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@ -426,25 +426,32 @@ between the effects of a drug versus a placebo::
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Simulation of arrival times and service deliveries in a single server queue::
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Simulation of arrival times and service deliveries in a single server queue::
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from random import gauss, expovariate
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from random import expovariate, gauss
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from statistics import mean, median, stdev
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average_arrival_interval = 5.6
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average_arrival_interval = 5.6
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average_service_time = 5.0
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average_service_time = 5.0
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stdev_service_time = 0.5
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stdev_service_time = 0.5
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num_waiting = 0
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num_waiting = 0
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arrivals = []
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starts = []
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arrival = service_end = 0.0
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arrival = service_end = 0.0
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for i in range(20000):
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for i in range(20000):
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if arrival <= service_end:
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if arrival <= service_end:
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num_waiting += 1
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num_waiting += 1
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arrival += expovariate(1.0 / average_arrival_interval)
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arrival += expovariate(1.0 / average_arrival_interval)
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print(f'{arrival:6.1f} arrived')
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arrivals.append(arrival)
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else:
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else:
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num_waiting -= 1
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num_waiting -= 1
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service_start = service_end if num_waiting else arrival
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service_start = service_end if num_waiting else arrival
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service_time = gauss(average_service_time, stdev_service_time)
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service_time = gauss(average_service_time, stdev_service_time)
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service_end = service_start + service_time
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service_end = service_start + service_time
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print(f'\t\t{service_start:.1f} to {service_end:.1f} serviced')
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starts.append(service_start)
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waits = [start - arrival for arrival, start in zip(arrivals, starts)]
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print(f'Mean wait: {mean(waits):.1f}. Stdev wait: {stdev(waits):.1f}.')
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print(f'Median wait: {median(waits):.1f}. Max wait: {max(waits):.1f}.')
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.. seealso::
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.. seealso::
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