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A ResultDict with additional job metadata.
cirq_google.engine.EngineResult(
*,
job_id: str,
job_finished_time: datetime.datetime,
params: Optional[study.ParamResolver] = None,
measurements: Optional[Mapping[str, np.ndarray]] = None,
records: Optional[Mapping[str, np.ndarray]] = None
)
Please see the documentation for cirq.ResultDict
for more information.
Additional Attributes:
job_id
: A string job identifier.job_finished_time
: A timestamp for when the job finished.
Args  

job_id

A string job identifier. 
job_finished_time

A timestamp for when the job finished; will be converted to UTC. 
params

A ParamResolver of settings used for this result. 
measurements

A dictionary from measurement gate key to measurement
results. See cirq.ResultDict .

records

A dictionary from measurement gate key to measurement
results. See cirq.ResultDict .

Attributes  

data

Measurements converted to a pandas dataframe.
The rows in the returned data frame correspond to repetitions of the
circuit, and the columns correspond to measurement keys, where each
element is a bigendian integer representation of measurement outcomes
for the measurement key in that repetition. To convert these ints to
bits see 
measurements

A mapping from measurement gate key to measurement results.
The value for each key is a 2D array of booleans, with the first index running over the repetitions, and the second index running over the qubits for the corresponding measurements. 
params

A ParamResolver of settings used for this result. 
records

A mapping from measurement key to measurement records.
The value for each key is a 3D array of booleans, with the first index running over circuit repetitions, the second index running over instances of the measurement key in the circuit, and the third index running over the qubits for the corresponding measurements. 
repetitions

Methods
dataframe_from_measurements
@staticmethod
dataframe_from_measurements( measurements: Mapping[str, np.ndarray] ) > pd.DataFrame
Converts the given measurements to a pandas dataframe.
This can be used by subclasses as a default implementation for the data property. Note that subclasses should typically memoize the result to avoid recomputing.
from_result
@classmethod
from_result( result: 'cirq.Result', *, job_id: str, job_finished_time: datetime.datetime )
from_single_parameter_set
@staticmethod
from_single_parameter_set( *, params: resolver.ParamResolver, measurements: Mapping[str, np.ndarray] ) > 'cirq.Result'
THIS FUNCTION IS DEPRECATED.
IT WILL BE REMOVED IN cirq v0.15
.
The static method from_single_parameter_set is deprecated. Use the ResultDict constructor instead.
Packages runs of a single parameterized circuit into a Result.
Args:
params: A ParamResolver of settings used for this result.
measurements: A dictionary from measurement gate key to measurement
results. The value for each key is a 2D array of booleans,
with the first index running over the repetitions, and the
second index running over the qubits for the corresponding
measurements.
histogram
histogram(
*,
key: TMeasurementKey,
fold_func: Callable[[Tuple], T] = cast(Callable[[Tuple], T], value.big_endian_bits_to_int)
) > collections.Counter
Counts the number of times a measurement result occurred.
For example, suppose that:
 fold_func is not specified
 key='abc'
 the measurement with key 'abc' measures qubits a, b, and c.
 the circuit was sampled 3 times.
 the sampled measurement values were:
1. a=1 b=0 c=0
2. a=0 b=1 c=0
3. a=1 b=0 c=0
Then the counter returned by this method will be:
collections.Counter({
0b100: 2,
0b010: 1
})
Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice that the bits are combined in a bigendian way by default, with the first measured qubit determining the highestvalue bit.
Args  

key

Keys of measurements to include in the histogram. 
fold_func

A function used to convert a sampled measurement result into a countable value. The input is a list of bits sampled together by a measurement. If this argument is not specified, it defaults to interpreting the bits as a big endian integer. 
Returns  

A counter indicating how often a measurement sampled various results. 
multi_measurement_histogram
multi_measurement_histogram(
*,
keys: Iterable[TMeasurementKey],
fold_func: Callable[[Tuple], T] = cast(Callable[[Tuple], T], _tuple_of_big_endian_int)
) > collections.Counter
Counts the number of times combined measurement results occurred.
This is a more general version of the 'histogram' method. Instead of only counting how often results occurred for one specific measurement, this method tensors multiple measurement results together and counts how often the combined results occurred.
For example, suppose that:
 fold_func is not specified
 keys=['abc', 'd']
 the measurement with key 'abc' measures qubits a, b, and c.
 the measurement with key 'd' measures qubit d.
 the circuit was sampled 3 times.
 the sampled measurement values were:
1. a=1 b=0 c=0 d=0
2. a=0 b=1 c=0 d=1
3. a=1 b=0 c=0 d=0
Then the counter returned by this method will be:
collections.Counter({
(0b100, 0): 2,
(0b010, 1): 1
})
Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice that the bits are combined in a bigendian way by default, with the first measured qubit determining the highestvalue bit.
Args  

fold_func

A function used to convert sampled measurement results into countable values. The input is a tuple containing the list of bits measured by each measurement specified by the keys argument. If this argument is not specified, it defaults to returning tuples of integers, where each integer is the big endian interpretation of the bits a measurement sampled. 
keys

Keys of measurements to include in the histogram. 
Returns  

A counter indicating how often measurements sampled various results. 
__add__
__add__(
other: 'cirq.Result'
) > 'cirq.Result'
__eq__
__eq__(
other
)
Return self==value.