# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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.
import warnings
from braket.ir.ahs.time_series import TimeSeries
from pydantic.v1.class_validators import root_validator
from braket.analog_hamiltonian_simulator.rydberg.validators.capabilities_constants import (
CapabilitiesConstants,
)
[docs]
class TimeSeriesValidator(TimeSeries):
capabilities: CapabilitiesConstants
# must have at least 2 time values
[docs]
@root_validator(pre=True, skip_on_failure=True)
def at_least_2_timepoints(cls, values):
times = values["times"]
length = len(times)
min_length = 2
if length < min_length:
raise ValueError(f"Length of times must be at least {min_length}; it is {length}")
return values
# the first times entry should be 0
[docs]
@root_validator(pre=True, skip_on_failure=True)
def times_start_with_0(cls, values):
times = values["times"]
if times[0] != 0.0:
raise ValueError(f"First time value is {times[0]}; it must be 0.0")
return values
# The duration of the program should be below MAX_TIME
# If not, a warning message will be issue to remind the user that the SI units are used here.
[docs]
@root_validator(pre=True, skip_on_failure=True)
def times_are_not_too_big(cls, values):
times = values["times"]
capabilities = values["capabilities"]
if times[-1] > capabilities.MAX_TIME:
warnings.warn(
f"Max time is {times[-1]} seconds which is bigger than the typical scale "
f"({capabilities.MAX_TIME} seconds). "
"The time points should be specified in SI units."
)
return values
# The time array must be sorted in ascending order
[docs]
@root_validator(pre=True, skip_on_failure=True)
def times_must_be_ascendingly_sorted(cls, values):
times = values["times"]
for i in range(len(times) - 1):
if times[i] >= times[i + 1]:
raise ValueError(
f"Time point {i} ({times[i]}) and time point {i + 1} ({times[i + 1]}) "
"must be monotonically increasing."
)
return values
# Check that the times and the values have the same length
[docs]
@root_validator(pre=True, skip_on_failure=True)
def check_times_and_values_have_same_length(cls, values):
len_times = len(values["times"])
len_values = len(values["values"])
if len_values != len_times:
raise ValueError(
f"The sample times (length: {len_times}) and the values (length: {len_values}) "
"must have the same length."
)
return values