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How to use stop_lambda method in localstack
Best Python code snippet using localstack_python
simulate_contention_interval.py
Source:simulate_contention_interval.py 
1import argparse2from math import log, ceil3from random import Random4from typing import Tuple, List5import matplotlib.pyplot as plt6import numpy as np7DEFAULT_STEP_LAMBDA = 0.018DEFAULT_STOP_LAMBDA = 4.09DEFAULT_START_LAMBDA = 1.010DEFAULT_ITERATIONS: int = int(1e6)11def simulate_first_successful_transmission_time(12 lambda_: float,13 seeded_random: Random,14) -> float:15 """16 Simulate attempting to transmit packets at random intervals sampled from an exponential distribution17 with parameter lambda_ until a packet transmission is deemed to be successful by not colliding with18 any other packet within a [t-1, t+1] time interval19 :param lambda_: The parameter for the exponential distribution from which to sample the transmission intervals20 :param seeded_random: An instance of random.Random used for running simulations21 :return: The amount of simulated time in arbitrary units it took until the first successful packet transmission22 """23 def sample_interval():24 return -lambda_ * log(seeded_random.uniform(0.0, 1.0))25 # Initial parameters discussed with Marcelo Ponce26 # t is initialized randomly via sample_interval27 # the first transmission has a greater likelihood if success since there is no prior transmission to contend with28 t_prev = float('-inf')29 t = sample_interval()30 t_next = t + sample_interval()31 while t - t_prev < 1 or t_next - t < 1:32 t_prev = t33 t = t_next34 t_next += sample_interval()35 return t36def simulate_average_contention_interval(37 lambda_: float,38 iterations: int,39 seeded_random: Random,40) -> float:41 """42 Run a simulation to determine the average contention interval for the given lambda value43 :param lambda_: The lambda value for which to run the contention interval simulation44 :param iterations: The number of trials to use to determine the average contention interval45 :param seeded_random: An instance of random.Random used for running the simulation46 :return: the average contention interval for the given lambda value47 """48 avg_contention_interval = np.average(49 [50 simulate_first_successful_transmission_time(lambda_, seeded_random)51 for _ in range(iterations)52 ]53 )54 print(f'λ={lambda_} --> {avg_contention_interval}')55 return avg_contention_interval56def find_optimal_lambda(57 iterations: int,58 seeded_random: Random,59 start_lambda: float,60 stop_lambda: float,61 step_lambda: float,62) -> Tuple[float, float]:63 """64 Run a contention interval simulation for each lambda value in the given range and return the minimum average65 contention interval and associated optimal lambda value. Plot the lambda values vs contention intervals.66 :param iterations: The number of times to simulate finding the contention interval for each lambda value67 :param seeded_random: An instance of random.Random used for running simulations68 :param start_lambda: The initial lambda value to simulate for69 :param stop_lambda: The final lambda value (inclusive) to simulate for70 :param step_lambda: The approximate step size between lambda values71 :return: A tuple of (optimal lambda value, minimum average contention interval)72 """73 # Run simulation for range of lambdas74 num_lambdas: int = ceil(((stop_lambda - start_lambda) / step_lambda))75 lambdas: List[float] = list(76 np.linspace(start=start_lambda, stop=stop_lambda, num=num_lambdas)77 )78 avg_contention_intervals: List[float] = [79 simulate_average_contention_interval(lambda_, iterations, seeded_random)80 for lambda_ in lambdas81 ]82 # identify optimal lambda and minimum interval83 i: int = np.argmin(avg_contention_intervals)84 optimal_lambda: float = lambdas[i]85 min_avg_contention_interval: float = avg_contention_intervals[i]86 # plot simulation and minimum interval87 fig = plt.figure()88 ax = fig.add_subplot(111)89 plt.plot(lambdas, avg_contention_intervals, 'r-')90 plt.plot(optimal_lambda, min_avg_contention_interval, 'bo')91 ax.annotate(92 f'Optimal λ: {optimal_lambda:.4f}\nMinimum Contention Interval: {min_avg_contention_interval:.4f}',93 xy=(optimal_lambda, min_avg_contention_interval),94 xytext=(10, -25),95 textcoords='offset points',96 )97 ax.set_ylim(bottom=min_avg_contention_interval - 0.4)98 plt.xlabel('λ')99 plt.ylabel('Contention interval (Arbitrary Units)')100 plt.show()101 return optimal_lambda, min_avg_contention_interval102def _get_args() -> argparse.Namespace:103 """104 Parse the arguments and perform basic validation105 :return: an argparse.Namespace containing the parsed arguments106 """107 parser: argparse.ArgumentParser = argparse.ArgumentParser()108 parser.add_argument(109 '-l',110 '--lambda',111 type=float,112 default=None,113 dest='lambda_',114 metavar='LAMBDA',115 help='λ parameter for to exponential distribution of intervals. Overrides all other lambda related options.',116 )117 parser.add_argument(118 '--start-lambda',119 type=float,120 default=DEFAULT_START_LAMBDA,121 help='The initial λ value to