feat: removes legacy bayes run, technically breaking but just don't use them
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@ -1,10 +1,7 @@
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import logging
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from deepdog.meta import __version__
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from deepdog.bayes_run import BayesRun
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from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
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from deepdog.real_spectrum_run import RealSpectrumRun
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from deepdog.temp_aware_real_spectrum_run import TempAwareRealSpectrumRun
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from deepdog.bayes_run_with_ss import BayesRunWithSubspaceSimulation
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def get_version():
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@ -13,11 +10,8 @@ def get_version():
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__all__ = [
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"get_version",
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"BayesRun",
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"BayesRunSimulPairs",
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"RealSpectrumRun",
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"TempAwareRealSpectrumRun",
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"BayesRunWithSubspaceSimulation",
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]
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@ -1,281 +0,0 @@
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import pdme.inputs
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import pdme.model
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import pdme.measurement.input_types
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import pdme.measurement.oscillating_dipole
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import pdme.util.fast_v_calc
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import pdme.util.fast_nonlocal_spectrum
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from typing import Sequence, Tuple, List
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import datetime
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import csv
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import multiprocessing
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import logging
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import numpy
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# TODO: remove hardcode
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CHUNKSIZE = 50
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# TODO: It's garbage to have this here duplicated from pdme.
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DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
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def get_a_result(input) -> int:
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model, dot_inputs, lows, highs, monte_carlo_count, max_frequency, seed = input
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rng = numpy.random.default_rng(seed)
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sample_dipoles = model.get_monte_carlo_dipole_inputs(
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monte_carlo_count, max_frequency, rng_to_use=rng
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)
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
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return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
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def get_a_result_using_pairs(input) -> int:
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(
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model,
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dot_inputs,
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pair_inputs,
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local_lows,
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local_highs,
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nonlocal_lows,
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nonlocal_highs,
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monte_carlo_count,
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max_frequency,
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) = input
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sample_dipoles = model.get_n_single_dipoles(monte_carlo_count, max_frequency)
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local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
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local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
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nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
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pair_inputs, sample_dipoles
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)
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nonlocal_matches = pdme.util.fast_v_calc.between(
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nonlocal_vals, nonlocal_lows, nonlocal_highs
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)
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combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
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return numpy.count_nonzero(combined_matches)
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class BayesRun:
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"""
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A single Bayes run for a given set of dots.
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Parameters
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----------
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dot_inputs : Sequence[DotInput]
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The dot inputs for this bayes run.
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models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
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The models to evaluate.
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actual_model : pdme.model.DipoleModel
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The model which is actually correct.
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filename_slug : str
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The filename slug to include.
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run_count: int
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The number of runs to do.
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"""
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def __init__(
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self,
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dot_positions: Sequence[numpy.typing.ArrayLike],
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frequency_range: Sequence[float],
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models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
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actual_model: pdme.model.DipoleModel,
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filename_slug: str,
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run_count: int = 100,
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low_error: float = 0.9,
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high_error: float = 1.1,
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monte_carlo_count: int = 10000,
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monte_carlo_cycles: int = 10,
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target_success: int = 100,
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max_monte_carlo_cycles_steps: int = 10,
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max_frequency: float = 20,
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end_threshold: float = None,
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chunksize: int = CHUNKSIZE,
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) -> None:
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self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
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dot_positions, frequency_range
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)
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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self.dot_inputs
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)
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self.models = [model for (_, model) in models_with_names]
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self.model_names = [name for (name, _) in models_with_names]
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self.actual_model = actual_model
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self.n: int
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try:
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self.n = self.actual_model.n # type: ignore
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except AttributeError:
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self.n = 1
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self.model_count = len(self.models)
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self.monte_carlo_count = monte_carlo_count
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self.monte_carlo_cycles = monte_carlo_cycles
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self.target_success = target_success
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self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
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self.run_count = run_count
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self.low_error = low_error
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self.high_error = high_error
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self.csv_fields = []
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for i in range(self.n):
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self.csv_fields.extend(
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[
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f"dipole_moment_{i+1}",
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f"dipole_location_{i+1}",
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f"dipole_frequency_{i+1}",
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]
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)
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self.compensate_zeros = True
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self.chunksize = chunksize
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for name in self.model_names:
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self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
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self.probabilities = [1 / self.model_count] * self.model_count
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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self.filename = f"{timestamp}-{filename_slug}.bayesrun.csv"
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self.max_frequency = max_frequency
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if end_threshold is not None:
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if 0 < end_threshold < 1:
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self.end_threshold: float = end_threshold
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self.use_end_threshold = True
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_logger.info(f"Will abort early, at {self.end_threshold}.")
