(assemble_engine, device.capitalize())
print('BUILD_DIR: {0}'.format(build_dir))
print('BUILD_DIR_TEST: {0}'.format(build_dir_test))
device = device.lower()
return (taskcluster.slugId(), generate_task(name=task_name, description=task_description, command=((((((('echo "--" > .adjust_token && ./gradlew --no-daemon clean ' + build_dir) + ' ') + build_dir_test) + ' && ./tools/taskcluster/google-firebase-testlab-login.sh && tools/taskcluster/execute-firebase-tests.sh ') + device) + ' ') + engine), dependencies=dependencies, scopes=['secrets:get:project/focus/firebase'], routes=['notify.irc-channel.#android-ci.on-any'], artifacts={'public': {'type': 'directory', 'path': '/opt/focus-android/test_artifacts', 'expires': taskcluster.stringDate(taskcluster.fromNow('1 week'))}})) |
def test_identity_expectation(self, device, shots, tol):
'Test that identity expectation value (i.e. the trace) is 1'
theta = 0.432
phi = 0.123
dev = device(2)
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])])
O = qml.Identity
name = 'Identity'
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([1, 1]), **tol) | 7,841,059,412,821,732,000 | Test that identity expectation value (i.e. the trace) is 1 | tests/test_expval.py | test_identity_expectation | wongwsvincent/pennylane-cirq | python | def test_identity_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])])
O = qml.Identity
name = 'Identity'
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([1, 1]), **tol) |
def test_pauliz_expectation(self, device, shots, tol):
'Test that PauliZ expectation value is correct'
theta = 0.432
phi = 0.123
dev = device(2)
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])])
O = qml.PauliZ
name = 'PauliZ'
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([np.cos(theta), (np.cos(theta) * np.cos(phi))]), **tol) | 3,565,328,443,008,282,600 | Test that PauliZ expectation value is correct | tests/test_expval.py | test_pauliz_expectation | wongwsvincent/pennylane-cirq | python | def test_pauliz_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])])
O = qml.PauliZ
name = 'PauliZ'
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([np.cos(theta), (np.cos(theta) * np.cos(phi))]), **tol) |
def test_paulix_expectation(self, device, shots, tol):
'Test that PauliX expectation value is correct'
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.PauliX
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([(np.sin(theta) * np.sin(phi)), np.sin(phi)]), **tol) | 5,108,788,294,049,689,000 | Test that PauliX expectation value is correct | tests/test_expval.py | test_paulix_expectation | wongwsvincent/pennylane-cirq | python | def test_paulix_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.PauliX
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([(np.sin(theta) * np.sin(phi)), np.sin(phi)]), **tol) |
def test_pauliy_expectation(self, device, shots, tol):
'Test that PauliY expectation value is correct'
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.PauliY
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([0, ((- np.cos(theta)) * np.sin(phi))]), **tol) | 4,112,659,539,980,843,500 | Test that PauliY expectation value is correct | tests/test_expval.py | test_pauliy_expectation | wongwsvincent/pennylane-cirq | python | def test_pauliy_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.PauliY
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
assert np.allclose(res, np.array([0, ((- np.cos(theta)) * np.sin(phi))]), **tol) |
def test_hadamard_expectation(self, device, shots, tol):
'Test that Hadamard expectation value is correct'
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hadamard
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
expected = (np.array([((np.sin(theta) * np.sin(phi)) + np.cos(theta)), ((np.cos(theta) * np.cos(phi)) + np.sin(phi))]) / np.sqrt(2))
assert np.allclose(res, expected, **tol) | 4,595,716,068,264,793,600 | Test that Hadamard expectation value is correct | tests/test_expval.py | test_hadamard_expectation | wongwsvincent/pennylane-cirq | python | def test_hadamard_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hadamard
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(wires=[0], do_queue=False).diagonalizing_gates() + O(wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = np.array([dev.expval(O(wires=[0], do_queue=False)), dev.expval(O(wires=[1], do_queue=False))])
expected = (np.array([((np.sin(theta) * np.sin(phi)) + np.cos(theta)), ((np.cos(theta) * np.cos(phi)) + np.sin(phi))]) / np.sqrt(2))
assert np.allclose(res, expected, **tol) |
def test_hermitian_expectation(self, device, shots, tol):
'Test that arbitrary Hermitian expectation values are correct'
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hermitian
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(A, wires=[0], do_queue=False).diagonalizing_gates() + O(A, wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(A, wires=[0], do_queue=False), O(A, wires=[1], do_queue=False)]
res = np.array([dev.expval(O(A, wires=[0], do_queue=False)), dev.expval(O(A, wires=[1], do_queue=False))])
a = A[(0, 0)]
re_b = A[(0, 1)].real
d = A[(1, 1)]
ev1 = ((((((a - d) * np.cos(theta)) + (((2 * re_b) * np.sin(theta)) * np.sin(phi))) + a) + d) / 2)
ev2 = (((((((a - d) * np.cos(theta)) * np.cos(phi)) + ((2 * re_b) * np.sin(phi))) + a) + d) / 2)
expected = np.array([ev1, ev2])
assert np.allclose(res, expected, **tol) | 6,151,081,162,952,617,000 | Test that arbitrary Hermitian expectation values are correct | tests/test_expval.py | test_hermitian_expectation | wongwsvincent/pennylane-cirq | python | def test_hermitian_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hermitian
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=(O(A, wires=[0], do_queue=False).diagonalizing_gates() + O(A, wires=[1], do_queue=False).diagonalizing_gates()))
dev._obs_queue = [O(A, wires=[0], do_queue=False), O(A, wires=[1], do_queue=False)]
res = np.array([dev.expval(O(A, wires=[0], do_queue=False)), dev.expval(O(A, wires=[1], do_queue=False))])
a = A[(0, 0)]
re_b = A[(0, 1)].real
d = A[(1, 1)]
ev1 = ((((((a - d) * np.cos(theta)) + (((2 * re_b) * np.sin(theta)) * np.sin(phi))) + a) + d) / 2)
ev2 = (((((((a - d) * np.cos(theta)) * np.cos(phi)) + ((2 * re_b) * np.sin(phi))) + a) + d) / 2)
expected = np.array([ev1, ev2])
assert np.allclose(res, expected, **tol) |
def test_multi_mode_hermitian_expectation(self, device, shots, tol):
'Test that arbitrary multi-mode Hermitian expectation values are correct'
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hermitian
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=O(B, wires=[0, 1], do_queue=False).diagonalizing_gates())
dev._obs_queue = [O(B, wires=[0, 1], do_queue=False)]
res = np.array([dev.expval(O(B, wires=[0, 1], do_queue=False))])
expected = (0.5 * ((((((6 * np.cos(theta)) * np.sin(phi)) - (np.sin(theta) * (((8 * np.sin(phi)) + (7 * np.cos(phi))) + 3))) - (2 * np.sin(phi))) - (6 * np.cos(phi))) - 6))
