def _generate_tas(config, channels, seed):
"""Generates a set amount of TAs in the range of data provided by parameters.
TA IDs start at 1
Note that eligible frequencies is a list of Kbps values
:param ConfigurationObject config: The Configuration for an instance of a challenge problem
:param [<Channel>] channels: The list of already generated channels to be used when assigning eligible_frequencies
:param int seed: The seed to use when evaluating PRFs
:returns [<TA>] tas: A list of TAs generated from config
"""
tas = []
for x in range(config.num_tas):
safety = PRF(config.safety)
voice = PRF(config.voice)
latency = PRF(config.latency)
scaling_factor = PRF(config.scaling_factor)
c = PRF(config.c)
eligible_frequencies = PRF(config.eligible_frequencies)
tas.append(
TA(id_='TA{0}'.format(x + 1),
minimum_voice_bandwidth=Kbps(voice.evaluate(seed)),
minimum_safety_bandwidth=Kbps(safety.evaluate(seed)),
latency=timedelta(microseconds=1000 *
int(latency.evaluate(seed))),
scaling_factor=scaling_factor.evaluate(seed),
c=c.evaluate(seed),
eligible_frequencies=list(
map(
lambda x: x.frequency,
random.sample(
channels,
eligible_frequencies.evaluate(seed))))))
return tas
def construct_ta_from_node(self, orientdb_node):
"""Constructs a TA object from an OrientDB node.
:param pyorient.otypes.OrientRecord orientdb_node: A pyorient node
:returns TA ta: A TA object constructed from the pyorient node
"""
orient_record = self.get_connected_nodes(orientdb_node._rid, direction='in', filterdepth=1)
eligible_frequencies=[]
for record in orient_record:
if record._class == 'Channel':
eligible_frequencies.append(Frequency(int(record.frequency)))
ta = TA(
id_=orientdb_node.id,
minimum_voice_bandwidth=Kbps(
float(orientdb_node.minimum_voice_bandwidth)),
minimum_safety_bandwidth=Kbps(
float(orientdb_node.minimum_safety_bandwidth)),
latency=timedelta(microseconds=1000*int(orientdb_node.latency)),
scaling_factor=float(orientdb_node.scaling_factor),
c=float(orientdb_node.c),
eligible_frequencies=eligible_frequencies)
return ta
def setUpClass(cls):
MdlId.clear()
cls.id_ = MdlId('TA1')
cls.minimum_voice_bandwidth = Kbps(100)
cls.minimum_safety_bandwidth = Kbps(75)
cls.scaling_factor = 1
cls.c = 0.05
def test_generates_correct_amount_of_tas(self):
generator = TAGenerator(lower_minimum_voice_bandwidth=Kbps(20),
upper_minimum_voice_bandwidth=Kbps(100),
lower_minimum_safety_bandwidth=Kbps(5),
upper_minimum_safety_bandwidth=Kbps(50),
lower_scaling_factor=1,
upper_scaling_factor=5,
lower_c=0.03,
upper_c=0.08,
lower_min_value=30,
upper_min_value=70)
num_tas = 100
ta_list = generator.generate(num_tas)
self.assertEqual(len(ta_list), num_tas)
def test_bandwidth_rate_init(self):
type_ = BandwidthTypes.VOICE
rate = Kbps(100)
bandwidth_rate = BandwidthRate(type_, rate)
self.assertEqual(type_, bandwidth_rate.type_)
self.assertEqual(rate, bandwidth_rate.rate)
def construct_channel_from_node(self, orientdb_node):
"""Constructs a Channel object from a pyorient node
:param pyorient.otypes.OrientRecord orientdb_node: A pyorient channel node
:returns Channel channel: A Channel object constructed from the pyorient node
"""
channel = Channel(frequency=Frequency(int(orientdb_node.frequency)),
capacity=Kbps(int(orientdb_node.capacity)))
return channel
def setUpClass(cls):
cls.valid_channel_frequency = Frequency(4919500000)
cls.valid_channel_length = time(microsecond=100000)
cls.valid_channel_latency = time(microsecond=50000)
cls.valid_channel_capacity = Kbps(100000)
cls.valid_channel = Channel(cls.valid_channel_frequency,
cls.valid_channel_length,
cls.valid_channel_latency,
cls.valid_channel_capacity)
def retrieve_constraints(self):
"""
Retrieves constraints from the provided database
:returns ConstraintsObject constraints_object: A Constraints Object