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else:
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raise ValueError(
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f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
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)
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def go(self) -> None:
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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writer.writeheader()
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for run in range(1, self.run_count + 1):
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# Generate the actual dipoles
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actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
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dots = actual_dipoles.get_percent_range_dot_measurements(
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self.dot_inputs, self.low_error, self.high_error
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)
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(
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lows,
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highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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dots
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)
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_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
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# define a new seed sequence for each run
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seed_sequence = numpy.random.SeedSequence(run)
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results = []
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_logger.debug("Going to iterate over models now")
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for model_count, model in enumerate(self.models):
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_logger.debug(f"Doing model #{model_count}")
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core_count = multiprocessing.cpu_count() - 1 or 1
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with multiprocessing.Pool(core_count) as pool:
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cycle_count = 0
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cycle_success = 0
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cycles = 0
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while (cycles < self.max_monte_carlo_cycles_steps) and (
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cycle_success <= self.target_success
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):
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_logger.debug(f"Starting cycle {cycles}")
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cycles += 1
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current_success = 0
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cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
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# generate a seed from the sequence for each core.
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# note this needs to be inside the loop for monte carlo cycle steps!
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# that way we get more stuff.
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seeds = seed_sequence.spawn(self.monte_carlo_cycles)
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current_success = sum(
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pool.imap_unordered(
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get_a_result,
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[
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(
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model,
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self.dot_inputs_array,
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lows,
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highs,
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self.monte_carlo_count,
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self.max_frequency,
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seed,
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)
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for seed in seeds
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],
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self.chunksize,
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)
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)
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cycle_success += current_success
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_logger.debug(f"current running successes: {cycle_success}")
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results.append((cycle_count, cycle_success))
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_logger.debug("Done, constructing output now")
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row = {
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"dipole_moment_1": actual_dipoles.dipoles[0].p,
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"dipole_location_1": actual_dipoles.dipoles[0].s,
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"dipole_frequency_1": actual_dipoles.dipoles[0].w,
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}
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for i in range(1, self.n):
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try:
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current_dipoles = actual_dipoles.dipoles[i]
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row[f"dipole_moment_{i+1}"] = current_dipoles.p
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row[f"dipole_location_{i+1}"] = current_dipoles.s
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row[f"dipole_frequency_{i+1}"] = current_dipoles.w
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except IndexError:
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_logger.info(f"Not writing anymore, saw end after {i}")
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break
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successes: List[float] = []
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counts: List[int] = []
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for model_index, (name, (count, result)) in enumerate(
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zip(self.model_names, results)
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):
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row[f"{name}_success"] = result
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row[f"{name}_count"] = count
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successes.append(max(result, 0.5))
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counts.append(count)
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success_weight = sum(
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[
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(succ / count) * prob
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for succ, count, prob in zip(successes, counts, self.probabilities)
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]
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)
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new_probabilities = [
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(succ / count) * old_prob / success_weight
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for succ, count, old_prob in zip(successes, counts, self.probabilities)
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]
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self.probabilities = new_probabilities
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for name, probability in zip(self.model_names, self.probabilities):
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row[f"{name}_prob"] = probability
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_logger.info(row)
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(
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outfile, fieldnames=self.csv_fields, dialect="unix"
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)
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writer.writerow(row)
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if self.use_end_threshold:
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max_prob = max(self.probabilities)
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if max_prob > self.end_threshold:
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_logger.info(
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f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
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)
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break
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@ -1,382 +0,0 @@
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import pdme.inputs
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import pdme.model
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import pdme.measurement.input_types
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import pdme.measurement.oscillating_dipole
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import pdme.util.fast_v_calc
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import pdme.util.fast_nonlocal_spectrum
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from typing import Sequence, Tuple, List
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import datetime
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import csv
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import multiprocessing
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import logging
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import numpy
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import numpy.random
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# TODO: remove hardcode
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CHUNKSIZE = 50
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# TODO: It's garbage to have this here duplicated from pdme.