assert np.allclose(res, expected, **tol) | 2,659,470,803,272,884,700 | Test that arbitrary multi-mode Hermitian expectation values are correct | tests/test_expval.py | test_multi_mode_hermitian_expectation | wongwsvincent/pennylane-cirq | python | def test_multi_mode_hermitian_expectation(self, device, shots, tol):
theta = 0.432
phi = 0.123
dev = device(2)
O = qml.Hermitian
with mimic_execution_for_expval(dev):
dev.apply([qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], rotations=O(B, wires=[0, 1], do_queue=False).diagonalizing_gates())
dev._obs_queue = [O(B, wires=[0, 1], do_queue=False)]
res = np.array([dev.expval(O(B, wires=[0, 1], do_queue=False))])
expected = (0.5 * ((((((6 * np.cos(theta)) * np.sin(phi)) - (np.sin(theta) * (((8 * np.sin(phi)) + (7 * np.cos(phi))) + 3))) - (2 * np.sin(phi))) - (6 * np.cos(phi))) - 6))
assert np.allclose(res, expected, **tol) |
def test_paulix_pauliy(self, device, shots, tol):
'Test that a tensor product involving PauliX and PauliY works correctly'
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
obs = (qml.PauliX(wires=[0], do_queue=False) @ qml.PauliY(wires=[2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = ((np.sin(theta) * np.sin(phi)) * np.sin(varphi))
assert np.allclose(res, expected, **tol) | 3,162,494,482,914,384,000 | Test that a tensor product involving PauliX and PauliY works correctly | tests/test_expval.py | test_paulix_pauliy | wongwsvincent/pennylane-cirq | python | def test_paulix_pauliy(self, device, shots, tol):
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
obs = (qml.PauliX(wires=[0], do_queue=False) @ qml.PauliY(wires=[2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = ((np.sin(theta) * np.sin(phi)) * np.sin(varphi))
assert np.allclose(res, expected, **tol) |
def test_pauliz_hadamard(self, device, shots, tol):
'Test that a tensor product involving PauliZ and PauliY and hadamard works correctly'
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
obs = ((qml.PauliZ(wires=[0], do_queue=False) @ qml.Hadamard(wires=[1], do_queue=False)) @ qml.PauliY(wires=[2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = ((- ((np.cos(varphi) * np.sin(phi)) + (np.sin(varphi) * np.cos(theta)))) / np.sqrt(2))
assert np.allclose(res, expected, **tol) | -1,377,457,128,183,673,600 | Test that a tensor product involving PauliZ and PauliY and hadamard works correctly | tests/test_expval.py | test_pauliz_hadamard | wongwsvincent/pennylane-cirq | python | def test_pauliz_hadamard(self, device, shots, tol):
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
obs = ((qml.PauliZ(wires=[0], do_queue=False) @ qml.Hadamard(wires=[1], do_queue=False)) @ qml.PauliY(wires=[2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = ((- ((np.cos(varphi) * np.sin(phi)) + (np.sin(varphi) * np.cos(theta)))) / np.sqrt(2))
assert np.allclose(res, expected, **tol) |
def test_hermitian(self, device, shots, tol):
'Test that a tensor product involving qml.Hermitian works correctly'
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
A = np.array([[(- 6), (2 + 1j), (- 3), ((- 5) + 2j)], [(2 - 1j), 0, (2 - 1j), ((- 5) + 4j)], [(- 3), (2 + 1j), 0, ((- 4) + 3j)], [((- 5) - 2j), ((- 5) - 4j), ((- 4) - 3j), (- 6)]])
obs = (qml.PauliZ(wires=[0], do_queue=False) @ qml.Hermitian(A, wires=[1, 2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = (0.5 * ((((((- 6) * np.cos(theta)) * (np.cos(varphi) + 1)) - ((2 * np.sin(varphi)) * ((np.cos(theta) + np.sin(phi)) - (2 * np.cos(phi))))) + ((3 * np.cos(varphi)) * np.sin(phi))) + np.sin(phi)))
assert np.allclose(res, expected, **tol) | 2,487,172,467,202,695,000 | Test that a tensor product involving qml.Hermitian works correctly | tests/test_expval.py | test_hermitian | wongwsvincent/pennylane-cirq | python | def test_hermitian(self, device, shots, tol):
theta = 0.432
phi = 0.123
varphi = (- 0.543)
dev = device(3)
A = np.array([[(- 6), (2 + 1j), (- 3), ((- 5) + 2j)], [(2 - 1j), 0, (2 - 1j), ((- 5) + 4j)], [(- 3), (2 + 1j), 0, ((- 4) + 3j)], [((- 5) - 2j), ((- 5) - 4j), ((- 4) - 3j), (- 6)]])
obs = (qml.PauliZ(wires=[0], do_queue=False) @ qml.Hermitian(A, wires=[1, 2], do_queue=False))
with mimic_execution_for_expval(dev):
dev.apply([qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.RX(varphi, wires=[2]), qml.CNOT(wires=[0, 1]), qml.CNOT(wires=[1, 2])], rotations=obs.diagonalizing_gates())
res = dev.expval(obs)
expected = (0.5 * ((((((- 6) * np.cos(theta)) * (np.cos(varphi) + 1)) - ((2 * np.sin(varphi)) * ((np.cos(theta) + np.sin(phi)) - (2 * np.cos(phi))))) + ((3 * np.cos(varphi)) * np.sin(phi))) + np.sin(phi)))
assert np.allclose(res, expected, **tol) |
def main(sys_argv: typing.List[str]) -> None:
'PV simulator execution entry point.\n\n Parameters\n ----------\n sys_argv : list\n contains the list of arguments passed to the CLI during its execution. The first argument contains the\n executed script name.\n '
main_logger: typing.Optional[logging.Logger] = None
try:
must_exit_after_24h = os.getenv('MUST_EXIT_AFTER_24H', '0')
must_exit_after_24h = (True if (must_exit_after_24h.isdecimal() and (int(must_exit_after_24h) == 1)) else False)
main_logger = utils.initialize_loggers(current_dir_path)
main_loop: MainLoop = MainLoop(constants.LOGGER_NAME, constants.RESULTS_LOGGER_NAME, current_dir_path, must_exit_after_24h, get_mq_receiver, get_pv_power_value_calculator, tests_modules_names_provider=get_test_modules_names)
main_loop.handle_arguments(sys_argv)
except KeyboardInterrupt:
if (main_logger is not None):
main_logger.exception('Required to abort:')
else:
import traceback
traceback.print_exc()
except Exception:
if (main_logger is not None):
main_logger.exception('Error:')
else:
import traceback
traceback.print_exc() | 795,831,813,431,906,000 | PV simulator execution entry point.
Parameters
----------
sys_argv : list
contains the list of arguments passed to the CLI during its execution. The first argument contains the
executed script name. | services/pv_simulator/main.py | main | reynierg/pv_simulator_challenge | python | def main(sys_argv: typing.List[str]) -> None:
'PV simulator execution entry point.\n\n Parameters\n ----------\n sys_argv : list\n contains the list of arguments passed to the CLI during its execution. The first argument contains the\n executed script name.\n '
main_logger: typing.Optional[logging.Logger] = None
try:
must_exit_after_24h = os.getenv('MUST_EXIT_AFTER_24H', '0')
must_exit_after_24h = (True if (must_exit_after_24h.isdecimal() and (int(must_exit_after_24h) == 1)) else False)
main_logger = utils.initialize_loggers(current_dir_path)
main_loop: MainLoop = MainLoop(constants.LOGGER_NAME, constants.RESULTS_LOGGER_NAME, current_dir_path, must_exit_after_24h, get_mq_receiver, get_pv_power_value_calculator, tests_modules_names_provider=get_test_modules_names)
main_loop.handle_arguments(sys_argv)
except KeyboardInterrupt:
if (main_logger is not None):
main_logger.exception('Required to abort:')
else:
import traceback
traceback.print_exc()
except Exception:
if (main_logger is not None):
main_logger.exception('Error:')
else:
import traceback
traceback.print_exc() |
def render_message_template(message_template: List[dict], **kwargs):
'Renders the jinja data included in the template itself.'
