feat!: removes alt_bayes bayes distinction, which was superfluous when only alt worked
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@ -1,307 +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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discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(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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discretisation,
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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 = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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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 AltBayesRun:
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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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discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
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The models to evaluate.
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actual_model_discretisation : pdme.model.Discretisation
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The discretisation 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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discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
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actual_model: pdme.model.Model,
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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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use_pairs: bool = False,
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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.use_pairs = use_pairs
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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.discretisations = [disc for (_, disc) in discretisations_with_names]
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self.model_names = [name for (name, _) in discretisations_with_names]
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self.actual_model = actual_model
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self.model_count = len(self.discretisations)
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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 = ["dipole_moment", "dipole_location", "dipole_frequency"]
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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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if self.use_pairs:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
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else:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.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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rng = numpy.random.default_rng()
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frequency = rng.uniform(1, self.max_frequency)
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# Generate the actual dipoles
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actual_dipoles = self.actual_model.get_dipoles(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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pair_lows, pair_highs = (None, None)
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if self.use_pairs:
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pair_measurements = (
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actual_dipoles.get_percent_range_dot_pair_measurements(
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self.dot_pair_inputs,
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self.pairs_low_error,
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self.pairs_high_error,
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)
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)
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(
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pair_lows,
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pair_highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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pair_measurements
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)
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_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
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results = []
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_logger.debug("Going to iterate over discretisations now")
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for disc_count, discretisation in enumerate(self.discretisations):
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_logger.debug(f"Doing discretisation #{disc_count}")
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with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) 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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if self.use_pairs:
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current_success = sum(
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pool.imap_unordered(
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get_a_result_using_pairs,
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[
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(
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discretisation,
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self.dot_inputs_array,
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self.dot_pair_inputs_array,
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lows,
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highs,
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pair_lows,
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pair_highs,
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self.monte_carlo_count,
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self.max_frequency,
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)
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]
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* self.monte_carlo_cycles,
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self.chunksize,
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)
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)
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else:
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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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discretisation,
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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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)
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]
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* self.monte_carlo_cycles,
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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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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": actual_dipoles.dipoles[0].p,
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"dipole_location": actual_dipoles.dipoles[0].s,
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"dipole_frequency": actual_dipoles.dipoles[0].w,
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}
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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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@ -1,17 +1,19 @@
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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 itertools
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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 scipy.optimize
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import multiprocessing
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# TODO: remove hardcode
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COST_THRESHOLD = 1e-10
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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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@ -20,13 +22,43 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
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def get_a_result(
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discretisation, dots, index
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) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
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return (index, discretisation.solve_for_index(dots, index))
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def get_a_result(input) -> int:
