Source code for dwave.cloud.computation

# Copyright 2017 D-Wave Systems Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Computation manages the interactions between your code and a :term:`solver`, which
manages interactions between the remote resource and your submitted problems.

Your solver instantiates a :class:`Future` object for its calls, via D-Wave Sampler API
(SAPI) servers, to the remote resource.

You can interact through the :class:`Future` object with pending (running) or completed
computation---sampling on a QPU or software solver---executed remotely, monitoring problem status,
waiting for and retrieving results, cancelling enqueued jobs, etc.

Some :class:`Future` methods are blocking.

"""

import time
import threading
import functools
import warnings

from operator import itemgetter
from dateutil.parser import parse
from concurrent.futures import TimeoutError

from dwave.cloud.utils import (
    utcnow, datetime_to_timestamp, aliasdict, deprecated)
from dwave.cloud.exceptions import InvalidAPIResponseError

# Use numpy if available for fast decoding
try:
    import numpy as np
    _numpy = True
except ImportError:
    _numpy = False

__all__ = ['Future']


[docs]@functools.total_ordering class Future(object): """Class for interacting with jobs submitted to SAPI. :class:`~dwave.cloud.solver.Solver` uses :class:`Future` to construct objects for pending SAPI calls that can wait for requests to complete and parse returned messages. Objects are blocked for the duration of any data accessed on the remote resource. Warning: :class:`Future` objects are not intended to be directly created. Problem submittal is initiated by one of the solvers in :mod:`~dwave.cloud.solver` module and executed by one of the clients. Args: solver (:class:`~dwave.cloud.solver.Solver`): Solver responsible for this :class:`Future` object. id_ (str, optional, default=None): Identification for a query submitted by a solver to SAPI. May be None following submission until an identification number is set. return_matrix (bool, optional, default=False): Return values for this :class:`Future` object are NumPy matrices. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource for 100 samples, and checks a couple of times whether the sampling is completed. >>> from dwave.cloud import Client >>> client = Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP >>> computation.done() # doctest: +SKIP False >>> computation.id # doctest: +SKIP '1cefeb6d-ebd5-4592-87c0-4cc43ec03e27' >>> computation.done() # doctest: +SKIP True >>> client.close() # doctest: +SKIP """ def __init__(self, solver, id_, return_matrix=False): self.solver = solver # Has the client tried to cancel this job self._cancel_requested = False self._cancel_sent = False self._single_cancel_lock = threading.Lock() # Make sure we only call cancel once # ID readiness notification self._id_ready_event = threading.Event() # Should the results be decoded as python lists or numpy matrices if return_matrix and not _numpy: raise ValueError("Matrix result requested without numpy.") self.return_matrix = return_matrix #: The id the server will use to identify this problem, None until the id is actually known self.id = id_ #: `datetime` the Future was created (immediately before enqueued in Client's submit queue) self.time_created = utcnow() #: `datetime` corresponding to the time when the problem was accepted by the server (None before then) self.time_received = None #: `datetime` corresponding to the time when the problem was completed by the server (None before then) self.time_solved = None #: `datetime` the Future was resolved (marked as done; succeeded or failed), or None before then self.time_resolved = None # estimated `earliest_completion_time` as returned on problem submit self.eta_min = None # estimated `latest_completion_time` as returned on problem submit self.eta_max = None # Track how long it took us to parse the data self.parse_time = None # approx. server-client clocks difference in seconds self.clock_diff = None # Data from the server before it is parsed self._message = None #: Status flag most recently returned by the server self.remote_status = None # Data from the server after it's parsed self._result = None self._exception = None # Event(s) to signal when the results are ready self._results_ready_event = threading.Event() self._other_events = [] # current poll back-off interval, in seconds self._poll_backoff = None # XXX: energy offset carried via Future, until implemented in SAPI self._offset = 0 # TODO: remove in 0.9.0 @property def error(self): """Deprecated in favor of :meth:`.exception`. Scheduled for removal in 0.9.0. """ warnings.warn( "'Future.error' is deprecated, and it will be removed " "in 0.9.0. please convert your code to use 'Future.exception()'", DeprecationWarning) return self._exception # make Future ordered def __lt__(self, other): return id(self) < id(other) def __eq__(self, other): return self is other def __hash__(self): return id(self) def _set_message(self, message): """Complete the future with a message from the server. The message from the server may actually be an error. Args: message (dict): Data from the server from trying to complete query. """ self._message = message self._signal_ready() def _set_exception(self, exception): """Complete the future with an exception. Args: exception (Exception): Exception that caused the failure. """ self._exception = exception self._signal_ready() def _signal_ready(self): """Signal all the events waiting on this future.""" self.time_resolved = utcnow() self._results_ready_event.set() [ev.set() for ev in self._other_events] def _add_event(self, event): """Add an event to be signaled after this event completes.""" self._other_events.append(event) if self.done(): event.set() def _remove_event(self, event): """Remove a completion event from this future.""" self._other_events.remove(event) def _set_clock_diff(self, server_response, localtime_of_response): """Calculate and set the `.clock_diff`, based on headers from a server response, and the local time of response received. Based on `clock_diff`, `eta_min`/`eta_max` may or may not make sense. """ try: server_time = datetime_to_timestamp(parse(server_response.headers['date'])) except: server_time = 0 self.clock_diff = abs(server_time - localtime_of_response)
[docs] @staticmethod def wait_multiple(futures, min_done=None, timeout=None): """Wait for multiple :class:`Future` objects to complete. Blocking call that uses an event object to emulate multi-wait for Python. Args: futures (list of Futures): List of :class:`Future` objects to await. min_done (int, optional, default=None): Minimum required completions to end the waiting. The wait is terminated when this number of results are ready. If None, waits for all the :class:`Future` objects to complete. timeout (float, optional, default=None): Maximum number of seconds to await completion. If None, waits indefinitely. Returns: Two-tuple of :class:`Future` objects: completed and not completed submitted tasks. Similar to `concurrent.futures.wait()` method's returned two-tuple of `done` and `not_done` sets. See Also: :func:`as_completed` for a blocking iterable of resolved futures similar to `concurrent.futures.as_completed()` method. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource 3 times for differing numers of samples, and waits for sampling to complete on any two of the submissions. The wait ends with the completion of two submissions while the third is still in progress. (A more typical approach would use something like :code:`first = next(Future.as_completed(computation))` instead.) >>> import dwave.cloud as dc >>> client = dc.Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = [solver.sample_qubo(Q, num_reads=1000), ... solver.sample_qubo(Q, num_reads=50), ... solver.sample_qubo(Q, num_reads=10)] # doctest: +SKIP >>> dc.computation.Future.wait_multiple(computation, min_done=1) # doctest: +SKIP ([<dwave.cloud.computation.Future at 0x17dde518>, <dwave.cloud.computation.Future at 0x17ddee80>], [<dwave.cloud.computation.Future at 0x15078080>]) >>> print(computation[0].done()) # doctest: +SKIP False >>> print(computation[1].done()) # doctest: +SKIP True >>> print(computation[2].done()) # doctest: +SKIP True >>> client.close() # doctest: +SKIP """ if min_done is None: min_done = len(futures) if timeout is None: timeout = float('inf') # Track the exit conditions finish = time.time() + timeout done = [] # Keep track of what futures haven't finished remaining = list(futures) # Insert our event into all the futures event = threading.Event() [f._add_event(event) for f in remaining] # Check the exit conditions while len(done) < min_done and finish > time.time(): # Prepare to wait on any of the jobs finishing event.clear() # Check if any of the jobs have finished. After the clear just in # case one finished and we erased the signal it by calling clear above finished_futures = {f for f in remaining if f.done()} if len(finished_futures) > 0: # If we did make a mistake reseting the event, undo that now # so that we double check the finished list before a wait blocks event.set() # Update our exit conditions done.extend(finished_futures) remaining = [f for f in remaining if f not in finished_futures] continue # Block on any of the jobs finishing wait_time = finish - time.time() if abs(finish) != float('inf') else None event.wait(wait_time) # Clean up after ourselves [f._remove_event(event) for f in futures] return done, remaining