data = []
new_copy = copy.deepcopy(message_template)
for d in new_copy:
if d.get('status_mapping'):
d['text'] = d['status_mapping'][kwargs['status']]
if d.get('datetime'):
d['datetime'] = Template(d['datetime']).render(**kwargs)
d['text'] = Template(d['text']).render(**kwargs)
d['title'] = Template(d['title']).render(**kwargs)
if d.get('title_link'):
d['title_link'] = Template(d['title_link']).render(**kwargs)
if (d['title_link'] == 'None'):
continue
if (not d['title_link']):
continue
if d.get('button_text'):
d['button_text'] = Template(d['button_text']).render(**kwargs)
if d.get('button_value'):
d['button_value'] = Template(d['button_value']).render(**kwargs)
data.append(d)
return data | 6,656,896,164,718,373,000 | Renders the jinja data included in the template itself. | src/dispatch/messaging.py | render_message_template | oliverzgy/dispatch | python | def render_message_template(message_template: List[dict], **kwargs):
data = []
new_copy = copy.deepcopy(message_template)
for d in new_copy:
if d.get('status_mapping'):
d['text'] = d['status_mapping'][kwargs['status']]
if d.get('datetime'):
d['datetime'] = Template(d['datetime']).render(**kwargs)
d['text'] = Template(d['text']).render(**kwargs)
d['title'] = Template(d['title']).render(**kwargs)
if d.get('title_link'):
d['title_link'] = Template(d['title_link']).render(**kwargs)
if (d['title_link'] == 'None'):
continue
if (not d['title_link']):
continue
if d.get('button_text'):
d['button_text'] = Template(d['button_text']).render(**kwargs)
if d.get('button_value'):
d['button_value'] = Template(d['button_value']).render(**kwargs)
data.append(d)
return data |
def __init__(self, params=None):
'Initialization method.\n\n Args:\n params (dict): Contains key-value parameters to the meta-heuristics.\n\n '
logger.info('Overriding class: Optimizer -> QSA.')
super(QSA, self).__init__()
self.build(params)
logger.info('Class overrided.') | -3,367,765,822,090,985,000 | Initialization method.
Args:
params (dict): Contains key-value parameters to the meta-heuristics. | opytimizer/optimizers/social/qsa.py | __init__ | anukaal/opytimizer | python | def __init__(self, params=None):
'Initialization method.\n\n Args:\n params (dict): Contains key-value parameters to the meta-heuristics.\n\n '
logger.info('Overriding class: Optimizer -> QSA.')
super(QSA, self).__init__()
self.build(params)
logger.info('Class overrided.') |
def _calculate_queue(self, n_agents, t_1, t_2, t_3):
'Calculates the number of agents that belongs to each queue.\n\n Args:\n n_agents (int): Number of agents.\n t_1 (float): Fitness value of first agent in the population.\n t_2 (float): Fitness value of second agent in the population.\n t_3 (float): Fitness value of third agent in the population.\n\n Returns:\n The number of agents in first, second and third queues.\n\n '
if (t_1 > c.EPSILON):
n_1 = ((1 / t_1) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
n_2 = ((1 / t_2) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
n_3 = ((1 / t_3) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
else:
n_1 = (1 / 3)
n_2 = (1 / 3)
n_3 = (1 / 3)
q_1 = int((n_1 * n_agents))
q_2 = int((n_2 * n_agents))
q_3 = int((n_3 * n_agents))
return (q_1, q_2, q_3) | 2,714,208,289,195,568,000 | Calculates the number of agents that belongs to each queue.
Args:
n_agents (int): Number of agents.
t_1 (float): Fitness value of first agent in the population.
t_2 (float): Fitness value of second agent in the population.
t_3 (float): Fitness value of third agent in the population.
Returns:
The number of agents in first, second and third queues. | opytimizer/optimizers/social/qsa.py | _calculate_queue | anukaal/opytimizer | python | def _calculate_queue(self, n_agents, t_1, t_2, t_3):
'Calculates the number of agents that belongs to each queue.\n\n Args:\n n_agents (int): Number of agents.\n t_1 (float): Fitness value of first agent in the population.\n t_2 (float): Fitness value of second agent in the population.\n t_3 (float): Fitness value of third agent in the population.\n\n Returns:\n The number of agents in first, second and third queues.\n\n '
if (t_1 > c.EPSILON):
n_1 = ((1 / t_1) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
n_2 = ((1 / t_2) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
n_3 = ((1 / t_3) / (((1 / t_1) + (1 / t_2)) + (1 / t_3)))
else:
n_1 = (1 / 3)
n_2 = (1 / 3)
n_3 = (1 / 3)
q_1 = int((n_1 * n_agents))
q_2 = int((n_2 * n_agents))
q_3 = int((n_3 * n_agents))
return (q_1, q_2, q_3) |
def _business_one(self, agents, function, beta):
'Performs the first business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n beta (float): Range of fluctuation.\n\n '
agents.sort(key=(lambda x: x.fit))
(A_1, A_2, A_3) = (copy.deepcopy(agents[0]), copy.deepcopy(agents[1]), copy.deepcopy(agents[2]))
(q_1, q_2, _) = self._calculate_queue(len(agents), A_1.fit, A_2.fit, A_3.fit)
case = None
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
if (i < q_1):
if (i == 0):
case = 1
A = copy.deepcopy(A_1)
elif (q_1 <= i < (q_1 + q_2)):
if (i == q_1):
case = 1
A = copy.deepcopy(A_2)
else:
if (i == (q_1 + q_2)):
case = 1
A = copy.deepcopy(A_3)
alpha = r.generate_uniform_random_number((- 1), 1)
E = r.generate_gamma_random_number(1, 0.5, (agent.n_variables, agent.n_dimensions))
if (case == 1):
e = r.generate_gamma_random_number(1, 0.5, 1)
F_1 = (((beta * alpha) * (E * np.fabs((A.position - a.position)))) + (e * (A.position - a.position)))
a.position = (A.position + F_1)
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit)
case = 1
else:
case = 2
else:
F_2 = ((beta * alpha) * (E * np.fabs((A.position - a.position))))
a.position += F_2
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit)
case = 2
else:
case = 1 | 7,335,458,090,288,138,000 | Performs the first business phase.
Args:
agents (list): List of agents.
function (Function): A Function object that will be used as the objective function.
beta (float): Range of fluctuation. | opytimizer/optimizers/social/qsa.py | _business_one | anukaal/opytimizer | python | def _business_one(self, agents, function, beta):
'Performs the first business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n beta (float): Range of fluctuation.\n\n '
agents.sort(key=(lambda x: x.fit))
(A_1, A_2, A_3) = (copy.deepcopy(agents[0]), copy.deepcopy(agents[1]), copy.deepcopy(agents[2]))
(q_1, q_2, _) = self._calculate_queue(len(agents), A_1.fit, A_2.fit, A_3.fit)
case = None
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
if (i < q_1):
if (i == 0):
case = 1
A = copy.deepcopy(A_1)
elif (q_1 <= i < (q_1 + q_2)):
if (i == q_1):
case = 1
A = copy.deepcopy(A_2)
else:
if (i == (q_1 + q_2)):
case = 1
A = copy.deepcopy(A_3)
alpha = r.generate_uniform_random_number((- 1), 1)
E = r.generate_gamma_random_number(1, 0.5, (agent.n_variables, agent.n_dimensions))
if (case == 1):
e = r.generate_gamma_random_number(1, 0.5, 1)
F_1 = (((beta * alpha) * (E * np.fabs((A.position - a.position)))) + (e * (A.position - a.position)))
a.position = (A.position + F_1)
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit)
case = 1
else:
case = 2
else:
F_2 = ((beta * alpha) * (E * np.fabs((A.position - a.position))))
a.position += F_2
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit)
case = 2
else:
case = 1 |
def _business_two(self, agents, function):
'Performs the second business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n\n '
agents.sort(key=(lambda x: x.fit))
(A_1, A_2, A_3) = (copy.deepcopy(agents[0]), copy.deepcopy(agents[1]), copy.deepcopy(agents[2]))
(q_1, q_2, _) = self._calculate_queue(len(agents), A_1.fit, A_2.fit, A_3.fit)
pr = [(i / len(agents)) for i in range(1, (len(agents) + 1))]
cv = (A_1.fit / ((A_2.fit + A_3.fit) + c.EPSILON))
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
if (i < q_1):
A = copy.deepcopy(A_1)
elif (q_1 <= i < (q_1 + q_2)):
A = copy.deepcopy(A_2)
else:
A = copy.deepcopy(A_3)
r1 = r.generate_uniform_random_number()
if (r1 < pr[i]):
(A_1, A_2) = np.random.choice(agents, 2, replace=False)
r2 = r.generate_uniform_random_number()
e = r.generate_gamma_random_number(1, 0.5, 1)
if (r2 < cv):
F_1 = (e * (A_1.position - A_2.position))
a.position += F_1
else:
F_2 = (e * (A.position - A_1.position))
a.position += F_2
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit) | 8,086,955,353,914,416,000 | Performs the second business phase.