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discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(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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class BayesRun:
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def get_a_result_using_pairs(input) -> int:
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(
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discretisation,
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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 = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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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 AltBayesRun:
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"""
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A single Bayes run for a given set of dots.
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@ -50,29 +82,73 @@ class BayesRun:
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def __init__(
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self,
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dot_inputs: Sequence[DotInput],
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dot_positions: Sequence[numpy.typing.ArrayLike],
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frequency_range: Sequence[float],
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discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
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actual_model: pdme.model.Model,
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filename_slug: str,
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run_count: int,
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max_frequency: float = None,
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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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use_pairs: bool = False,
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) -> None:
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self.dot_inputs = dot_inputs
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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.use_pairs = use_pairs
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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.discretisations = [disc for (_, disc) in discretisations_with_names]
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self.model_names = [name for (name, _) in discretisations_with_names]
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self.actual_model = actual_model
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self.model_count = len(self.discretisations)
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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 = ["dipole_moment", "dipole_location", "dipole_frequency"]
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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}.csv"
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if self.use_pairs:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
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else:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
|
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self.max_frequency = max_frequency
|
||||
|
||||
if end_threshold is not None:
|
||||
@ -91,52 +167,114 @@ class BayesRun:
|
||||
writer.writeheader()
|
||||
|
||||
for run in range(1, self.run_count + 1):
|
||||
frequency: float = run
|
||||
if self.max_frequency is not None and self.max_frequency > 1:
|
||||
|
||||
rng = numpy.random.default_rng()
|
||||
frequency = rng.uniform(1, self.max_frequency)
|
||||
dipoles = self.actual_model.get_dipoles(frequency)
|
||||
|
||||
dots = dipoles.get_dot_measurements(self.dot_inputs)
|
||||
_logger.info(f"Going to work on dipole at {dipoles.dipoles}")
|
||||
# Generate the actual dipoles
|
||||
actual_dipoles = self.actual_model.get_dipoles(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)
|
||||
if self.use_pairs:
|
||||
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}")
|
||||
|
||||
results = []
|
||||
_logger.debug("Going to iterate over discretisations now")
|
||||
for disc_count, discretisation in enumerate(self.discretisations):
|
||||
_logger.debug(f"Doing discretisation #{disc_count}")
|
||||
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
|
||||
results.append(
|
||||
pool.starmap(
|
||||
cycle_count = 0
|
||||
cycle_success = 0
|
||||
cycles = 0
|
||||
while (cycles < self.max_monte_carlo_cycles_steps) and (
|
||||
cycle_success <= self.target_success
|
||||
):
|
||||
_logger.debug(f"Starting cycle {cycles}")
|
||||
cycles += 1
|
||||
current_success = 0
|
||||
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
|
||||
if self.use_pairs:
|
||||
current_success = sum(
|
||||
pool.imap_unordered(
|
||||
get_a_result_using_pairs,
|
||||
[
|
||||
(
|
||||
discretisation,
|
||||
self.dot_inputs_array,
|
||||
self.dot_pair_inputs_array,
|
||||
lows,
|
||||
highs,
|
||||
pair_lows,
|
||||
pair_highs,
|
||||
self.monte_carlo_count,
|
||||
self.max_frequency,
|
||||
)
|
||||
]
|
||||
* self.monte_carlo_cycles,
|
||||
self.chunksize,
|
||||
)
|
||||
)
|
||||
else:
|
||||
current_success = sum(
|
||||
pool.imap_unordered(
|
||||
get_a_result,
|
||||
zip(
|
||||
itertools.repeat(discretisation),
|
||||
itertools.repeat(dots),
|
||||
discretisation.all_indices(),
|
||||
),
|
||||
[
|
||||
(
|
||||
discretisation,
|
||||
self.dot_inputs_array,
|
||||
lows,
|
||||
highs,
|
||||
self.monte_carlo_count,
|
||||
self.max_frequency,
|
||||
)
|
||||
]
|
||||
* self.monte_carlo_cycles,
|
||||
self.chunksize,
|
||||
)
|
||||
)
|
||||
|
||||
cycle_success += current_success
|
||||
results.append((cycle_count, cycle_success))
|
||||
|
||||
_logger.debug("Done, constructing output now")
|
||||
row = {
|
||||
"dipole_moment": dipoles.dipoles[0].p,
|
||||
"dipole_location": dipoles.dipoles[0].s,
|
||||
"dipole_frequency": dipoles.dipoles[0].w,
|
||||
"dipole_moment": actual_dipoles.dipoles[0].p,
|
||||
"dipole_location": actual_dipoles.dipoles[0].s,
|
||||
"dipole_frequency": actual_dipoles.dipoles[0].w,
|
||||
}
|
||||
successes: List[float] = []
|
||||
counts: List[int] = []
|
||||
for model_index, (name, result) in enumerate(
|
||||
for model_index, (name, (count, result)) in enumerate(
|
||||
zip(self.model_names, results)
|
||||
):
|
||||
count = 0
|
||||
success = 0
|
||||
for idx, val in result:
|
||||
count += 1
|
||||
if val.success and val.cost <= COST_THRESHOLD:
|
||||
success += 1
|
||||
|
||||
row[f"{name}_success"] = success
|
||||
row[f"{name}_success"] = result
|
||||
row[f"{name}_count"] = count
|
||||
successes.append(max(success, 0.5))
|
||||
successes.append(max(result, 0.5))
|
||||
counts.append(count)
|
||||
|
||||
success_weight = sum(
|
||||
|
||||
@ -7,7 +7,7 @@ import pdme.util.fast_nonlocal_spectrum
|
||||
from typing import Sequence, Tuple, List
|
||||
import datetime
|
||||
import csv
|
||||
import multiprocessing
|
||||
import multiprocessingg
|
||||
import logging
|
||||
import numpy
|
||||
import numpy.random
|
||||
@ -1,160 +0,0 @@
|
||||
from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
|
||||
import pdme
|
||||
from deepdog.bayes_run import DotInput
|
||||
import datetime
|
||||
import numpy
|
||||
from dataclasses import dataclass
|
||||
import logging
|
||||
from typing import Sequence, Tuple
|
||||
import csv
|
||||
import itertools
|
||||
import multiprocessing
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def get_a_result(discretisation, dots, index):
|
||||
return (index, discretisation.solve_for_index(dots, index))
|
||||
|
||||
|
||||
@dataclass
|
||||
class SingleDipoleDiagnostic:
|
||||
model: str
|
||||
index: Tuple
|
||||
bounds: Tuple
|
||||
actual_dipole: OscillatingDipole
|
||||
result_dipole: OscillatingDipole
|
||||
success: bool
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
self.p_actual_x = self.actual_dipole.p[0]
|
||||
self.p_actual_y = self.actual_dipole.p[1]
|
||||
self.p_actual_z = self.actual_dipole.p[2]
|
||||
self.s_actual_x = self.actual_dipole.s[0]
|
||||
self.s_actual_y = self.actual_dipole.s[1]
|
||||
self.s_actual_z = self.actual_dipole.s[2]
|
||||
self.p_result_x = self.result_dipole.p[0]
|
||||
self.p_result_y = self.result_dipole.p[1]
|
||||
self.p_result_z = self.result_dipole.p[2]
|
||||
self.s_result_x = self.result_dipole.s[0]
|
||||
self.s_result_y = self.result_dipole.s[1]
|
||||
self.s_result_z = self.result_dipole.s[2]
|
||||
self.w_actual = self.actual_dipole.w
|
||||
self.w_result = self.result_dipole.w
|
||||
|
||||
|
||||
class Diagnostic:
|
||||
"""
|
||||
Represents a diagnostic for a single dipole moment given a set of discretisations.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dot_inputs : Sequence[DotInput]
|
||||
The dot inputs for this diagnostic.