"""
orientdb_constraints_objects = self._get_system_wide_constraints()
constraints_objects = []
for orientdb_constraints_object in orientdb_constraints_objects:
connected_nodes = self.get_connected_nodes(orientdb_constraints_object._rid, filterdepth=1)
candidate_tas = []
channels = []
for node in connected_nodes:
if node._class == 'TA':
candidate_tas.append(self.construct_ta_from_node(node))
elif node._class == 'Channel':
channels.append(self.construct_channel_from_node(node))
constraints_objects.append(ConstraintsObject(
id_ = orientdb_constraints_object.id,
candidate_tas=candidate_tas,
channels=channels,
goal_throughput_bulk=BandwidthRate(
BandwidthTypes.BULK,
Kbps(int(orientdb_constraints_object.goal_throughput_bulk))
),
goal_throughput_voice=BandwidthRate(
BandwidthTypes.VOICE,
Kbps(int(orientdb_constraints_object.goal_throughput_voice))
),
goal_throughput_safety=BandwidthRate(
BandwidthTypes.SAFETY,
Kbps(int(orientdb_constraints_object.goal_throughput_safety))
),
guard_band=timedelta(microseconds=1000 * int(orientdb_constraints_object.guard_band)),
epoch=timedelta(microseconds=1000 * int(orientdb_constraints_object.epoch)),
txop_timeout=TxOpTimeout(int(orientdb_constraints_object.txop_timeout)),
seed=orientdb_constraints_object.seed))
return constraints_objects
def __init__(self,
id_,
minimum_voice_bandwidth,
minimum_safety_bandwidth,
latency,
scaling_factor,
c,
eligible_frequencies,
bandwidth=Kbps(0),
channel=None,
value=0):
"""Constructor
:param str id_: The ID of the TA.
:param Kbps minimum_voice_bandwidth: The minimum required voice bandwidth to get this TA in the air
:param Kbps minimum_safety_bandwidth: The minimum required safety bandwidth to get this TA in the air
:param timedelta latency: The maximum delay between radio transmissions
:param int scaling_factor: The amount by which to scale the overall value by onc.
:param int c: The coefficient of a sample value function. For now it's set to 1 because there is no real value
function.
:param List[Channel] eligible_frequencies: The list of channels communication is permissible over.
:param Kbps bandwidth: The amount of bandwidth assigned to this TA
:param Channel channel: The channel this TA has been assigned to communicate on
:param timedelta channel: The channel this TA has been assigned to communicate on
:param int value: The amount of value this TA provides at a some bandwidth
"""
self.id_ = id_
self.minimum_voice_bandwidth = minimum_voice_bandwidth
self.minimum_safety_bandwidth = minimum_safety_bandwidth
self.latency = latency
self.scaling_factor = float(scaling_factor)
self.c = float(c)
self.eligible_frequencies = eligible_frequencies
self.bandwidth = bandwidth
self.channel = channel
self.value = value
self.total_minimum_bandwidth = Kbps(minimum_voice_bandwidth.value +
minimum_safety_bandwidth.value)
self.min_value = self.compute_value_at_bandwidth(
self.total_minimum_bandwidth)
self.max_value = self.compute_value_at_bandwidth(MAX_BANDWIDTH)
def __init__(self,
id_,
candidate_tas,
channels,
seed,
goal_throughput_bulk=BandwidthRate(BandwidthTypes.BULK,
Kbps(100)),
goal_throughput_voice=BandwidthRate(BandwidthTypes.VOICE,
Kbps(100)),
goal_throughput_safety=BandwidthRate(BandwidthTypes.SAFETY,
Kbps(100)),
guard_band=timedelta(microseconds=1000),
epoch=timedelta(microseconds=100000),
txop_timeout=TxOpTimeout(255)):
"""
Constructor
:param BandwidthRate goal_throughput_bulk: The new value for all Bulk ServiceLevelProfiles.
:param BandwidthRate goal_throughput_voice: The new value for all Voice ServiceLevelProfiles.
:param BandwidthRate goal_throughput_safety: The new value for all Safety ServiceLevelProfiles.
:param timedelta guard_band: The unused part of the radio spectrum between radio bands to prevent interference.