[docs] @staticmethod def as_completed(fs, timeout=None): """Yield Futures objects as they complete. Returns an iterator over the specified list of :class:`Future` objects that yields those objects as they complete. Completion occurs when the submitted job is finished or cancelled. Emulates the behavior of the `concurrent.futures.as_completed()` function. Args: fs (list): List of :class:`Future` objects to iterate over. timeout (float, optional, default=None): Maximum number of seconds to await completion. If None, awaits indefinitely. Returns: Generator (:class:`Future` objects): Listed :class:`Future` objects as they complete. Raises: `concurrent.futures.TimeoutError` is raised if per-future timeout is exceeded. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource 3 times for differing numers of samples, and yields timing information for each job as it completes. >>> import dwave.cloud as dc >>> client = dc.Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = [solver.sample_qubo(Q, num_reads=1000), ... solver.sample_qubo(Q, num_reads=50), ... solver.sample_qubo(Q, num_reads=10)] # doctest: +SKIP >>> for tasks in dc.computation.Future.as_completed(computation, timeout=10) ... print(tasks.timing) # doctest: +SKIP ... {'total_real_time': 17318, ... 'qpu_readout_time_per_sample': 123} {'total_real_time': 10816, ... 'qpu_readout_time_per_sample': 123} {'total_real_time': 26285, ... 'qpu_readout_time_per_sample': 123} ... >>> client.close() # doctest: +SKIP """ not_done = fs while not_done: done, not_done = Future.wait_multiple(not_done, min_done=1, timeout=timeout) if not done: raise TimeoutError for f in done: yield f
[docs] def wait(self, timeout=None): """Wait for the solver to receive a response for a submitted problem. Blocking call that waits for a :class:`Future` object to complete. Args: timeout (float, optional, default=None): Maximum number of seconds to await completion. If None, waits indefinitely. Returns: Boolean: True if solver received a response. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource for 100 samples, and tries waiting for 10 seconds for sampling to complete. >>> from dwave.cloud import Client >>> client = Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP >>> computation.wait(timeout=10) # doctest: +SKIP False >>> computation.remote_status # doctest: +SKIP 'IN_PROGRESS' >>> computation.wait(timeout=10) # doctest: +SKIP True >>> computation.remote_status # doctest: +SKIP 'COMPLETED' >>> client.close() # doctest: +SKIP """ return self._results_ready_event.wait(timeout)
[docs] def done(self): """Check whether the solver received a response for a submitted problem. Non-blocking call that checks whether the solver has received a response from the remote resource. Returns: Boolean: True if solver received a response. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource for 100 samples, and checks a couple of times whether sampling is completed. >>> from dwave.cloud import Client >>> client = Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP >>> computation.done() # doctest: +SKIP False >>> computation.done() # doctest: +SKIP True >>> client.close() # doctest: +SKIP """ return self._results_ready_event.is_set()