Args:
agents (list): List of agents.
function (Function): A Function object that will be used as the objective function. | opytimizer/optimizers/social/qsa.py | _business_two | anukaal/opytimizer | python | def _business_two(self, agents, function):
'Performs the second business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n\n '
agents.sort(key=(lambda x: x.fit))
(A_1, A_2, A_3) = (copy.deepcopy(agents[0]), copy.deepcopy(agents[1]), copy.deepcopy(agents[2]))
(q_1, q_2, _) = self._calculate_queue(len(agents), A_1.fit, A_2.fit, A_3.fit)
pr = [(i / len(agents)) for i in range(1, (len(agents) + 1))]
cv = (A_1.fit / ((A_2.fit + A_3.fit) + c.EPSILON))
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
if (i < q_1):
A = copy.deepcopy(A_1)
elif (q_1 <= i < (q_1 + q_2)):
A = copy.deepcopy(A_2)
else:
A = copy.deepcopy(A_3)
r1 = r.generate_uniform_random_number()
if (r1 < pr[i]):
(A_1, A_2) = np.random.choice(agents, 2, replace=False)
r2 = r.generate_uniform_random_number()
e = r.generate_gamma_random_number(1, 0.5, 1)
if (r2 < cv):
F_1 = (e * (A_1.position - A_2.position))
a.position += F_1
else:
F_2 = (e * (A.position - A_1.position))
a.position += F_2
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit) |
def _business_three(self, agents, function):
'Performs the third business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n\n '
agents.sort(key=(lambda x: x.fit))
pr = [(i / len(agents)) for i in range(1, (len(agents) + 1))]
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
for j in range(agent.n_variables):
r1 = r.generate_uniform_random_number()
if (r1 < pr[i]):
(A_1, A_2) = np.random.choice(agents, 2, replace=False)
e = r.generate_gamma_random_number(1, 0.5, 1)
a.position[j] = (A_1.position[j] + (e * (A_2.position[j] - a.position[j])))
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit) | -4,295,532,801,114,571,300 | Performs the third business phase.
Args:
agents (list): List of agents.
function (Function): A Function object that will be used as the objective function. | opytimizer/optimizers/social/qsa.py | _business_three | anukaal/opytimizer | python | def _business_three(self, agents, function):
'Performs the third business phase.\n\n Args:\n agents (list): List of agents.\n function (Function): A Function object that will be used as the objective function.\n\n '
agents.sort(key=(lambda x: x.fit))
pr = [(i / len(agents)) for i in range(1, (len(agents) + 1))]
for (i, agent) in enumerate(agents):
a = copy.deepcopy(agent)
for j in range(agent.n_variables):
r1 = r.generate_uniform_random_number()
if (r1 < pr[i]):
(A_1, A_2) = np.random.choice(agents, 2, replace=False)
e = r.generate_gamma_random_number(1, 0.5, 1)
a.position[j] = (A_1.position[j] + (e * (A_2.position[j] - a.position[j])))
a.fit = function(a.position)
if (a.fit < agent.fit):
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit) |
def update(self, space, function, iteration, n_iterations):
'Wraps Queue Search Algorithm over all agents and variables.\n\n Args:\n space (Space): Space containing agents and update-related information.\n function (Function): A Function object that will be used as the objective function.\n iteration (int): Current iteration.\n n_iterations (int): Maximum number of iterations.\n\n '
beta = np.exp((np.log((1 / (iteration + c.EPSILON))) * np.sqrt((iteration / n_iterations))))
self._business_one(space.agents, function, beta)
self._business_two(space.agents, function)
self._business_three(space.agents, function) | 8,129,542,189,646,762,000 | Wraps Queue Search Algorithm over all agents and variables.
Args:
space (Space): Space containing agents and update-related information.
function (Function): A Function object that will be used as the objective function.
iteration (int): Current iteration.
n_iterations (int): Maximum number of iterations. | opytimizer/optimizers/social/qsa.py | update | anukaal/opytimizer | python | def update(self, space, function, iteration, n_iterations):
'Wraps Queue Search Algorithm over all agents and variables.\n\n Args:\n space (Space): Space containing agents and update-related information.\n function (Function): A Function object that will be used as the objective function.\n iteration (int): Current iteration.\n n_iterations (int): Maximum number of iterations.\n\n '
beta = np.exp((np.log((1 / (iteration + c.EPSILON))) * np.sqrt((iteration / n_iterations))))
self._business_one(space.agents, function, beta)
self._business_two(space.agents, function)
self._business_three(space.agents, function) |
def __init__(self, **kwargs):
'\n Initializes a new WorkRequestLogEntryCollection object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param items:\n The value to assign to the items property of this WorkRequestLogEntryCollection.\n :type items: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n\n '
self.swagger_types = {'items': 'list[WorkRequestLogEntry]'}
self.attribute_map = {'items': 'items'}
self._items = None | 326,346,107,807,940,160 | Initializes a new WorkRequestLogEntryCollection object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param items:
The value to assign to the items property of this WorkRequestLogEntryCollection.
:type items: list[oci.network_load_balancer.models.WorkRequestLogEntry] | src/oci/network_load_balancer/models/work_request_log_entry_collection.py | __init__ | LaudateCorpus1/oci-python-sdk | python | def __init__(self, **kwargs):
'\n Initializes a new WorkRequestLogEntryCollection object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param items:\n The value to assign to the items property of this WorkRequestLogEntryCollection.\n :type items: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n\n '
self.swagger_types = {'items': 'list[WorkRequestLogEntry]'}
self.attribute_map = {'items': 'items'}
self._items = None |
@property
def items(self):
'\n Gets the items of this WorkRequestLogEntryCollection.\n An array of WorkRequestLogEntry objects.\n\n\n :return: The items of this WorkRequestLogEntryCollection.\n :rtype: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n '
return self._items | 6,000,403,713,483,571,000 | Gets the items of this WorkRequestLogEntryCollection.
An array of WorkRequestLogEntry objects.
:return: The items of this WorkRequestLogEntryCollection.
:rtype: list[oci.network_load_balancer.models.WorkRequestLogEntry] | src/oci/network_load_balancer/models/work_request_log_entry_collection.py | items | LaudateCorpus1/oci-python-sdk | python | @property
def items(self):
'\n Gets the items of this WorkRequestLogEntryCollection.\n An array of WorkRequestLogEntry objects.\n\n\n :return: The items of this WorkRequestLogEntryCollection.\n :rtype: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n '
return self._items |
@items.setter
def items(self, items):
'\n Sets the items of this WorkRequestLogEntryCollection.\n An array of WorkRequestLogEntry objects.\n\n\n :param items: The items of this WorkRequestLogEntryCollection.\n :type: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n '
self._items = items | -1,871,939,153,352,299,300 | Sets the items of this WorkRequestLogEntryCollection.
An array of WorkRequestLogEntry objects.
:param items: The items of this WorkRequestLogEntryCollection.