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DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
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def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
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(
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model,
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dot_inputs,
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pair_inputs,
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local_lows,
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local_highs,
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nonlocal_lows,
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nonlocal_highs,
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monte_carlo_count,
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monte_carlo_cycles,
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max_frequency,
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seed,
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) = input
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rng = numpy.random.default_rng(seed)
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local_total = 0
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combined_total = 0
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sample_dipoles = model.get_monte_carlo_dipole_inputs(
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monte_carlo_count, max_frequency, rng_to_use=rng
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)
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local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
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local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
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nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
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pair_inputs, sample_dipoles
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)
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nonlocal_matches = pdme.util.fast_v_calc.between(
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nonlocal_vals, nonlocal_lows, nonlocal_highs
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)
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combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
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local_total += numpy.count_nonzero(local_matches)
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combined_total += numpy.count_nonzero(combined_matches)
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return numpy.array([local_total, combined_total])
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class BayesRunSimulPairs:
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"""
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A dual pairs-nonpairs Bayes run for a given set of dots.
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Parameters
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----------
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dot_inputs : Sequence[DotInput]
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The dot inputs for this bayes run.
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models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
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The models to evaluate.
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actual_model : pdme.model.DipoleModel
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The modoel for the model which is actually correct.
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filename_slug : str
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The filename slug to include.
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run_count: int
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The number of runs to do.
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"""
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def __init__(
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self,
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dot_positions: Sequence[numpy.typing.ArrayLike],
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frequency_range: Sequence[float],
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models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
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actual_model: pdme.model.DipoleModel,
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filename_slug: str,
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run_count: int = 100,
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low_error: float = 0.9,
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high_error: float = 1.1,
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pairs_high_error=None,
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pairs_low_error=None,
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monte_carlo_count: int = 10000,
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monte_carlo_cycles: int = 10,
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target_success: int = 100,
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max_monte_carlo_cycles_steps: int = 10,
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max_frequency: float = 20,
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end_threshold: float = None,
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chunksize: int = CHUNKSIZE,
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) -> None:
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self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
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dot_positions, frequency_range
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)
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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self.dot_inputs
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)
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self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
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dot_positions, frequency_range
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)
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self.dot_pair_inputs_array = (
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pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
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)
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self.models = [mod for (_, mod) in models_with_names]
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self.model_names = [name for (name, _) in models_with_names]
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self.actual_model = actual_model
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self.n: int
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try:
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self.n = self.actual_model.n # type: ignore
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except AttributeError:
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self.n = 1
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self.model_count = len(self.models)
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self.monte_carlo_count = monte_carlo_count
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self.monte_carlo_cycles = monte_carlo_cycles
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self.target_success = target_success
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self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
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self.run_count = run_count
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self.low_error = low_error
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self.high_error = high_error
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if pairs_low_error is None:
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self.pairs_low_error = self.low_error
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else:
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self.pairs_low_error = pairs_low_error
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if pairs_high_error is None:
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self.pairs_high_error = self.high_error
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else:
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self.pairs_high_error = pairs_high_error
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self.csv_fields = []
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for i in range(self.n):
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self.csv_fields.extend(
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[
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f"dipole_moment_{i+1}",
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f"dipole_location_{i+1}",
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f"dipole_frequency_{i+1}",
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]
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)
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self.compensate_zeros = True
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self.chunksize = chunksize
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for name in self.model_names:
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self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
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self.probabilities_no_pairs = [1 / self.model_count] * self.model_count
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self.probabilities_pairs = [1 / self.model_count] * self.model_count
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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self.filename_pairs = f"{timestamp}-{filename_slug}.simulpairs.yespairs.csv"
|
||||
self.filename_no_pairs = f"{timestamp}-{filename_slug}.simulpairs.noopairs.csv"
|
||||
|
||||
self.max_frequency = max_frequency
|
||||
|
||||
if end_threshold is not None:
|
||||
if 0 < end_threshold < 1:
|
||||
self.end_threshold: float = end_threshold
|
||||
self.use_end_threshold = True
|
||||
_logger.info(f"Will abort early, at {self.end_threshold}.")
