feat!: bayes run now handles multidipoles with changes to output file format etc.
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@ -23,9 +23,11 @@ _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 = input
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sample_dipoles = model.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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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_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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@ -88,8 +90,6 @@ class BayesRun:
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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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@ -97,7 +97,6 @@ class BayesRun:
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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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@ -106,18 +105,16 @@ class BayesRun:
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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.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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@ -126,15 +123,16 @@ class BayesRun:
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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.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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@ -143,10 +141,7 @@ class BayesRun:
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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.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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@ -179,29 +174,17 @@ class BayesRun:
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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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# 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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with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
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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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@ -212,55 +195,50 @@ class BayesRun:
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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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model,
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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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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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)
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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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# 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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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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"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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