oasislmf.pytools.gul.random¶
This file contains the utilities for generating random numbers in gulpy.
Module Contents¶
Functions¶
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Generate hash for a given group_id, event_id pair for the vulnerability pdf. |
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Generate hash for a given hazard_group_id, event_id pair for the hazard pdf. |
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Get the random generator function. |
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Generate hashes for all peril correlation groups for a given event_id. |
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Generate random numbers using the default Mersenne Twister algorithm. |
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Generate random numbers using the Latin Hypercube algorithm. |
Attributes¶
- oasislmf.pytools.gul.random.generate_hash(group_id, event_id, base_seed=0)[source]¶
Generate hash for a given group_id, event_id pair for the vulnerability pdf.
- Args:
group_id (int): group id. event_id (int]): event id. base_seed (int, optional): base random seed. Defaults to 0.
- Returns:
int64: hash
- oasislmf.pytools.gul.random.generate_hash_hazard(hazard_group_id, event_id, base_seed=0)[source]¶
Generate hash for a given hazard_group_id, event_id pair for the hazard pdf.
- Args:
hazard_group_id (int): group id. event_id (int]): event id. base_seed (int, optional): base random seed. Defaults to 0.
- Returns:
int64: hash
- oasislmf.pytools.gul.random.get_random_generator(random_generator)[source]¶
Get the random generator function.
- Args:
random_generator (int): random generator function id.
- Returns:
The random generator function.
Generate hashes for all peril correlation groups for a given event_id.
- Args:
unique_peril_correlation_groups (List[int]): list of the unique peril correlation groups. event_id (int): event id. base_seed (int, optional): base random seed. Defaults to 0.
- Returns:
List[int64]: hashes
- oasislmf.pytools.gul.random.get_corr_rval(x_unif, y_unif, rho, x_min, x_max, N, norm_inv_cdf, cdf_min, cdf_max, norm_cdf, Nsamples, z_unif)[source]¶
- oasislmf.pytools.gul.random.random_MersenneTwister(seeds, n, skip_seeds=0)[source]¶
Generate random numbers using the default Mersenne Twister algorithm.
- Args:
seeds (List[int64]): List of seeds. n (int): number of random samples to generate for each seed. skip_seeds (int): number of seeds to skip starting from the beginning
of the seeds array. For skipped seeds no random numbers are generated and the output rndms will contain zeros at their corresponding row. Default is 0, i.e. no seeds are skipped.
- Returns:
- rndms (array[float]): 2-d array of shape (number of seeds, n)
containing the random values generated for each seed.
- rndms_idx (Dict[int64, int]): mapping between seed and the
row in rndms that stores the corresponding random values.
- oasislmf.pytools.gul.random.random_LatinHypercube(seeds, n, skip_seeds=0)[source]¶
Generate random numbers using the Latin Hypercube algorithm.
- Args:
seeds (List[int64]): List of seeds. n (int): number of random samples to generate for each seed.
- Returns:
- rndms (array[float]): 2-d array of shape (number of seeds, n)
containing the random values generated for each seed.
- rndms_idx (Dict[int64, int]): mapping between seed and the
row in rndms that stores the corresponding random values.
- skip_seeds (int): number of seeds to skip starting from the beginning
of the seeds array. For skipped seeds no random numbers are generated and the output rndms will contain zeros at their corresponding row. Default is 0, i.e. no seeds are skipped.
- Notes:
Implementation follows scipy.stats.qmc.LatinHypercube v1.8.0. Following scipy notation, here we assume centered=False all the times: instead of taking samples=0.5*np.ones(n), here we always draw uniform random samples in order to initialise samples.