|
||||
else:
|
||||
raise ValueError(
|
||||
f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
|
||||
)
|
||||
|
||||
def go(self) -> None:
|
||||
with open(self.filename_pairs, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
||||
writer.writeheader()
|
||||
with open(self.filename_no_pairs, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
||||
writer.writeheader()
|
||||
|
||||
for run in range(1, self.run_count + 1):
|
||||
|
||||
# Generate the actual dipoles
|
||||
actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
|
||||
|
||||
dots = actual_dipoles.get_percent_range_dot_measurements(
|
||||
self.dot_inputs, self.low_error, self.high_error
|
||||
)
|
||||
(
|
||||
lows,
|
||||
highs,
|
||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
||||
dots
|
||||
)
|
||||
|
||||
pair_lows, pair_highs = (None, None)
|
||||
pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(
|
||||
self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error
|
||||
)
|
||||
(
|
||||
pair_lows,
|
||||
pair_highs,
|
||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
||||
pair_measurements
|
||||
)
|
||||
|
||||
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
|
||||
|
||||
# define a new seed sequence for each run
|
||||
seed_sequence = numpy.random.SeedSequence(run)
|
||||
|
||||
results_pairs = []
|
||||
results_no_pairs = []
|
||||
_logger.debug("Going to iterate over models now")
|
||||
for model_count, model in enumerate(self.models):
|
||||
_logger.debug(f"Doing model #{model_count}")
|
||||
|
||||
core_count = multiprocessing.cpu_count() - 1 or 1
|
||||
with multiprocessing.Pool(core_count) as pool:
|
||||
cycle_count = 0
|
||||
cycle_success_pairs = 0
|
||||
cycle_success_no_pairs = 0
|
||||
cycles = 0
|
||||
while (cycles < self.max_monte_carlo_cycles_steps) and (
|
||||
min(cycle_success_pairs, cycle_success_no_pairs)
|
||||
<= self.target_success
|
||||
):
|
||||
_logger.debug(f"Starting cycle {cycles}")
|
||||
|
||||
cycles += 1
|
||||
current_success_pairs = 0
|
||||
current_success_no_pairs = 0
|
||||
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
|
||||
|
||||
# generate a seed from the sequence for each core.
|
||||
# note this needs to be inside the loop for monte carlo cycle steps!
|
||||
# that way we get more stuff.
|
||||
|
||||
seeds = seed_sequence.spawn(self.monte_carlo_cycles)
|
||||
_logger.debug(f"Creating {self.monte_carlo_cycles} seeds")
|
||||
current_success_both = numpy.array(
|
||||
sum(
|
||||
pool.imap_unordered(
|
||||
get_a_simul_result_using_pairs,
|
||||
[
|
||||
(
|
||||
model,
|
||||
self.dot_inputs_array,
|
||||
self.dot_pair_inputs_array,
|
||||
lows,
|
||||
highs,
|
||||
pair_lows,
|
||||
pair_highs,
|
||||
self.monte_carlo_count,
|
||||
self.monte_carlo_cycles,
|
||||
self.max_frequency,
|
||||
seed,
|
||||
)
|
||||
for seed in seeds
|
||||
],
|
||||
self.chunksize,
|
||||
)
|
||||
)
|
||||
)
|
||||
current_success_no_pairs = current_success_both[0]
|
||||
current_success_pairs = current_success_both[1]
|
||||
|
||||
cycle_success_no_pairs += current_success_no_pairs
|
||||
cycle_success_pairs += current_success_pairs
|
||||
_logger.debug(
|
||||
f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}"
|
||||
)
|
||||
results_pairs.append((cycle_count, cycle_success_pairs))
|
||||
results_no_pairs.append((cycle_count, cycle_success_no_pairs))
|
||||
|
||||
_logger.debug("Done, constructing output now")
|
||||
row_pairs = {
|
||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
||||
}
|
||||
row_no_pairs = {
|
||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
||||
}
|
||||
for i in range(1, self.n):
|
||||
try:
|
||||
current_dipoles = actual_dipoles.dipoles[i]
|
||||
row_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
|
||||
row_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
|
||||
row_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
||||
row_no_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
|
||||
row_no_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
|
||||