:param timedelta epoch: The epoch of the MDL file.
:param TxOpTimeout txop_timeout: The timeout value for TxOp nodes
:param List[TA] candidate_tas: List of potential TA's.
:param List[Channel] channels: Channel specific data such as the amount of throughput available
:param int seed: The number that was used to seed the random generator
"""
self.goal_throughput_bulk = goal_throughput_bulk
self.goal_throughput_voice = goal_throughput_voice
self.goal_throughput_safety = goal_throughput_safety
self.guard_band = guard_band
self.epoch = epoch
self.txop_timeout = txop_timeout
self.candidate_tas = candidate_tas
self.channels = channels
self.seed = seed
self.id_ = id_
self.deadline_window = guard_band + MDL_MIN_INTERVAL
def compute_bw_from_comm_len(self,
capacity,
latency,
communication_length,
guard_band=timedelta(microseconds=0)):
"""Returns the amount of bandwidth required to allocate to this TA based on the amount of time this TA communicates for
:param timedelta communication_length: The time this TA communicates for
:returns Kbps bandwidth_required: The amount of bandwidth this TA requires to communicate
"""
bandwidth_required = (capacity.value / latency.get_microseconds()) * (
communication_length.get_microseconds() -
2 * guard_band.get_microseconds())
return Kbps(bandwidth_required)
def _discretize(self, constraints_object):
discretized_tas = []
for ta in constraints_object.candidate_tas:
for channel in constraints_object.channels:
discretization_length = int(
MAX_BANDWIDTH.value -
ta.total_minimum_bandwidth.value) / self.disc_count
for i in range(0, self.disc_count):
disc_ta = copy.deepcopy(ta)
disc_ta.channel = channel
bandwidth = Kbps(disc_ta.total_minimum_bandwidth.value +
(i * discretization_length))
disc_ta.value = ta.compute_value_at_bandwidth(bandwidth)
disc_ta.bandwidth = bandwidth
discretized_tas.append(disc_ta)
return discretized_tas
def _generate_channels(config, seed):
"""Generates a set amount of Channels in the range of data provided by data_file.
:param ConfigurationObject config: The Configuration for an instance of a challenge problem
:param int seed: The seed to use when evaluating PRFs
:returns [<Channel>] channels: A list of channels generated from config
"""
channels = []
for i in range(0, config.num_channels):
base_frequency = config.frequency[0]
frequency_incrementation = config.frequency[1]
capacity = PRF(config.capacity)
channels.append(
Channel(frequency=Frequency(base_frequency +
(i * frequency_incrementation)),
capacity=Kbps(capacity.evaluate(seed))))
return channels
def generate(config):
"""Generates a ConstraintsObject within the parameters specified by a ConfigurationObject
:param ConfigurationObject config: The Configuration for an instance of a challenge problem
:returns ConstraintsObject:
"""
constraints_object_list = []
if config.testing == 1:
if config.testing_seed != 'timestamp':
seed = config.testing_seed
numpy.random.seed(seed)
random.seed(seed)
channel_list = []
for i in range(config.testing_num_channels):
channel = Channel(frequency=Frequency(4919 + i * 22),
capacity=Kbps(
config.testing_channel_capacity))
channel_list.append(channel)
ta_list = []
for i in range(config.testing_num_tas):
eligible_frequency_list = []
for j in range(config.testing_eligible_frequencies[i]):
eligible_frequency_list.append(channel_list[j].frequency)
ta = TA(id_='TA{0}'.format(i + 1),
minimum_voice_bandwidth=Kbps(
int(config.testing_total_min_bw[i]) / 2),
minimum_safety_bandwidth=Kbps(
int(config.testing_total_min_bw[i]) / 2),
latency=timedelta(microseconds=1000 *
int(config.testing_latency[i])),
scaling_factor=config.testing_scaling_factor[i],
c=config.testing_c[i],