[docs] def cancel(self): """Try to cancel the problem corresponding to this result. Non-blocking call to the remote resource in a best-effort attempt to prevent execution of a problem. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem to a remote D-Wave resource for 100 samples, and tries (and in this case succeeds) to cancel it. >>> from dwave.cloud import Client >>> client = Client.from_config() # doctest: +SKIP >>> solver = client.get_solver() # doctest: +SKIP >>> u, v = next(iter(solver.edges)) # doctest: +SKIP >>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP >>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP >>> computation.cancel() # doctest: +SKIP >>> computation.done() # doctest: +SKIP True >>> computation.remote_status # doctest: +SKIP 'CANCELLED' >>> client.close() # doctest: +SKIP """ # Don't need to cancel something already finished if self.done(): return with self._single_cancel_lock: # Already done if self._cancel_requested: return # Set the cancel flag self._cancel_requested = True # The cancel request will be sent here, or by the solver when it # gets a status update for this problem (in the case where the id hasn't been set yet) if self.id is not None and not self._cancel_sent: self._cancel_sent = True self.solver.client._cancel(self.id, self)
[docs] def wait_id(self, timeout=None): """Blocking id getter. Return the submitted problem ID, but unlike :meth:`.id`, block until the ID becomes known, or until `timeout` expires. Args: timeout (float, default=None): Timeout in seconds. By default, wait indefinitely for problem id to become known/available. Returns: str: Problem ID, as returned by SAPI. Raises: :exc:`concurrent.futures.TimeoutError`: When `timeout` exceeded, and problem id not ready. """ if not self._id_ready_event.wait(timeout=timeout): raise TimeoutError("problem id not available yet") return self._id
@property def id(self): """Simple non-blocking id getter for backward compat.""" return self._id @id.setter def id(self, value): """Sets the problem ID, notifying the related event.""" self._id = value # notify ID is set/ready if value is not None: self._id_ready_event.set()
[docs] def result(self): """Results for a submitted job. Retrives raw result data in a :class:`Future` object that the solver submitted to a remote resource. First calls to access this data are blocking. Returns: dict: Results of the submitted job. Should be considered read-only. Note: Helper properties on :class:`Future` object are preferred to reading raw results, as they abstract away the differences in response between some solvers like. Available methods are: :meth:`samples`, :meth:`energies`, :meth:`occurrences`, :meth:`variables`, :meth:`timing`, :meth:`problem_type`, :meth:`sampleset` (only if dimod package is installed). Warning: The dictionary returned by :meth:`result` depends on the solver used. Starting with version 0.7.0 we will not try to standardize them anymore, on client side. For QPU solvers, please replace `'samples'` with `'solutions'` and `'occurrences'` with `'num_occurrences'`. Better yet, use :meth:`Future.samples` and :meth:`Future.num_occurrences` instead. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem (representing a Boolean NOT gate by a penalty function) to a remote D-Wave resource for 5 samples, and prints part of the returned result (the relevant samples). >>> from dwave.cloud import Client >>> with Client.from_config() as client: # doctest: +SKIP ... solver = client.get_solver() ... u, v = next(iter(solver.edges)) ... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} ... computation = solver.sample_qubo(Q, num_reads=5) ... for i in range(5): ... result = computation.result() ... print(result['solutions'][i][u], result['solutions'][i][v]) ... ... (0, 1) (1, 0) (1, 0) (0, 1) (0, 1) """ self._load_result() return self._result
[docs] def exception(self): if self._exception is not None: raise self._exception
@property def energies(self): """Energy buffer for the submitted job. First calls to access data of a :class:`Future` object are blocking; subsequent access to this property is non-blocking. Returns: list or NumPy matrix of doubles: Energies for each set of samples. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a random Ising problem (+1 or -1 values of linear and quadratic biases on all nodes and edges, respectively, of the solver's garph) to a remote D-Wave resource for 10 samples, and prints the returned energies. >>> import random >>> from dwave.cloud import Client >>> with Client.from_config() as client: # doctest: +SKIP ... solver = client.get_solver() ... linear = {index: random.choice([-1, 1]) for index in solver.nodes} ... quad = {key: random.choice([-1, 1]) for key in solver.undirected_edges} ... computation = solver.sample_ising(linear, quad, num_reads=10) ... print(computation.energies) ... [-3976.0, -3974.0, -3972.0, -3970.0, -3968.0, -3968.0, -3966.0, -3964.0, -3964.0, -3960.0] """ # return energies from sampleset, if already constructed result = self.result() if 'sampleset' in result: return result['sampleset'].record.energy # fallback to energies from response return result['energies'] @property def samples(self): """State buffer for the submitted job. First calls to access data of a :class:`Future` object are blocking; subsequent access to this property is non-blocking. Returns: list of lists or NumPy matrix: Samples on the nodes of solver's graph. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple QUBO problem (representing a Boolean NOT gate by a penalty function) to a remote D-Wave resource for 5 samples, and prints part of the returned result (the relevant samples). >>> from dwave.cloud import Client >>> with Client.from_config() as client: # doctest: +SKIP ... solver = client.get_solver() ... u, v = next(iter(solver.edges)) ... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} ... computation = solver.sample_qubo(Q, num_reads=5) ... for i in range(5): ... print(computation.samples[i][u], computation.samples[i][v]) ... ... (1, 0) (0, 1) (0, 1) (1, 0) (0, 1) """ # return samples from sampleset, if already constructed result = self.result() if 'sampleset' in result: return result['sampleset'].record.sample # fallback to samples from response return result['solutions'] @property def variables(self): """List of active variables in response/answer.""" result = self.result() if 'active_variables' in result: return result['active_variables'] if 'sampleset' in result: return result['sampleset'].variables raise InvalidAPIResponseError("Active variables not present in the response") @property def num_occurrences(self): """Number of sample occurrences buffer for the submitted job. First calls to access data of a :class:`Future` object are blocking; subsequent access to this property is non-blocking. Returns: list or NumPy matrix of doubles: number of occurrences. When returned results are ordered in a histogram, `num_occurrences` indicates the number of times a particular solution recurred. Examples: This example creates a solver using the local system's default D-Wave Cloud Client configuration file, submits a simple Ising problem with several ground states to a remote D-Wave resource for 20 samples, and prints the returned results, which are ordered as a histogram. The problem's ground states tend to recur frequently, and so those solutions have `occurrences` greater than 1. >>> from dwave.cloud import Client >>> with Client.from_config() as client: # doctest: +SKIP ... solver = client.get_solver() ... quad = {(16, 20): -1, (17, 20): 1, (16, 21): 1, (17, 21): 1} ... computation = solver.sample_ising({}, quad, num_reads=500, answer_mode='histogram') ... for i in range(len(computation.num_occurrences)): ... print(computation.samples[i][16], computation.samples[i][17], ... computation.samples[i][20], computation.samples[i][21], ... ' --> ', computation.energies[i], computation.num_occurrences[i]) ... (-1, 1, -1, -1, ' --> ', -2.0, 41) (-1, -1, -1, 1, ' --> ', -2.0, 53) (1, -1, 1, 1, ' --> ', -2.0, 55) (1, 1, -1, -1, ' --> ', -2.0, 52) (1, 1, 1, -1, ' --> ', -2.0, 60) (1, -1, 1, -1, ' --> ', -2.0, 196) (-1, 1, -1, 1, ' --> ', -2.0, 15) (-1, -1, 1, 1, ' --> ', -2.0, 28) """ # return num_occurrences from sampleset, if already constructed result = self.result() if 'sampleset' in result: return result['sampleset'].record.num_occurrences # fallback to num_occurrences from response # (but `occurrences` data is not present if `answer_mode` was set to "raw") if 'num_occurrences' in result: return result['num_occurrences'] elif self.return_matrix: return np.ones((len(result['solutions']),)) else: return [1] * len(result['solutions']) # TODO: remove in 0.10.0+ @property def occurrences(self): """Deprecated in favor of Future.num_occurrences property. Scheduled for removal in 0.10.0. """ warnings.warn( "'Future.occurrences' is deprecated, and it will be removed " "in 0.10.0+. Please convert your code to use 'Future.num_occurrences'", DeprecationWarning) return self.num_occurrences