row_no_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
||||
except IndexError:
|
||||
_logger.info(f"Not writing anymore, saw end after {i}")
|
||||
break
|
||||
|
||||
successes_pairs: List[float] = []
|
||||
successes_no_pairs: List[float] = []
|
||||
counts: List[int] = []
|
||||
for model_index, (
|
||||
name,
|
||||
(count_pair, result_pair),
|
||||
(count_no_pair, result_no_pair),
|
||||
) in enumerate(zip(self.model_names, results_pairs, results_no_pairs)):
|
||||
|
||||
row_pairs[f"{name}_success"] = result_pair
|
||||
row_pairs[f"{name}_count"] = count_pair
|
||||
successes_pairs.append(max(result_pair, 0.5))
|
||||
|
||||
row_no_pairs[f"{name}_success"] = result_no_pair
|
||||
row_no_pairs[f"{name}_count"] = count_no_pair
|
||||
successes_no_pairs.append(max(result_no_pair, 0.5))
|
||||
|
||||
counts.append(count_pair)
|
||||
|
||||
success_weight_pair = sum(
|
||||
[
|
||||
(succ / count) * prob
|
||||
for succ, count, prob in zip(
|
||||
successes_pairs, counts, self.probabilities_pairs
|
||||
)
|
||||
]
|
||||
)
|
||||
success_weight_no_pair = sum(
|
||||
[
|
||||
(succ / count) * prob
|
||||
for succ, count, prob in zip(
|
||||
successes_no_pairs, counts, self.probabilities_no_pairs
|
||||
)
|
||||
]
|
||||
)
|
||||
new_probabilities_pair = [
|
||||
(succ / count) * old_prob / success_weight_pair
|
||||
for succ, count, old_prob in zip(
|
||||
successes_pairs, counts, self.probabilities_pairs
|
||||
)
|
||||
]
|
||||
new_probabilities_no_pair = [
|
||||
(succ / count) * old_prob / success_weight_no_pair
|
||||
for succ, count, old_prob in zip(
|
||||
successes_no_pairs, counts, self.probabilities_no_pairs
|
||||
)
|
||||
]
|
||||
self.probabilities_pairs = new_probabilities_pair
|
||||
self.probabilities_no_pairs = new_probabilities_no_pair
|
||||
for name, probability_pair, probability_no_pair in zip(
|
||||
self.model_names, self.probabilities_pairs, self.probabilities_no_pairs
|
||||
):
|
||||
row_pairs[f"{name}_prob"] = probability_pair
|
||||
row_no_pairs[f"{name}_prob"] = probability_no_pair
|
||||
_logger.debug(row_pairs)
|
||||
_logger.debug(row_no_pairs)
|
||||
|
||||
with open(self.filename_pairs, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(
|
||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
||||
)
|
||||
writer.writerow(row_pairs)
|
||||
with open(self.filename_no_pairs, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(
|
||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
||||
)
|
||||
writer.writerow(row_no_pairs)
|
||||
|
||||
if self.use_end_threshold:
|
||||
max_prob = min(
|
||||
max(self.probabilities_pairs), max(self.probabilities_no_pairs)
|
||||
)
|
||||
if max_prob > self.end_threshold:
|
||||
_logger.info(
|
||||
f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
|
||||
)
|
||||
break
|
||||
@ -1,261 +0,0 @@
|
||||
import deepdog.subset_simulation
|
||||
import pdme.inputs
|
||||
import pdme.model
|
||||
import pdme.measurement.input_types
|
||||
import pdme.measurement.oscillating_dipole
|
||||
import pdme.util.fast_v_calc
|
||||
import pdme.util.fast_nonlocal_spectrum
|
||||
from typing import Sequence, Tuple, List, Optional
|
||||
import datetime
|
||||
import csv
|
||||
import logging
|
||||
import numpy
|
||||
import numpy.typing
|
||||
|
||||
|
||||
# TODO: remove hardcode
|
||||
CHUNKSIZE = 50
|
||||
|
||||
# TODO: It's garbage to have this here duplicated from pdme.
|
||||
DotInput = Tuple[numpy.typing.ArrayLike, float]
|
||||
|
||||
|
||||
CLAMPING_FACTOR = 10
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BayesRunWithSubspaceSimulation:
|
||||
"""
|
||||
A single Bayes run for a given set of dots.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dot_inputs : Sequence[DotInput]
|
||||
The dot inputs for this bayes run.
|
||||
|
||||
models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
|
||||
The models to evaluate.
|
||||
|
||||
actual_model : pdme.model.DipoleModel
|
||||
The model which is actually correct.
|
||||
|
||||
filename_slug : str
|
||||
The filename slug to include.