simulate for',122 )123 parser.add_argument(124 '--stop-lambda',125 type=float,126 default=DEFAULT_STOP_LAMBDA,127 help='The final λ value to simulate for',128 )129 parser.add_argument(130 '--step-lambda',131 type=float,132 default=DEFAULT_STEP_LAMBDA,133 help='The approximate step size between the λ values to simulate for',134 )135 parser.add_argument(136 '-i',137 '--iterations',138 type=int,139 default=DEFAULT_ITERATIONS,140 help='number of times to run the simulation for each λ value before averaging',141 )142 parser.add_argument(143 '-s',144 '--seed',145 type=int,146 default=None,147 help='seed for PRNG used to calculate random contention intervals',148 )149 args: argparse.Namespace = parser.parse_args()150 if args.lambda_ is not None:151 assert isinstance(args.lambda_, float)152 else:153 assert isinstance(args.start_lambda, float)154 assert isinstance(args.stop_lambda, float)155 assert isinstance(args.step_lambda, float)156 assert isinstance(args.iterations, int)157 assert args.seed is None or isinstance(args.seed, int)158 return args159def _create_random(seed: int) -> Random:160 """161 Create an instance of random.Random seeded with seed and advanced through 1248 iterations. Note that the Mersenne162 Twister pseudorandom number generator is used for all random operations163 :param seed: The seed to the Mersenne Twister pseudorandom number generator164 :return: An instance of random.Random seeded with seed and advanced through 1248 iterations165 """166 seeded_random: Random = Random()167 seeded_random.seed(seed)168 # throw away first 624*2 outputs of Mersenne Twister as recommended in https://stats.stackexchange.com/a/438057169 for _ in range(1248):170 seeded_random.random()171 return seeded_random172def main():173 """174 Parse the input arguments and initiate the simulation175 """176 args: argparse.Namespace = _get_args()177 seeded_random = _create_random(args.seed)178 if args.lambda_ is not None:179 # Simulate given value of lambda180 avg_contention_interval = simulate_average_contention_interval(181 lambda_=args.lambda_,182 iterations=args.iterations,183 seeded_random=seeded_random,184 )185 print(186 f'The average contention interval for λ={args.lambda_:.4f} is {avg_contention_interval:.4f}'187 )188 else:189 # Find optimal lambda in range [start_lambda, stop_lambda] with granularity of step_lambda190 opt_lambda, avg_contention_interval = find_optimal_lambda(191 iterations=args.iterations,192 seeded_random=seeded_random,193 start_lambda=args.start_lambda,194 stop_lambda=args.stop_lambda,195 step_lambda=args.step_lambda,196 )197 print(198 f'The optimal lambda value is λ={opt_lambda:.4f} with an average contention interval of {avg_contention_interval:.4f}'199 )200if __name__ == '__main__':...
app.py
Source:app.py
...20 send_notification_to_ms_teams("Start cluster response {}".format(response))21# -----------------------STOP SERVICE-------------------------------#22# Run at 1:30pm (UTC) (i.e 07:00pm [IST]) every Monday through Friday.23@app.schedule(Cron(30, 13, '?', '*', 'MON-FRI', '*'))24def stop_lambda(event):25 print("Stopping cluster")26 try:27 response = rds.stop_db_cluster(DBClusterIdentifier=os.environ.get("DBClusterIdentifier"))28 except Exception as ex:29 err = "Exception occurred on stopping neptune due to {}".format(ex)30 print(err)31 send_notification_to_ms_teams(err)32 print('Stopped your cluster: ' + str(response))33 # Sending notification to Microsoft Team34 send_notification_to_ms_teams("Stop cluster response {}".format(response))35@app.schedule(Cron(30, 17, '?', '*', 'MON-FRI', '*'))36def stop_neptune_at_11_pm(event):37 stop_lambda(event)38# Sending notification to Microsoft Team39def send_notification_to_ms_teams(message):40 message += "<----->\n Posted using repo -----> https://github.com/m-thirumal/aws-service-start-stop-scheduler"41 print("Sending message {} to MS Teams".format(message))42 ms_teams_message = pymsteams.connectorcard(os.environ.get('msTeamChannelIncomingWebhook'))43 ms_teams_message.text(message)...
cav_internal.py
Source:cav_internal.py
1import numpy as np2import matplotlib.pyplot as plt3class FabryPerotCavity(object):4 def __init__(self):5 #FP settings6 self.R1 = 0.997 self.R2 = 0.998 self.d = 1E6 #m9 self.c = 3E8 #m/s10 # create frequency arrays: optical regime first11 self.start_lambda = 300E-9 #m12 self.stop_lambda = 800E-9 #m13 self.step_lambda = 0.0001E-9 #m Make sure this is small enough ! otherwise resonances don't show correctly14 self.lambdas = np.arange( self.start_lambda, self.stop_lambda + self.step_lambda , self.step_lambda )15 self.omegas = ( 2 * np.pi ) / self.lambdas16 def CalculateInternalCavityField(self):17 '''Calculate P_cav / P_in '''18 phi_array = ( self.omegas * self.d ) / self.c19 power_ratios = ( 1 - self.R1 ) / ( np.absolute( 1 + ( np.exp( 1j * phi_array ) ) * np.sqrt( self.R1 * self.R2 ) )**2 )20 return phi_array, power_ratios21if __name__ == '__main__':22 FPcav = FabryPerotCavity()23 phi_array, power_ratios = FPcav.CalculateInternalCavityField()24 plt.plot( phi_array , power_ratios, color = 'b' )...
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