:type: list[oci.network_load_balancer.models.WorkRequestLogEntry] | src/oci/network_load_balancer/models/work_request_log_entry_collection.py | items | LaudateCorpus1/oci-python-sdk | python | @items.setter
def items(self, items):
'\n Sets the items of this WorkRequestLogEntryCollection.\n An array of WorkRequestLogEntry objects.\n\n\n :param items: The items of this WorkRequestLogEntryCollection.\n :type: list[oci.network_load_balancer.models.WorkRequestLogEntry]\n '
self._items = items |
def twoSum(self, nums, target):
'\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n '
lookup = dict(((v, i) for (i, v) in enumerate(nums)))
return next((((i + 1), (lookup.get((target - v)) + 1)) for (i, v) in enumerate(nums) if (lookup.get((target - v), i) != i)), None) | -5,897,341,292,383,729,000 | :type nums: List[int]
:type target: int
:rtype: List[int] | array/twosum.py | twoSum | mengyangbai/leetcode | python | def twoSum(self, nums, target):
'\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n '
lookup = dict(((v, i) for (i, v) in enumerate(nums)))
return next((((i + 1), (lookup.get((target - v)) + 1)) for (i, v) in enumerate(nums) if (lookup.get((target - v), i) != i)), None) |
def _parse_actions(actions):
' Actions come in as a combined list. This method separates the webhook actions into a\n separate collection and combines any number of email actions into a single email collection\n and a single value for `email_service_owners`. If any email action contains a True value\n for `send_to_service_owners` then it is assumed the entire value should be True. '
from azure.mgmt.monitor.models import RuleEmailAction, RuleWebhookAction
actions = (actions or [])
email_service_owners = None
webhooks = [x for x in actions if isinstance(x, RuleWebhookAction)]
custom_emails = set()
for action in actions:
if isinstance(action, RuleEmailAction):
if action.send_to_service_owners:
email_service_owners = True
custom_emails = (custom_emails | set(action.custom_emails))
return (list(custom_emails), webhooks, email_service_owners) | 3,501,492,025,227,609,600 | Actions come in as a combined list. This method separates the webhook actions into a
separate collection and combines any number of email actions into a single email collection
and a single value for `email_service_owners`. If any email action contains a True value
for `send_to_service_owners` then it is assumed the entire value should be True. | src/azure-cli/azure/cli/command_modules/monitor/operations/metric_alert.py | _parse_actions | 21m57/azure-cli | python | def _parse_actions(actions):
' Actions come in as a combined list. This method separates the webhook actions into a\n separate collection and combines any number of email actions into a single email collection\n and a single value for `email_service_owners`. If any email action contains a True value\n for `send_to_service_owners` then it is assumed the entire value should be True. '
from azure.mgmt.monitor.models import RuleEmailAction, RuleWebhookAction
actions = (actions or [])
email_service_owners = None
webhooks = [x for x in actions if isinstance(x, RuleWebhookAction)]
custom_emails = set()
for action in actions:
if isinstance(action, RuleEmailAction):
if action.send_to_service_owners:
email_service_owners = True
custom_emails = (custom_emails | set(action.custom_emails))
return (list(custom_emails), webhooks, email_service_owners) |
def _parse_action_removals(actions):
' Separates the combined list of keys to remove into webhooks and emails. '
flattened = list({x for sublist in actions for x in sublist})
emails = []
webhooks = []
for item in flattened:
if (item.startswith('http://') or item.startswith('https://')):
webhooks.append(item)
else:
emails.append(item)
return (emails, webhooks) | -5,889,282,027,629,094,000 | Separates the combined list of keys to remove into webhooks and emails. | src/azure-cli/azure/cli/command_modules/monitor/operations/metric_alert.py | _parse_action_removals | 21m57/azure-cli | python | def _parse_action_removals(actions):
' '
flattened = list({x for sublist in actions for x in sublist})
emails = []
webhooks = []
for item in flattened:
if (item.startswith('http://') or item.startswith('https://')):
webhooks.append(item)
else:
emails.append(item)
return (emails, webhooks) |
async def async_setup_entry(opp: OpenPeerPower, config_entry: ConfigEntry, async_add_entities) -> None:
'Set up discovered sensors.'
devs = []
for dev in opp.data[AQUALINK_DOMAIN][DOMAIN]:
devs.append(OppAqualinkSensor(dev))
async_add_entities(devs, True) | 6,281,085,809,589,287,000 | Set up discovered sensors. | openpeerpower/components/iaqualink/sensor.py | async_setup_entry | OpenPeerPower/core | python | async def async_setup_entry(opp: OpenPeerPower, config_entry: ConfigEntry, async_add_entities) -> None:
devs = []
for dev in opp.data[AQUALINK_DOMAIN][DOMAIN]:
devs.append(OppAqualinkSensor(dev))
async_add_entities(devs, True) |
@property
def name(self) -> str:
'Return the name of the sensor.'
return self.dev.label | -2,087,878,669,653,740,500 | Return the name of the sensor. | openpeerpower/components/iaqualink/sensor.py | name | OpenPeerPower/core | python | @property
def name(self) -> str:
return self.dev.label |
@property
def unit_of_measurement(self) -> (str | None):
'Return the measurement unit for the sensor.'
if self.dev.name.endswith('_temp'):
if (self.dev.system.temp_unit == 'F'):
return TEMP_FAHRENHEIT
return TEMP_CELSIUS
return None | 4,229,724,856,024,876,000 | Return the measurement unit for the sensor. | openpeerpower/components/iaqualink/sensor.py | unit_of_measurement | OpenPeerPower/core | python | @property
def unit_of_measurement(self) -> (str | None):
if self.dev.name.endswith('_temp'):
if (self.dev.system.temp_unit == 'F'):
return TEMP_FAHRENHEIT
return TEMP_CELSIUS
return None |
@property
def state(self) -> (str | None):
'Return the state of the sensor.'
if (self.dev.state == ''):
return None
try:
state = int(self.dev.state)
except ValueError:
state = float(self.dev.state)
return state | 9,126,054,177,930,050,000 | Return the state of the sensor. | openpeerpower/components/iaqualink/sensor.py | state | OpenPeerPower/core | python | @property
def state(self) -> (str | None):
if (self.dev.state == ):
return None
try:
state = int(self.dev.state)
except ValueError:
state = float(self.dev.state)
return state |
@property
def device_class(self) -> (str | None):
'Return the class of the sensor.'