|
||||
|
||||
run_count: int
|
||||
The number of runs to do.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dot_positions: Sequence[numpy.typing.ArrayLike],
|
||||
frequency_range: Sequence[float],
|
||||
models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
|
||||
actual_model: pdme.model.DipoleModel,
|
||||
filename_slug: str,
|
||||
max_frequency: float = 20,
|
||||
end_threshold: float = None,
|
||||
run_count=100,
|
||||
chunksize: int = CHUNKSIZE,
|
||||
ss_n_c: int = 500,
|
||||
ss_n_s: int = 100,
|
||||
ss_m_max: int = 15,
|
||||
ss_target_cost: Optional[float] = None,
|
||||
ss_level_0_seed: int = 200,
|
||||
ss_mcmc_seed: int = 20,
|
||||
ss_use_adaptive_steps=True,
|
||||
ss_default_phi_step=0.01,
|
||||
ss_default_theta_step=0.01,
|
||||
ss_default_r_step=0.01,
|
||||
ss_default_w_log_step=0.01,
|
||||
ss_default_upper_w_log_step=4,
|
||||
ss_dump_last_generation=False,
|
||||
ss_initial_costs_chunk_size=100,
|
||||
write_output_to_bayesruncsv=True,
|
||||
use_timestamp_for_output=True,
|
||||
) -> None:
|
||||
self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
|
||||
dot_positions, frequency_range
|
||||
)
|
||||
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
|
||||
self.dot_inputs
|
||||
)
|
||||
|
||||
self.models_with_names = models_with_names
|
||||
self.models = [model for (_, model) in models_with_names]
|
||||
self.model_names = [name for (name, _) in models_with_names]
|
||||
self.actual_model = actual_model
|
||||
|
||||
self.n: int
|
||||
try:
|
||||
self.n = self.actual_model.n # type: ignore
|
||||
except AttributeError:
|
||||
self.n = 1
|
||||
|
||||
self.model_count = len(self.models)
|
||||
|
||||
self.csv_fields = []
|
||||
for i in range(self.n):
|
||||
self.csv_fields.extend(
|
||||
[
|
||||
f"dipole_moment_{i+1}",
|
||||
f"dipole_location_{i+1}",
|
||||
f"dipole_frequency_{i+1}",
|
||||
]
|
||||
)
|
||||
self.compensate_zeros = True
|
||||
self.chunksize = chunksize
|
||||
for name in self.model_names:
|
||||
self.csv_fields.extend([f"{name}_likelihood", f"{name}_prob"])
|
||||
|
||||
self.probabilities = [1 / self.model_count] * self.model_count
|
||||
|
||||
if use_timestamp_for_output:
|
||||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||
self.filename = f"{timestamp}-{filename_slug}.bayesrunwithss.csv"
|
||||
else:
|
||||
self.filename = f"{filename_slug}.bayesrunwithss.csv"
|
||||
self.max_frequency = max_frequency
|
||||
|
||||
if end_threshold is not None:
|
||||
if 0 < end_threshold < 1:
|
||||
self.end_threshold: float = end_threshold
|
||||
self.use_end_threshold = True
|
||||
_logger.info(f"Will abort early, at {self.end_threshold}.")
|
||||
else:
|
||||
raise ValueError(
|
||||
f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
|
||||
)
|
||||
|
||||
self.ss_n_c = ss_n_c
|
||||
self.ss_n_s = ss_n_s
|
||||
self.ss_m_max = ss_m_max
|
||||
self.ss_target_cost = ss_target_cost
|
||||
self.ss_level_0_seed = ss_level_0_seed
|
||||
self.ss_mcmc_seed = ss_mcmc_seed
|
||||
self.ss_use_adaptive_steps = ss_use_adaptive_steps
|
||||
self.ss_default_phi_step = ss_default_phi_step
|
||||
self.ss_default_theta_step = ss_default_theta_step
|
||||
self.ss_default_r_step = ss_default_r_step
|
||||
self.ss_default_w_log_step = ss_default_w_log_step
|
||||
self.ss_default_upper_w_log_step = ss_default_upper_w_log_step
|
||||
self.ss_dump_last_generation = ss_dump_last_generation
|
||||
self.ss_initial_costs_chunk_size = ss_initial_costs_chunk_size
|
||||
self.run_count = run_count
|
||||
|
||||
self.write_output_to_csv = write_output_to_bayesruncsv
|
||||
|
||||
def go(self) -> Sequence:
|
||||
|
||||
if self.write_output_to_csv:
|
||||
with open(self.filename, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(