eligible_frequencies=eligible_frequency_list)
ta_list.append(ta)
testing_constraints_object = ConstraintsObject(
id_='TestingConstraintsObject',
candidate_tas=ta_list,
channels=channel_list,
seed='timestamp',
goal_throughput_bulk=Kbps(config.goal_throughput_bulk),
goal_throughput_voice=Kbps(config.goal_throughput_voice),
goal_throughput_safety=Kbps(config.goal_throughput_safety),
guard_band=timedelta(microseconds=1000 *
int(config.guard_band)),
epoch=timedelta(microseconds=1000 * int(config.epoch)),
txop_timeout=TxOpTimeout(config.txop_timeout))
constraints_object_list.append(testing_constraints_object)
else:
if len(config.instances) == 2:
seed = config.instances[1]
numpy.random.seed(seed)
random.seed(seed)
else:
seed = 'timestamp'
for x in range(1, config.instances[0] + 1):
channels = ConstraintsObjectGenerator._generate_channels(
config, seed)
candidate_tas = ConstraintsObjectGenerator._generate_tas(
config, channels, seed)
constraints_object = ConstraintsObject(
id_=x,
candidate_tas=candidate_tas,
channels=channels,
seed=seed,
goal_throughput_bulk=Kbps(config.goal_throughput_bulk),
goal_throughput_voice=Kbps(config.goal_throughput_voice),
goal_throughput_safety=Kbps(config.goal_throughput_safety),
guard_band=timedelta(microseconds=1000 *
int(config.guard_band)),
epoch=timedelta(microseconds=1000 * int(config.epoch)),
txop_timeout=TxOpTimeout(config.txop_timeout))
constraints_object_list.append(constraints_object)
if isinstance(seed, int):
seed += 1
return constraints_object_list
def test_compute_value_caps_at_2000(self):
ta = TA(self.id_, Kbps(2000), Kbps(2000), self.scaling_factor, self.c)
ta2 = TA(self.id_, Kbps(2000), Kbps(0), self.scaling_factor, self.c)
self.assertEqual(ta.min_value, ta2.min_value)
self.assertEqual(100, ta.min_value)
def perturb_constraints_object(self, constraints_object, optimizer_result,
lower_bound_optimizer_result):
"""Perturbs an optimization result based on the perturbations selected.
There are three possible perturbations:
1. If ta_bandwidth is set to any value other than 0, 1 random TA
will have it's bandwidth value perturbed by the specified amount.
2. If channel_dropoff is greater than 0, channel_dropoff number of TAs will be
dropped from their channels until there are no TAs left to drop.
3. If channel_capacity is set to any value other than 0, one random
channel will have it's capacity increased or decreased by the specified
amount.
:param OptimizerResult optimizer_result: An unperturbed optimization result
:param OptimizerResult lower_bound_optimizer_result: The result of the lower bound optimizaztion to use when computing averages
:returns OptimizerResult: A perturbed optimization result
"""
optimizer_result = deepcopy(optimizer_result)
lower_bound_optimizer_result = deepcopy(lower_bound_optimizer_result)
# Increases the total_minimum_bandwidth value of a TA by the selected
# amount in the ConfigurationObject
if self.ta_bandwidth != 0:
for or_ in [lower_bound_optimizer_result, optimizer_result]:
ta_to_perturb = self._select_random_scheduled_ta(
or_.scheduled_tas, constraints_object.seed)
if or_ == optimizer_result:
logger.debug(
'PERTURBATION: Changing {0} minimum bandwidth by {1}'.
format(ta_to_perturb.id_, self.ta_bandwidth))
if ta_to_perturb.total_minimum_bandwidth.value - self.ta_bandwidth < 0:
self.ta_bandwidth = ta_to_perturb.total_minimum_bandwidth.value
ta_to_perturb.total_minimum_bandwidth.value += self.ta_bandwidth
ta_to_perturb.minimum_safety_bandwidth.value += (
self.ta_bandwidth / 2)
ta_to_perturb.minimum_voice_bandwidth.value += (
self.ta_bandwidth / 2)
ta_to_perturb.min_value = ta_to_perturb.compute_value_at_bandwidth(
ta_to_perturb.total_minimum_bandwidth)