if self.dev.name.endswith('_temp'):
return DEVICE_CLASS_TEMPERATURE
return None | -1,855,583,597,421,016,000 | Return the class of the sensor. | openpeerpower/components/iaqualink/sensor.py | device_class | OpenPeerPower/core | python | @property
def device_class(self) -> (str | None):
if self.dev.name.endswith('_temp'):
return DEVICE_CLASS_TEMPERATURE
return None |
def scanI2c(ip):
'\n scans devices on i2c bus\n :return: list of hex string addresses present on i2c bus\n '
try:
req_url = (('http://' + ip) + '/i2c/scan')
resp = requests.get(url=req_url)
return resp.content.decode('utf-8')
except ValueError:
print('i2c failed scan') | -6,240,809,939,133,776,000 | scans devices on i2c bus
:return: list of hex string addresses present on i2c bus | python/papaya_i2chttpinst.py | scanI2c | papaya-iot/papaya-examples | python | def scanI2c(ip):
'\n scans devices on i2c bus\n :return: list of hex string addresses present on i2c bus\n '
try:
req_url = (('http://' + ip) + '/i2c/scan')
resp = requests.get(url=req_url)
return resp.content.decode('utf-8')
except ValueError:
print('i2c failed scan') |
def read(self, reg_addr, len_read):
'\n read len_read bytes starting from register reg_addr\n :param reg_addr: (str) register address to read in hex\n :param len_read: (int) number of bytes to read\n :return: bytestring of data\n '
assert (len_read < 256), 'num of bytes to read cannot exceed 255'
hex_reg_addr = enforce_hex(reg_addr)
try:
req_url = ('%sread/%s/%s/%d' % (self.url, self.dev_addr, hex_reg_addr, len_read))
resp = requests.get(url=req_url)
return binascii.a2b_hex(resp.content)
except ValueError:
print('i2c failed read') | -222,386,349,693,098,020 | read len_read bytes starting from register reg_addr
:param reg_addr: (str) register address to read in hex
:param len_read: (int) number of bytes to read
:return: bytestring of data | python/papaya_i2chttpinst.py | read | papaya-iot/papaya-examples | python | def read(self, reg_addr, len_read):
'\n read len_read bytes starting from register reg_addr\n :param reg_addr: (str) register address to read in hex\n :param len_read: (int) number of bytes to read\n :return: bytestring of data\n '
assert (len_read < 256), 'num of bytes to read cannot exceed 255'
hex_reg_addr = enforce_hex(reg_addr)
try:
req_url = ('%sread/%s/%s/%d' % (self.url, self.dev_addr, hex_reg_addr, len_read))
resp = requests.get(url=req_url)
return binascii.a2b_hex(resp.content)
except ValueError:
print('i2c failed read') |
def write(self, reg_addr, data, len_data=0):
"\n :param reg_addr: (str) register address to write to in hex\n :param data: (str or bytes) hex-encoded bytes, ie: '014ce8'\n :param len_data: (optional int) dummy variable to support code portability\n :return: None\n "
hex_reg_addr = enforce_hex(reg_addr)
if (type(data) == bytes):
data = getencoder('hex')(data)[0].decode('ascii')
try:
req_url = ('%swrite/%s/%s/%s' % (self.url, self.dev_addr, hex_reg_addr, data))
requests.get(url=req_url)
except ValueError:
print(('i2c device 0x%s failed write' % self.dev_addr)) | -2,559,223,647,912,131,600 | :param reg_addr: (str) register address to write to in hex
:param data: (str or bytes) hex-encoded bytes, ie: '014ce8'
:param len_data: (optional int) dummy variable to support code portability
:return: None | python/papaya_i2chttpinst.py | write | papaya-iot/papaya-examples | python | def write(self, reg_addr, data, len_data=0):
"\n :param reg_addr: (str) register address to write to in hex\n :param data: (str or bytes) hex-encoded bytes, ie: '014ce8'\n :param len_data: (optional int) dummy variable to support code portability\n :return: None\n "
hex_reg_addr = enforce_hex(reg_addr)
if (type(data) == bytes):
data = getencoder('hex')(data)[0].decode('ascii')
try:
req_url = ('%swrite/%s/%s/%s' % (self.url, self.dev_addr, hex_reg_addr, data))
requests.get(url=req_url)
except ValueError:
print(('i2c device 0x%s failed write' % self.dev_addr)) |
@abc.abstractmethod
def __iter__(self) -> Iterator[Class]:
'Create an iterator for the class map values.' | 2,161,965,101,768,955,100 | Create an iterator for the class map values. | xsdata/codegen/mixins.py | __iter__ | amal-khailtash/xsdata | python | @abc.abstractmethod
def __iter__(self) -> Iterator[Class]:
|
@abc.abstractmethod
def find(self, qname: str, condition: Callable=return_true) -> Optional[Class]:
'Search by qualified name for a specific class with an optional\n condition callable.' | -7,627,424,956,996,297,000 | Search by qualified name for a specific class with an optional
condition callable. | xsdata/codegen/mixins.py | find | amal-khailtash/xsdata | python | @abc.abstractmethod
def find(self, qname: str, condition: Callable=return_true) -> Optional[Class]:
'Search by qualified name for a specific class with an optional\n condition callable.' |
@abc.abstractmethod
def find_inner(self, source: Class, qname: str) -> Class:
'Search by qualified name for a specific inner class or fail.' | -3,995,696,163,048,785,000 | Search by qualified name for a specific inner class or fail. | xsdata/codegen/mixins.py | find_inner | amal-khailtash/xsdata | python | @abc.abstractmethod
def find_inner(self, source: Class, qname: str) -> Class:
|
@abc.abstractmethod
def add(self, item: Class):
'Add class item to the container.' | 1,259,824,434,139,553,300 | Add class item to the container. | xsdata/codegen/mixins.py | add | amal-khailtash/xsdata | python | @abc.abstractmethod
def add(self, item: Class):
|
@abc.abstractmethod
def extend(self, items: List[Class]):
'Add a list of classes the container.' | 5,647,266,450,418,243,000 | Add a list of classes the container. | xsdata/codegen/mixins.py | extend | amal-khailtash/xsdata | python | @abc.abstractmethod
def extend(self, items: List[Class]):
|
@abc.abstractmethod
def reset(self, item: Class, qname: str):
'Update the given class qualified name.' | -8,610,711,932,646,473,000 | Update the given class qualified name. | xsdata/codegen/mixins.py | reset | amal-khailtash/xsdata | python | @abc.abstractmethod
def reset(self, item: Class, qname: str):
|
@abc.abstractmethod
def process(self, target: Class):
'Process the given target class.' | -781,199,706,564,020,600 | Process the given target class. | xsdata/codegen/mixins.py | process | amal-khailtash/xsdata | python | @abc.abstractmethod
def process(self, target: Class):
|
@abc.abstractmethod
def run(self):
'Run the process for the whole container.' | 8,029,094,563,809,355,000 | Run the process for the whole container. | xsdata/codegen/mixins.py | run | amal-khailtash/xsdata | python | @abc.abstractmethod
def run(self):
|
def __init__(self, db):
'\n :type db: datacube.index.postgres._api.PostgresDb\n '
self._db = db | 8,569,476,321,902,646,000 | :type db: datacube.index.postgres._api.PostgresDb | datacube/index/_datasets.py | __init__ | cronosnull/agdc-v2 | python | def __init__(self, db):
'\n \n '
self._db = db |
def add(self, definition, allow_table_lock=False):
"\n :type definition: dict\n :param allow_table_lock:\n Allow an exclusive lock to be taken on the table while creating the indexes.\n This will halt other user's requests until completed.\n\n If false, creation will be slightly slower and cannot be done in a transaction.\n :rtype: datacube.model.MetadataType\n "
MetadataType.validate(definition)
name = definition['name']
existing = self._db.get_metadata_type_by_name(name)
if existing:
check_doc_unchanged(existing.definition, definition, 'Metadata Type {}'.format(name))
else:
self._db.add_metadata_type(name=name, definition=definition, concurrently=(not allow_table_lock))
return self.get_by_name(name) | -8,154,873,849,997,148,000 | :type definition: dict
:param allow_table_lock:
Allow an exclusive lock to be taken on the table while creating the indexes.
This will halt other user's requests until completed.
If false, creation will be slightly slower and cannot be done in a transaction.
