Deepak Mallubhotla
986e42df4e
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34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
import dataclasses
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import logging
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import math
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_logger = logging.getLogger(__name__)
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# how many times faster than the max frequency we want to be, bigger is more accurate but 10 is probably fine
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DESIRED_THRESHOLD = 10
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@dataclasses.dataclass
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class SuperSample:
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super_dt: float
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super_sample_ratio: int
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def get_supersample(max_frequency: float, dt: float) -> SuperSample:
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# now we want to sample at least 10x faster than max_frequency, otherwise we're going to skew our statistics
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# note that this is why if performance mattered we'd be optimising this to pre-gen our flip times with poisson statistics.
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# so we want (1/dt) > 10 * max_freq
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if DESIRED_THRESHOLD * dt * max_frequency < 1:
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# can return unchanged
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_logger.debug("no supersampling needed")
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return SuperSample(super_dt=dt, super_sample_ratio=1)
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else:
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# else we want a such that a / dt > 10 * max_freq, or a > 10 * dt * max_freq, a = math.ceil(10 * dt * max_freq)
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a = math.ceil(DESIRED_THRESHOLD * dt * max_frequency)
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_logger.debug(
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f"max_frequency {max_frequency} and delta_t {dt} needs a ratio of {a}"
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)
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ret_val = SuperSample(super_dt=dt / a, super_sample_ratio=a)
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_logger.debug(ret_val)
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return ret_val
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