# This is a workaround for the issue in floating point precision
# in Python. Here I am setting the result to 0 if the two
# floats are equal. Otherwise due to floating point precision
# you get a value other than 0.
if or_ == lower_bound_optimizer_result:
if lower_bound_optimizer_result.value == ta_to_perturb.value:
lower_bound_optimizer_result.value = 0
lower_bound_optimizer_result.scheduled_tas = []
else:
lower_bound_optimizer_result.value -= ta_to_perturb.value
lower_bound_optimizer_result.scheduled_tas.remove(
ta_to_perturb)
for channel, ta_list in lower_bound_optimizer_result.scheduled_tas_by_channel.items(
):
for ta in ta_list:
if ta == ta_to_perturb:
ta_list.remove(ta)
# Randomly selects one channel to drop from the original
# ConstraintsObject
if self.channel_dropoff > 0:
for or_ in [lower_bound_optimizer_result, optimizer_result]:
dropped_tas = []
for i in range(self.channel_dropoff):
if len(dropped_tas) == len(or_.scheduled_tas):
logger.debug(
'All scheduled TAs have been dropped from their channels. The number of TAs you have requested to drop ({0}) has exceeded the amount of TAs that have been scheduled ({1}).'
.format(self.channel_dropoff,
len(or_.scheduled_tas)))
break
else:
ta_to_perturb = self._select_random_scheduled_ta([
ta for ta in or_.scheduled_tas
if ta not in dropped_tas
], constraints_object.seed)
dropped_tas.append(ta_to_perturb)
if or_ == optimizer_result:
logger.debug(
'PERTURBATION: Removing {0} from {1}'.format(
ta_to_perturb.id_,
ta_to_perturb.channel.frequency.value))
ta_to_perturb.eligible_frequencies.remove(
ta_to_perturb.channel.frequency)
if or_ == lower_bound_optimizer_result:
previously_scheduled_tas = deepcopy(or_.scheduled_tas)
or_.scheduled_tas = [
ta for ta in previously_scheduled_tas
if ta not in dropped_tas
]
for channel, ta_list in or_.scheduled_tas_by_channel.items(
):
old_ta_list = deepcopy(ta_list)
or_.scheduled_tas_by_channel[channel] = [
ta for ta in old_ta_list if ta not in dropped_tas
]
or_.value = sum([ta.value for ta in or_.scheduled_tas])
# Increases or Decreases the capacity of one random channel
if self.channel_capacity != 0:
for or_ in [lower_bound_optimizer_result, optimizer_result]:
ta_to_perturb = self._select_random_scheduled_ta(
or_.scheduled_tas, constraints_object.seed)
channel_to_perturb = next(
channel for channel in constraints_object.channels
if channel == ta_to_perturb.channel)
if or_ == optimizer_result:
logger.debug(
'PERTURBATION: Changing channel {0} capacity by {1}'.
format(ta_to_perturb.channel.frequency.value,
self.channel_capacity))
channel_to_perturb.capacity.value += self.channel_capacity
# If the amount of bandwidth to reduce the channel by exceeds the
# channel's capacity. Remove that channel, an the scheduled TAs on it.
if channel_to_perturb.capacity.value <= 0:
for ta in or_.scheduled_tas:
tas_to_remove = []
if ta.channel.frequency.value == channel_to_perturb.frequency.value:
tas_to_remove.append(ta)
or_.value -= ta.value
for channel, ta_list in or_.scheduled_tas_by_channel.items(
):
if channel == channel_to_perturb:
or_.scheduled_tas_by_channel[channel] = []
constraints_object.channels.remove(channel_to_perturb)
previously_scheduled_tas = deepcopy(or_.scheduled_tas)
or_.scheduled_tas = [
ta for ta in previously_scheduled_tas
if ta not in tas_to_remove
]
# Update the value if you're dealing with a greedy optimization
if or_ == lower_bound_optimizer_result:
or_.value = sum([ta.value for ta in or_.scheduled_tas])
if or_ == lower_bound_optimizer_result:
if self.channel_capacity < 0:
tas_to_remove = []
for ta in or_.scheduled_tas:
if ta.channel.frequency.value == channel_to_perturb.frequency.value:
tas_to_remove.append(ta)
or_.value -= ta.value
for channel, ta_list in or_.scheduled_tas_by_channel.items(
):
if channel == channel_to_perturb:
or_.scheduled_tas_by_channel[channel] = []
previously_scheduled_tas = deepcopy(or_.scheduled_tas)
or_.scheduled_tas = [
ta for ta in previously_scheduled_tas
if ta not in tas_to_remove
]
# Randomly selects reconsider from the list of unscheduled TAs
unscheduled_tas = [
ta for ta in constraints_object.candidate_tas
if ta.id_ not in [x.id_ for x in optimizer_result.scheduled_tas]
]
# The new candidate_tas for a ConstraintsObject is reconsider
# unscheduled TAs and the scheduled TAs
randomly_sampled_tas = []
try:
randomly_sampled_tas = random.sample(unscheduled_tas,
self.reconsider)
except ValueError:
if len(unscheduled_tas) == 0:
logger.debug(
"""There are 0 unscheduled TAs to reconsider. The optimizer will attempt to resolve the perturbation using the currently scheduled TAs."""
