service_time_with_no_showsThis notebook provides documentation for the function service_time_with_no_shows and includes a unit test to verify its functionality.
service_time_with_no_shows(s: List[float], q: float) -> List[float]
Adjusts a distribution of service times to account for no-shows. The function scales the original service time distribution by the probability of a patient showing up (i.e., 1 - q) and then adds the no-show probability q to the service time for zero time slots.
List[float]): The original service time probability distribution. This list represents the probabilities associated with different service times.float): The probability of no-shows. This value should be between 0 and 1.List[float]: The adjusted service time probability distribution where the no-show probability has been incorporated into the probability of zero service time.from functions import service_time_with_no_shows
# Example usage
original_distribution = [0.0, 0.5, 0.3, 0.2]
no_show_probability = 0.1
adjusted_distribution = service_time_with_no_shows(original_distribution, no_show_probability)
print("Adjusted distribution:", adjusted_distribution)Adjusted distribution: [0.1, 0.45, 0.27, 0.18000000000000002]import unittest
class TestServiceTimeWithNoShows(unittest.TestCase):
def test_adjust_distribution(self):
# Test with a known distribution and no-show probability
original_distribution = [0.0, 0.5, 0.3, 0.2]
no_show_probability = 0.1
# Expected adjustment: second element 0.1,
# other elements: multiplied by 0.9
expected_distribution = [0.1, 0.45, 0.27, 0.18]
result = service_time_with_no_shows(original_distribution, no_show_probability)
# Using almost equal check due to floating point arithmetic
for r, e in zip(result, expected_distribution):
self.assertAlmostEqual(r, e, places=5)
if __name__ == '__main__':
unittest.main(argv=[''], exit=False).
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Ran 1 test in 0.001s
OK