[docs] def wait_sampleset(self): """Blocking sampleset getter.""" result = self._load_result() if 'sampleset' in result: return result['sampleset'] # construct sampleset from available data try: import dimod except ImportError: raise RuntimeError("Can't construct SampleSet without dimod. " "Re-install the library with 'bqm' support.") # filter inactive variables from samples variables = self.variables samples = [[sample[v] for v in variables] for sample in self.samples] # infer vartype from problem type # note: KeyError on unknown problem types. BQM/DQM should be handled above. vartype_from_problem_type = {'ising': 'SPIN', 'qubo': 'BINARY'} vartype = vartype_from_problem_type[self.problem_type] # include timing and id in info info = dict(timing=self.timing, problem_id=self.id) sampleset = dimod.SampleSet.from_samples( (samples, variables), vartype=vartype, energy=self.energies, num_occurrences=self.num_occurrences, info=info, sort_labels=True) # this means that samplesets retrieved BEFORE this function are called # are not the same object as after, but it is a simpler implementation self._result['sampleset'] = self._sampleset = sampleset return sampleset
@property def sampleset(self): """Return :class:`~dimod.SampleSet` representation of the results.""" try: return self._sampleset except AttributeError: pass try: import dimod except ImportError: raise RuntimeError("Can't construct SampleSet without dimod. " "Re-install the library with 'bqm' support.") self._sampleset = sampleset = dimod.SampleSet.from_future( self, lambda f: f.wait_sampleset()) return sampleset @property def timing(self): """Timing information about a solver operation. Mapping from string keys to numeric values representing timing details for a submitted job as returned from the remote resource. Keys are dependant on the particular solver. First calls to access data of a :class:`Future` object are blocking; subsequent access to this property is non-blocking. Returns: dict: Mapping from string keys to numeric values representing timing information. Examples: This example creates a client using the local system's default D-Wave Cloud Client configuration file, which is configured to access a D-Wave 2000Q QPU, submits a simple :term:`Ising` problem (opposite linear biases on two coupled qubits) for 5 samples, and prints timing information for the job. >>> from dwave.cloud import Client >>> with Client.from_config() as client: # doctest: +SKIP ... solver = client.get_solver() ... u, v = next(iter(solver.edges)) ... computation = solver.sample_ising({u: -1, v: 1},{}, num_reads=5) ... print(computation.timing) ... {'total_real_time': 10961, 'anneal_time_per_run': 20, ...} """ return self.result().get('timing', {}) @property def problem_type(self): """Submitted problem type for this computation, as returned by the solver API. Typical values are 'ising' and 'qubo'. """ return self.result()['problem_type'] def __getitem__(self, key): """Provide a simple results item getter. Blocks if future is unresolved. Args: key: keywords for result fields. """ self._load_result() if key not in self._result: raise KeyError('{} is not a property of response object'.format(key)) return self._result[key] def _load_result(self): """Get the result, waiting and decoding as needed.""" if self._result is None: # Wait for the query response self.wait(timeout=None) # Check for other error conditions if self._exception is not None: raise self._exception # If someone else took care of this while we were waiting if self._result is not None: return self._result # Prepare results from the response self._decode() self._alias_result() return self._result def _patch_offset(self): # XXX: This is a temporary fix, until SAPI starts returning the offset # in answer (for structured solvers only). # It will patch `self._message` to include the offset as set in # `self._offset`, but only if SAPI answer does not contain offset already. msg = self._message fmt = msg.get('answer', {}).get('format') if fmt == 'qp': if 'offset' not in msg['answer']: msg['answer']['offset'] = self._offset def _decode(self): """Decode answer data from the response.""" start = time.time() self._patch_offset() self._result = self.solver.decode_response(self._message) self.parse_time = time.time() - start return self._result # TODO: schedule for removal def _alias_result(self): """Alias `solutions` and `num_occurrences`. Deprecated in version 0.8.0. """ if not self._result: return msg = "'{}' alias has been deprecated in favor of '{}'" samples_msg = msg.format('samples', 'solutions') occurrences_msg = msg.format('occurrences', 'num_occurrences') aliases = dict( samples=deprecated(samples_msg)(itemgetter('solutions')), occurrences=deprecated(occurrences_msg)(itemgetter('num_occurrences'))) self._result = aliasdict(self._result) self._result.alias(aliases) return self._result