# Quellcode für qiskit.algorithms.phase_estimators.phase_estimation_result

# This code is part of Qiskit.
#
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Result of running PhaseEstimation"""

from typing import Dict, Union
import numpy
from qiskit.result import Result
from .phase_estimator import PhaseEstimatorResult

[Doku]class PhaseEstimationResult(PhaseEstimatorResult):
"""Store and manipulate results from running PhaseEstimation.

This class is instantiated by the PhaseEstimation class, not via user code.
The PhaseEstimation class generates a list of phases and corresponding weights. Upon
completion it returns the results as an instance of this class. The main method for
accessing the results is filter_phases.
"""

[Doku]    def __init__(self, num_evaluation_qubits: int,
circuit_result: Result,
phases: Union[numpy.ndarray, Dict[str, float]]) -> None:
"""
Args:
num_evaluation_qubits: number of qubits in phase-readout register.
circuit_result: result object returned by method running circuit.
phases: ndarray or dict of phases and frequencies determined by QPE.
"""
super().__init__()

self._phases = phases
# int: number of qubits in phase-readout register
self._num_evaluation_qubits = num_evaluation_qubits
self._circuit_result = circuit_result

@property
def phases(self) -> Union[numpy.ndarray, dict]:
"""Return all phases and their frequencies computed by QPE.

This is an array or dict whose values correspond to weights on bit strings.
"""
return self._phases

@property
def circuit_result(self) -> Result:
"""Return the result object returned by running the QPE circuit (on hardware or simulator).

This is useful for inspecting and troubleshooting the QPE algorithm.
"""
return self._circuit_result

@property
def most_likely_phase(self) -> float:
r"""Return the estimated phase as a number in :math:[0.0, 1.0).

1.0 corresponds to a phase of :math:2\pi. It is assumed that the input vector is an
eigenvector of the unitary so that the peak of the probability density occurs at the bit
string that most closely approximates the true phase.
"""
if isinstance(self.phases, dict):
binary_phase_string = max(self.phases, key=self.phases.get)
else:
# numpy.argmax ignores complex part of number. But, we take abs anyway
idx = numpy.argmax(abs(self.phases))
binary_phase_string = numpy.binary_repr(idx, self._num_evaluation_qubits)[::-1]
phase = _bit_string_to_phase(binary_phase_string)
return phase

[Doku]    def filter_phases(self, cutoff: float = 0.0,
as_float: bool = True) -> Dict:
"""Return a filtered dict of phases (keys) and frequencies (values).

Only phases with frequencies (counts) larger than cutoff are included.
It is assumed that the run method has been called so that the phases have been computed.
When using a noiseless, shot-based simulator to read a single phase that can
be represented exactly by num_evaluation_qubits, all the weight will
be concentrated on a single phase. In all other cases, many, or all, bit
strings will have non-zero weight. This method is useful for filtering
out these uninteresting bit strings.

Args:
cutoff: Minimum weight of number of counts required to keep a bit string.
The default value is 0.0.
as_float: If True, returned keys are floats in :math:[0.0, 1.0). If False
returned keys are bit strings.

Returns:
A filtered dict of phases (keys) and frequencies (values).
"""
if isinstance(self.phases, dict):
counts = self.phases
if as_float:
phases = {_bit_string_to_phase(k): counts[k]
for k in counts.keys() if counts[k] > cutoff}
else:
phases = {k: counts[k] for k in counts.keys() if counts[k] > cutoff}

else:
phases = {}
for idx, amplitude in enumerate(self.phases):
if amplitude > cutoff:
# Each index corresponds to a computational basis state with the LSB rightmost.
# But, we chose to apply the unitaries such that the phase is recorded
# in reverse order. So, we reverse the bitstrings here.
binary_phase_string = numpy.binary_repr(idx, self._num_evaluation_qubits)[::-1]
if as_float:
_key = _bit_string_to_phase(binary_phase_string)
else:
_key = binary_phase_string
phases[_key] = amplitude

phases = _sort_phases(phases)

return phases

def _bit_string_to_phase(binary_string: str) -> float:
"""Convert bit string to a normalized phase in :math:[0,1).

It is assumed that the bit string is correctly padded and that the order of
the bits has been reversed relative to their order when the counts
were recorded. The LSB is the right most when interpreting the bitstring as
a phase.

Args:
binary_string: A string of characters '0' and '1'.

Returns:
A phase scaled to :math:[0,1).
"""
n_qubits = len(binary_string)
return int(binary_string, 2) / (2 ** n_qubits)

def _sort_phases(phases: Dict) -> Dict:
"""Sort a dict of bit strings representing phases (keys) and frequencies (values) by bit string.

The bit strings are sorted according to increasing phase. This relies on Python
preserving insertion order when building dicts.
"""
pkeys = list(phases.keys())
pkeys.sort(reverse=False)  # Sorts in order of the integer encoded by binary string
phases = {k: phases[k] for k in pkeys}
return phases