)
else:
logger.debug(
f"""The amount of TAs to reconsider ({self.reconsider}) is greater than the number of unscheduled TAs ({len(unscheduled_tas)}). Only able to reconsider {len(unscheduled_tas)} TA{'s' if len(unscheduled_tas) > 1 else ''}."""
)
# Reset the bandwidth and value assignments of TAs between runs
for ta in optimizer_result.scheduled_tas:
ta.value = 0
ta.bandwidth = Kbps(0)
scheduled_and_randomly_sampled_tas = optimizer_result.scheduled_tas + randomly_sampled_tas
# Perturbed constraints objects are identical to original constraints,
# just different candidate_tas and id
constraints_object.candidate_tas = scheduled_and_randomly_sampled_tas
# Just in case the perturbations have sent this value beneath 0
if lower_bound_optimizer_result.value < 0:
lower_bound_optimizer_result.value = 0
if len(lower_bound_optimizer_result.scheduled_tas) < 0:
lower_bound_optimizer_result.scheduled_tas = []
return (constraints_object, lower_bound_optimizer_result)
def test__eq__(self):
kbps1 = Kbps(100)
kbps2 = Kbps(100)
self.assertEqual(kbps1, kbps2)
def test_to_bits_per_second(self):
kbps = Kbps(100)
self.assertEqual(100000, kbps.to_bits_per_second())
def test_compute_value_at_minimum(self):
ta = TA(self.id_, Kbps(200), Kbps(0), self.scaling_factor, self.c)
value = ta.compute_value(200)
self.assertTrue(value >= 99.995 and value <= 99.996)
def test_compute_value_1750_03_001(self):
ta = TA(self.id_, Kbps(1500), Kbps(250), 0.3, 0.001)
self.assertTrue(ta.min_value >= 24.786 and ta.min_value <= 24.787)
def test_compute_value_1500_08_0001(self):
ta = TA(self.id_, Kbps(1500), Kbps(0), 0.8, 0.001)
self.assertTrue(ta.min_value >= 62.149 and ta.min_value <= 62.150)
def test_compute_value_1250_06_0009(self):
ta = TA(self.id_, Kbps(500), Kbps(750), 0.6, 0.009)
self.assertTrue(ta.min_value >= 59.999 and ta.min_value <= 60)
def test_compute_value_below_minimum_bandwidth(self):
ta = TA(self.id_, Kbps(200), Kbps(0), self.scaling_factor, self.c)
self.assertEqual(0, ta.compute_value(1))
def test_bandwidth_rate_invalid_type(self):
self.assertRaises(BandwidthRateInitializationException, BandwidthRate,
'foo', Kbps(100))
def test_compute_value_at_500_05_0002(self):
ta = TA(self.id_, Kbps(500), Kbps(0), 0.5, 0.002)
self.assertTrue(ta.min_value >= 31.606 and ta.min_value <= 31.607)
def test_valid_init(self):
value = 10000
kbps = Kbps(value)
self.assertEqual(value, kbps.value)
def test_compute_value_at_750_01_0004(self):
ta = TA(self.id_, Kbps(750), Kbps(0), 0.1, 0.004)
self.assertTrue(ta.min_value >= 9.502 and ta.min_value <= 9.503)
def test_compute_value_negative(self):
ta = TA(self.id_, Kbps(200), Kbps(0), self.scaling_factor, self.c)
self.assertRaises(ComputeValueException, ta.compute_value, -1)
def test___eq__(self):
b1 = BandwidthRate(BandwidthTypes.VOICE, Kbps(100))
b2 = BandwidthRate(BandwidthTypes.VOICE, Kbps(100))
self.assertEqual(b1, b2)
def test_compute_value_at_1000_025_0007(self):
ta = TA(self.id_, Kbps(1000), Kbps(0), 0.25, 0.007)
self.assertTrue(ta.min_value >= 24.977 and ta.min_value <= 24.978)