|
||||
|
||||
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
|
||||
The models to evaluate.
|
||||
|
||||
actual_model_discretisation : pdme.model.Discretisation
|
||||
The discretisation for 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,
|
||||
actual_dipole_moment: numpy.ndarray,
|
||||
actual_dipole_position: numpy.ndarray,
|
||||
actual_dipole_frequency: float,
|
||||
dot_inputs: Sequence[DotInput],
|
||||
discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
|
||||
filename_slug: str,
|
||||
) -> None:
|
||||
self.dipoles = OscillatingDipoleArrangement(
|
||||
[
|
||||
OscillatingDipole(
|
||||
actual_dipole_moment,
|
||||
actual_dipole_position,
|
||||
actual_dipole_frequency,
|
||||
)
|
||||
]
|
||||
)
|
||||
self.dots = self.dipoles.get_dot_measurements(dot_inputs)
|
||||
|
||||
self.discretisations_with_names = discretisations_with_names
|
||||
self.model_count = len(self.discretisations_with_names)
|
||||
|
||||
self.csv_fields = [
|
||||
"model",
|
||||
"index",
|
||||
"bounds",
|
||||
"p_actual_x",
|
||||
"p_actual_y",
|
||||
"p_actual_z",
|
||||
"s_actual_x",
|
||||
"s_actual_y",
|
||||
"s_actual_z",
|
||||
"w_actual",
|
||||
"success",
|
||||
"p_result_x",
|
||||
"p_result_y",
|
||||
"p_result_z",
|
||||
"s_result_x",
|
||||
"s_result_y",
|
||||
"s_result_z",
|
||||
"w_result",
|
||||
]
|
||||
|
||||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||
self.filename = f"{timestamp}-{filename_slug}.diag.csv"
|
||||
|
||||
def go(self):
|
||||
with open(self.filename, "a", newline="") as outfile:
|
||||
# csv fields
|
||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
||||
writer.writeheader()
|
||||
|
||||
for (name, discretisation) in self.discretisations_with_names:
|
||||
_logger.info(f"Working on discretisation {name}")
|
||||
|
||||
results = []
|
||||
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
|
||||
results = pool.starmap(
|
||||
get_a_result,
|
||||
zip(
|
||||
itertools.repeat(discretisation),
|
||||
itertools.repeat(self.dots),
|
||||
discretisation.all_indices(),
|
||||
),
|
||||
)
|
||||
|
||||
with open(self.filename, "a", newline="") as outfile:
|
||||
writer = csv.DictWriter(
|
||||
outfile,
|
||||
fieldnames=self.csv_fields,
|
||||
dialect="unix",
|
||||
extrasaction="ignore",
|
||||
)
|
||||
|
||||
for idx, result in results:
|
||||
|
||||
bounds = discretisation.bounds(idx)
|
||||
|
||||
actual_success = result.success and result.cost <= 1e-10
|
||||
diag_row = SingleDipoleDiagnostic(
|
||||
name,
|
||||
idx,
|
||||
bounds,
|
||||
self.dipoles.dipoles[0],
|
||||
discretisation.model.solution_as_dipoles(result.normalised_x)[
|
||||
0
|
||||
],
|
||||
actual_success,
|
||||
)
|
||||
row = vars(diag_row)
|
||||
_logger.debug(f"Writing result {row}")
|
||||
writer.writerow(row)
|
||||
Loading…
x
Reference in New Issue
Block a user