|
||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
||||
)
|
||||
writer.writeheader()
|
||||
|
||||
return_result = []
|
||||
|
||||
for run in range(1, self.run_count + 1):
|
||||
|
||||
# Generate the actual dipoles
|
||||
actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
|
||||
|
||||
measurements = actual_dipoles.get_dot_measurements(self.dot_inputs)
|
||||
|
||||
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
|
||||
|
||||
# define a new seed sequence for each run
|
||||
|
||||
results = []
|
||||
_logger.debug("Going to iterate over models now")
|
||||
for model_count, model in enumerate(self.models_with_names):
|
||||
_logger.debug(f"Doing model #{model_count}, {model[0]}")
|
||||
subset_run = deepdog.subset_simulation.SubsetSimulation(
|
||||
model,
|
||||
self.dot_inputs,
|
||||
measurements,
|
||||
self.ss_n_c,
|
||||
self.ss_n_s,
|
||||
self.ss_m_max,
|
||||
self.ss_target_cost,
|
||||
self.ss_level_0_seed,
|
||||
self.ss_mcmc_seed,
|
||||
self.ss_use_adaptive_steps,
|
||||
self.ss_default_phi_step,
|
||||
self.ss_default_theta_step,
|
||||
self.ss_default_r_step,
|
||||
self.ss_default_w_log_step,
|
||||
self.ss_default_upper_w_log_step,
|
||||
initial_cost_chunk_size=self.ss_initial_costs_chunk_size,
|
||||
keep_probs_list=False,
|
||||
dump_last_generation_to_file=self.ss_dump_last_generation,
|
||||
)
|
||||
results.append(subset_run.execute())
|
||||
|
||||
_logger.debug("Done, constructing output now")
|
||||
row = {
|
||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
||||
}
|
||||
for i in range(1, self.n):
|
||||
try:
|
||||
current_dipoles = actual_dipoles.dipoles[i]
|
||||
row[f"dipole_moment_{i+1}"] = current_dipoles.p
|
||||
row[f"dipole_location_{i+1}"] = current_dipoles.s
|
||||
row[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
||||
except IndexError:
|
||||
_logger.info(f"Not writing anymore, saw end after {i}")
|
||||
break
|
||||
|
||||
likelihoods: List[float] = []
|
||||
|
||||
for (name, result) in zip(self.model_names, results):
|
||||
if result.over_target_likelihood is None:
|
||||
if result.lowest_likelihood is None:
|
||||
_logger.error(f"result {result} looks bad")
|
||||
clamped_likelihood = 10**-15
|
||||
else:
|
||||
clamped_likelihood = result.lowest_likelihood / CLAMPING_FACTOR
|
||||
_logger.warning(
|
||||
f"got a none result, clamping to {clamped_likelihood}"
|
||||
)
|
||||
else:
|
||||
clamped_likelihood = result.over_target_likelihood
|
||||
likelihoods.append(clamped_likelihood)
|
||||
row[f"{name}_likelihood"] = clamped_likelihood
|
||||
|
||||
success_weight = sum(
|
||||
[
|
||||
likelihood * prob
|
||||
for likelihood, prob in zip(likelihoods, self.probabilities)
|
||||
]
|
||||
)
|
||||
new_probabilities = [
|
||||
likelihood * old_prob / success_weight
|
||||
for likelihood, old_prob in zip(likelihoods, self.probabilities)
|
||||
]
|
||||
self.probabilities = new_probabilities
|
||||
for name, probability in zip(self.model_names, self.probabilities):
|
||||
row[f"{name}_prob"] = probability
|
||||
_logger.info(row)
|
||||
return_result.append(row)
|
||||
|
||||
if self.write_output_to_csv:
|
||||
with open(self.filename, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(
|
||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
||||
)
|
||||
writer.writerow(row)
|
||||
|
||||
if self.use_end_threshold:
|
||||
max_prob = max(self.probabilities)
|
||||
if max_prob > self.end_threshold:
|
||||
_logger.info(
|
||||
f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
|
||||
)
|
||||
break
|
||||
|
||||
return return_result
|
||||
@ -1,158 +0,0 @@
|
||||
import deepdog
|
||||
import logging
|
||||
import logging.config
|
||||
|
||||
import numpy.random