:rtype: datacube.model.MetadataType | datacube/index/_datasets.py | add | cronosnull/agdc-v2 | python | def add(self, definition, allow_table_lock=False):
"\n :type definition: dict\n :param allow_table_lock:\n Allow an exclusive lock to be taken on the table while creating the indexes.\n This will halt other user's requests until completed.\n\n If false, creation will be slightly slower and cannot be done in a transaction.\n :rtype: datacube.model.MetadataType\n "
MetadataType.validate(definition)
name = definition['name']
existing = self._db.get_metadata_type_by_name(name)
if existing:
check_doc_unchanged(existing.definition, definition, 'Metadata Type {}'.format(name))
else:
self._db.add_metadata_type(name=name, definition=definition, concurrently=(not allow_table_lock))
return self.get_by_name(name) |
@lru_cache()
def get(self, id_):
'\n :rtype: datacube.model.MetadataType\n '
return self._make(self._db.get_metadata_type(id_)) | -2,829,958,382,661,361,700 | :rtype: datacube.model.MetadataType | datacube/index/_datasets.py | get | cronosnull/agdc-v2 | python | @lru_cache()
def get(self, id_):
'\n \n '
return self._make(self._db.get_metadata_type(id_)) |
@lru_cache()
def get_by_name(self, name):
'\n :rtype: datacube.model.MetadataType\n '
record = self._db.get_metadata_type_by_name(name)
if (not record):
return None
return self._make(record) | 966,241,435,974,297,600 | :rtype: datacube.model.MetadataType | datacube/index/_datasets.py | get_by_name | cronosnull/agdc-v2 | python | @lru_cache()
def get_by_name(self, name):
'\n \n '
record = self._db.get_metadata_type_by_name(name)
if (not record):
return None
return self._make(record) |
def check_field_indexes(self, allow_table_lock=False, rebuild_all=False):
"\n Create or replace per-field indexes and views.\n :param allow_table_lock:\n Allow an exclusive lock to be taken on the table while creating the indexes.\n This will halt other user's requests until completed.\n\n If false, creation will be slightly slower and cannot be done in a transaction.\n "
self._db.check_dynamic_fields(concurrently=(not allow_table_lock), rebuild_all=rebuild_all) | 3,272,245,721,149,252,000 | Create or replace per-field indexes and views.
:param allow_table_lock:
Allow an exclusive lock to be taken on the table while creating the indexes.
This will halt other user's requests until completed.
If false, creation will be slightly slower and cannot be done in a transaction. | datacube/index/_datasets.py | check_field_indexes | cronosnull/agdc-v2 | python | def check_field_indexes(self, allow_table_lock=False, rebuild_all=False):
"\n Create or replace per-field indexes and views.\n :param allow_table_lock:\n Allow an exclusive lock to be taken on the table while creating the indexes.\n This will halt other user's requests until completed.\n\n If false, creation will be slightly slower and cannot be done in a transaction.\n "
self._db.check_dynamic_fields(concurrently=(not allow_table_lock), rebuild_all=rebuild_all) |
def _make(self, query_row):
'\n :rtype list[datacube.model.MetadataType]\n '
definition = query_row['definition']
dataset_ = definition['dataset']
return MetadataType(query_row['name'], dataset_, dataset_search_fields=self._db.get_dataset_fields(query_row), id_=query_row['id']) | 895,167,996,513,292,200 | :rtype list[datacube.model.MetadataType] | datacube/index/_datasets.py | _make | cronosnull/agdc-v2 | python | def _make(self, query_row):
'\n \n '
definition = query_row['definition']
dataset_ = definition['dataset']
return MetadataType(query_row['name'], dataset_, dataset_search_fields=self._db.get_dataset_fields(query_row), id_=query_row['id']) |
def __init__(self, db, metadata_type_resource):
'\n :type db: datacube.index.postgres._api.PostgresDb\n :type metadata_type_resource: MetadataTypeResource\n '
self._db = db
self.metadata_type_resource = metadata_type_resource | -6,762,795,850,044,275,000 | :type db: datacube.index.postgres._api.PostgresDb
:type metadata_type_resource: MetadataTypeResource | datacube/index/_datasets.py | __init__ | cronosnull/agdc-v2 | python | def __init__(self, db, metadata_type_resource):
'\n :type db: datacube.index.postgres._api.PostgresDb\n :type metadata_type_resource: MetadataTypeResource\n '
self._db = db
self.metadata_type_resource = metadata_type_resource |
def from_doc(self, definition):
'\n Create a Product from its definitions\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(definition)
metadata_type = definition['metadata_type']
if isinstance(metadata_type, compat.string_types):
metadata_type = self.metadata_type_resource.get_by_name(metadata_type)
else:
metadata_type = self.metadata_type_resource.add(metadata_type, allow_table_lock=False)
if (not metadata_type):
raise InvalidDocException(('Unknown metadata type: %r' % definition['metadata_type']))
return DatasetType(metadata_type, definition) | -8,327,443,222,895,981,000 | Create a Product from its definitions
:param dict definition: product definition document
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | from_doc | cronosnull/agdc-v2 | python | def from_doc(self, definition):
'\n Create a Product from its definitions\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(definition)
metadata_type = definition['metadata_type']
if isinstance(metadata_type, compat.string_types):
metadata_type = self.metadata_type_resource.get_by_name(metadata_type)
else:
metadata_type = self.metadata_type_resource.add(metadata_type, allow_table_lock=False)
if (not metadata_type):
raise InvalidDocException(('Unknown metadata type: %r' % definition['metadata_type']))
return DatasetType(metadata_type, definition) |
def add(self, type_):
'\n Add a Product\n\n :param datacube.model.DatasetType type_: Product to add\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(type_.definition)
existing = self._db.get_dataset_type_by_name(type_.name)
if existing:
check_doc_unchanged(existing.definition, jsonify_document(type_.definition), 'Dataset type {}'.format(type_.name))
else:
self._db.add_dataset_type(name=type_.name, metadata=type_.metadata_doc, metadata_type_id=type_.metadata_type.id, definition=type_.definition)
return self.get_by_name(type_.name) | 3,908,486,326,552,046,600 | Add a Product
:param datacube.model.DatasetType type_: Product to add
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | add | cronosnull/agdc-v2 | python | def add(self, type_):
'\n Add a Product\n\n :param datacube.model.DatasetType type_: Product to add\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(type_.definition)
existing = self._db.get_dataset_type_by_name(type_.name)
if existing:
check_doc_unchanged(existing.definition, jsonify_document(type_.definition), 'Dataset type {}'.format(type_.name))
else:
self._db.add_dataset_type(name=type_.name, metadata=type_.metadata_doc, metadata_type_id=type_.metadata_type.id, definition=type_.definition)
return self.get_by_name(type_.name) |
def update(self, type_, allow_unsafe_updates=False):
'\n Update a product. Unsafe changes will throw a ValueError by default.\n\n (An unsafe change is anything that may potentially make the product\n incompatible with existing datasets of that type)\n\n :param datacube.model.DatasetType type_: Product to add\n :param allow_unsafe_updates bool: Allow unsafe changes. Use with caution.\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(type_.definition)
existing = self._db.get_dataset_type_by_name(type_.name)
if (not existing):
raise ValueError(('Unknown product %s, cannot update – did you intend to add it?' % type_.name))
def handle_unsafe(msg):
if (not allow_unsafe_updates):
raise ValueError(msg)
else:
_LOG.warning('Ignoring %s', msg)
safe_keys_to_change = ('description', 'metadata')
doc_changes = get_doc_changes(existing.definition, jsonify_document(type_.definition))
for (offset, old_value, new_value) in doc_changes:
_LOG.info('Changing %s %s: %r -> %r', type_.name, '.'.join(offset), old_value, new_value)
key_name = offset[0]
if (key_name not in safe_keys_to_change):
handle_unsafe(('Potentially unsafe update: changing %r of product definition.' % key_name))
if (key_name == 'metadata'):
if (not contains(old_value, new_value, case_sensitive=True)):
handle_unsafe('Unsafe update: new product match rules are not a superset of old ones.')
if doc_changes:
_LOG.info('Updating product %s', type_.name)
self._db.update_dataset_type(name=type_.name, metadata=type_.metadata_doc, metadata_type_id=type_.metadata_type.id, definition=type_.definition)
self.get_by_name.cache_clear()
self.get.cache_clear()
else:
_LOG.info('No changes detected for product %s', type_.name) | -881,313,504,314,386,600 | Update a product. Unsafe changes will throw a ValueError by default.
(An unsafe change is anything that may potentially make the product
incompatible with existing datasets of that type)
:param datacube.model.DatasetType type_: Product to add
:param allow_unsafe_updates bool: Allow unsafe changes. Use with caution.
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | update | cronosnull/agdc-v2 | python | def update(self, type_, allow_unsafe_updates=False):
'\n Update a product. Unsafe changes will throw a ValueError by default.\n\n (An unsafe change is anything that may potentially make the product\n incompatible with existing datasets of that type)\n\n :param datacube.model.DatasetType type_: Product to add\n :param allow_unsafe_updates bool: Allow unsafe changes. Use with caution.\n :rtype: datacube.model.DatasetType\n '
DatasetType.validate(type_.definition)
existing = self._db.get_dataset_type_by_name(type_.name)
if (not existing):
raise ValueError(('Unknown product %s, cannot update – did you intend to add it?' % type_.name))
def handle_unsafe(msg):
if (not allow_unsafe_updates):
raise ValueError(msg)
else:
_LOG.warning('Ignoring %s', msg)
safe_keys_to_change = ('description', 'metadata')
doc_changes = get_doc_changes(existing.definition, jsonify_document(type_.definition))
for (offset, old_value, new_value) in doc_changes:
_LOG.info('Changing %s %s: %r -> %r', type_.name, '.'.join(offset), old_value, new_value)
key_name = offset[0]
if (key_name not in safe_keys_to_change):
handle_unsafe(('Potentially unsafe update: changing %r of product definition.' % key_name))
if (key_name == 'metadata'):
if (not contains(old_value, new_value, case_sensitive=True)):
handle_unsafe('Unsafe update: new product match rules are not a superset of old ones.')
if doc_changes:
_LOG.info('Updating product %s', type_.name)
self._db.update_dataset_type(name=type_.name, metadata=type_.metadata_doc, metadata_type_id=type_.metadata_type.id, definition=type_.definition)
self.get_by_name.cache_clear()
self.get.cache_clear()
else:
_LOG.info('No changes detected for product %s', type_.name) |
def update_document(self, definition, allow_unsafe_update=False):
'\n Update a Product using its difinition\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
type_ = self.from_doc(definition)
return self.update(type_, allow_unsafe_updates=allow_unsafe_update) | 5,400,559,459,547,803,000 | Update a Product using its difinition
:param dict definition: product definition document
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | update_document | cronosnull/agdc-v2 | python | def update_document(self, definition, allow_unsafe_update=False):
'\n Update a Product using its difinition\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
type_ = self.from_doc(definition)
return self.update(type_, allow_unsafe_updates=allow_unsafe_update) |
def add_document(self, definition):
'\n Add a Product using its difinition\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
type_ = self.from_doc(definition)
return self.add(type_) | 3,831,264,721,657,754,000 | Add a Product using its difinition
:param dict definition: product definition document
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | add_document | cronosnull/agdc-v2 | python | def add_document(self, definition):
'\n Add a Product using its difinition\n\n :param dict definition: product definition document\n :rtype: datacube.model.DatasetType\n '
type_ = self.from_doc(definition)
return self.add(type_) |
@lru_cache()
def get(self, id_):
'\n Retrieve Product by id\n\n :param int id_: id of the Product\n :rtype: datacube.model.DatasetType\n '
return self._make(self._db.get_dataset_type(id_)) | 4,528,030,748,589,950,000 | Retrieve Product by id
:param int id_: id of the Product
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | get | cronosnull/agdc-v2 | python | @lru_cache()
def get(self, id_):
'\n Retrieve Product by id\n\n :param int id_: id of the Product\n :rtype: datacube.model.DatasetType\n '
return self._make(self._db.get_dataset_type(id_)) |
@lru_cache()
def get_by_name(self, name):
'\n Retrieve Product by name\n\n :param str name: name of the Product\n :rtype: datacube.model.DatasetType\n '
result = self._db.get_dataset_type_by_name(name)
if (not result):
return None
return self._make(result) | 7,114,874,661,913,099,000 | Retrieve Product by name
:param str name: name of the Product
:rtype: datacube.model.DatasetType | datacube/index/_datasets.py | get_by_name | cronosnull/agdc-v2 | python | @lru_cache()
def get_by_name(self, name):
'\n Retrieve Product by name\n\n :param str name: name of the Product\n :rtype: datacube.model.DatasetType\n '
result = self._db.get_dataset_type_by_name(name)
if (not result):
return None
return self._make(result) |
def get_with_fields(self, field_names):
'\n Return dataset types that have all the given fields.\n\n :param tuple[str] field_names:\n :rtype: __generator[DatasetType]\n '
for type_ in self.get_all():
for name in field_names:
if (name not in type_.metadata_type.dataset_fields):
break
else:
(yield type_) | 8,910,857,798,950,001,000 | Return dataset types that have all the given fields.
:param tuple[str] field_names:
:rtype: __generator[DatasetType] | datacube/index/_datasets.py | get_with_fields | cronosnull/agdc-v2 | python | def get_with_fields(self, field_names):
'\n Return dataset types that have all the given fields.\n\n :param tuple[str] field_names:\n :rtype: __generator[DatasetType]\n '
for type_ in self.get_all():
for name in field_names:
if (name not in type_.metadata_type.dataset_fields):
break
else:
(yield type_) |
def search(self, **query):
'\n Return dataset types that have all the given fields.\n\n :param dict query:\n :rtype: __generator[DatasetType]\n '
for (type_, q) in self.search_robust(**query):
if (not q):
(yield type_) | 4,333,933,674,560,769,500 | Return dataset types that have all the given fields.
:param dict query:
:rtype: __generator[DatasetType] | datacube/index/_datasets.py | search | cronosnull/agdc-v2 | python | def search(self, **query):
'\n Return dataset types that have all the given fields.\n\n :param dict query:\n :rtype: __generator[DatasetType]\n '
for (type_, q) in self.search_robust(**query):
if (not q):
(yield type_) |
def search_robust(self, **query):
'\n Return dataset types that match match-able fields and dict of remaining un-matchable fields.\n\n :param dict query:\n :rtype: __generator[(DatasetType, dict)]\n '
for type_ in self.get_all():
q = query.copy()
if (q.pop('product', type_.name) != type_.name):
continue
if (q.pop('metadata_type', type_.metadata_type.name) != type_.metadata_type.name):
continue
for (key, value) in list(q.items()):
try:
exprs = fields.to_expressions(type_.metadata_type.dataset_fields.get, **{key: value})
except UnknownFieldError as e:
break
try:
if all((expr.evaluate(type_.metadata_doc) for expr in exprs)):
q.pop(key)
else:
break
except (AttributeError, KeyError, ValueError) as e:
continue
else:
(yield (type_, q)) | -9,188,898,544,133,689,000 | Return dataset types that match match-able fields and dict of remaining un-matchable fields.
:param dict query:
:rtype: __generator[(DatasetType, dict)] | datacube/index/_datasets.py | search_robust | cronosnull/agdc-v2 | python | def search_robust(self, **query):
'\n Return dataset types that match match-able fields and dict of remaining un-matchable fields.\n\n :param dict query:\n :rtype: __generator[(DatasetType, dict)]\n '
for type_ in self.get_all():
q = query.copy()
if (q.pop('product', type_.name) != type_.name):
continue
if (q.pop('metadata_type', type_.metadata_type.name) != type_.metadata_type.name):
continue
for (key, value) in list(q.items()):
try:
exprs = fields.to_expressions(type_.metadata_type.dataset_fields.get, **{key: value})
except UnknownFieldError as e:
break
try:
if all((expr.evaluate(type_.metadata_doc) for expr in exprs)):
q.pop(key)
else:
break
except (AttributeError, KeyError, ValueError) as e:
continue
else:
(yield (type_, q)) |
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