|
||||
|
||||
from pdme.model import (
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
|
||||
)
|
||||
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def fixed_z_model_func(
|
||||
xmin,
|
||||
xmax,
|
||||
ymin,
|
||||
ymax,
|
||||
zmin,
|
||||
zmax,
|
||||
wexp_min,
|
||||
wexp_max,
|
||||
pfixed,
|
||||
n_max,
|
||||
prob_occupancy,
|
||||
):
|
||||
return LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
|
||||
xmin,
|
||||
xmax,
|
||||
ymin,
|
||||
ymax,
|
||||
zmin,
|
||||
zmax,
|
||||
wexp_min,
|
||||
wexp_max,
|
||||
pfixed,
|
||||
0,
|
||||
0,
|
||||
n_max,
|
||||
prob_occupancy,
|
||||
)
|
||||
|
||||
|
||||
def get_model(orientation):
|
||||
model_funcs = {
|
||||
"fixedz": fixed_z_model_func,
|
||||
"free": LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
|
||||
"fixedxy": LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
|
||||
}
|
||||
model = model_funcs[orientation](
|
||||
-10,
|
||||
10,
|
||||
-17.5,
|
||||
17.5,
|
||||
5,
|
||||
7.5,
|
||||
-5,
|
||||
6.5,
|
||||
10**3,
|
||||
2,
|
||||
0.99999999,
|
||||
)
|
||||
model.n = 2
|
||||
model.rng = numpy.random.default_rng(1234)
|
||||
|
||||
return (
|
||||
f"connors_geom-5height-orientation_{orientation}-pfixexp_{3}-dipole_count_{2}",
|
||||
model,
|
||||
)
|
||||
|
||||
|
||||
def test_basic_analysis(snapshot):
|
||||
|
||||
dot_positions = [[0, 0, 0], [0, 1, 0]]
|
||||
|
||||
freqs = [1, 10, 100]
|
||||
models = []
|
||||
|
||||
orientations = ["free", "fixedxy", "fixedz"]
|
||||
for orientation in orientations:
|
||||
models.append(get_model(orientation))
|
||||
|
||||
_logger.info(f"have {len(models)} models to look at")
|
||||
if len(models) == 1:
|
||||
_logger.info(f"only one model, name: {models[0][0]}")
|
||||
|
||||
square_run = deepdog.BayesRunWithSubspaceSimulation(
|
||||
dot_positions,
|
||||
freqs,
|
||||
models,
|
||||
models[0][1],
|
||||
filename_slug="test",
|
||||
end_threshold=0.9,
|
||||
ss_n_c=5,
|
||||
ss_n_s=2,
|
||||
ss_m_max=10,
|
||||
ss_target_cost=150,
|
||||
ss_level_0_seed=200,
|
||||
ss_mcmc_seed=20,
|
||||
ss_use_adaptive_steps=True,
|
||||
ss_default_phi_step=0.01,
|
||||
ss_default_theta_step=0.01,
|
||||
ss_default_r_step=0.01,
|
||||
ss_default_w_log_step=0.01,
|
||||
ss_default_upper_w_log_step=4,
|
||||
ss_dump_last_generation=False,
|
||||
write_output_to_bayesruncsv=False,
|
||||
ss_initial_costs_chunk_size=1000,
|
||||
)
|
||||
result = square_run.go()
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_bayesss_with_tighter_cost(snapshot):
|
||||
|
||||
dot_positions = [[0, 0, 0], [0, 1, 0]]
|
||||
|
||||
freqs = [1, 10, 100]
|
||||
models = []
|
||||
|
||||
orientations = ["free", "fixedxy", "fixedz"]
|
||||
for orientation in orientations:
|
||||
models.append(get_model(orientation))
|
||||
|
||||
_logger.info(f"have {len(models)} models to look at")
|
||||
if len(models) == 1:
|
||||
_logger.info(f"only one model, name: {models[0][0]}")
|
||||
|
||||
square_run = deepdog.BayesRunWithSubspaceSimulation(
|
||||
dot_positions,
|
||||
freqs,
|
||||
models,
|
||||
models[0][1],
|
||||
filename_slug="test",
|
||||
end_threshold=0.9,
|
||||
ss_n_c=5,
|
||||
ss_n_s=2,
|
||||
ss_m_max=10,
|
||||
ss_target_cost=1.5,
|
||||
ss_level_0_seed=200,
|
||||
ss_mcmc_seed=20,
|
||||
ss_use_adaptive_steps=True,
|
||||
ss_default_phi_step=0.01,
|
||||
ss_default_theta_step=0.01,
|
||||
ss_default_r_step=0.01,
|
||||
ss_default_w_log_step=0.01,
|
||||
ss_default_upper_w_log_step=4,
|
||||
ss_dump_last_generation=False,
|
||||
write_output_to_bayesruncsv=False,
|
||||
ss_initial_costs_chunk_size=1,
|
||||
)
|
||||
result = square_run.go()
|
||||
|
||||
assert result == snapshot
|
||||
Loading…
x
Reference in New Issue
Block a user