Deepak Mallubhotla
9a7a3ff2c7
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159 lines
3.0 KiB
Python
159 lines
3.0 KiB
Python
import deepdog
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import logging
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import logging.config
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import numpy.random
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from pdme.model import (
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LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
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LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
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LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
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)
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_logger = logging.getLogger(__name__)
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def fixed_z_model_func(
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xmin,
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xmax,
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ymin,
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ymax,
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zmin,
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zmax,
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wexp_min,
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wexp_max,
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pfixed,
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n_max,
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prob_occupancy,
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):
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return LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
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xmin,
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xmax,
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ymin,
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ymax,
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zmin,
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zmax,
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wexp_min,
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wexp_max,
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pfixed,
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0,
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0,
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n_max,
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prob_occupancy,
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)
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def get_model(orientation):
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model_funcs = {
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"fixedz": fixed_z_model_func,
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"free": LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
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"fixedxy": LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
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}
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model = model_funcs[orientation](
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-10,
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10,
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-17.5,
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17.5,
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5,
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7.5,
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-5,
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6.5,
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10**3,
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2,
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0.99999999,
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)
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model.n = 2
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model.rng = numpy.random.default_rng(1234)
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return (
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f"connors_geom-5height-orientation_{orientation}-pfixexp_{3}-dipole_count_{2}",
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model,
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)
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def test_basic_analysis(snapshot):
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dot_positions = [[0, 0, 0], [0, 1, 0]]
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freqs = [1, 10, 100]
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models = []
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orientations = ["free", "fixedxy", "fixedz"]
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for orientation in orientations:
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models.append(get_model(orientation))
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_logger.info(f"have {len(models)} models to look at")
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if len(models) == 1:
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_logger.info(f"only one model, name: {models[0][0]}")
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square_run = deepdog.BayesRunWithSubspaceSimulation(
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dot_positions,
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freqs,
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models,
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models[0][1],
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filename_slug="test",
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end_threshold=0.9,
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ss_n_c=5,
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ss_n_s=2,
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ss_m_max=10,
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ss_target_cost=150,
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ss_level_0_seed=200,
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ss_mcmc_seed=20,
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ss_use_adaptive_steps=True,
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ss_default_phi_step=0.01,
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ss_default_theta_step=0.01,
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ss_default_r_step=0.01,
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ss_default_w_log_step=0.01,
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ss_default_upper_w_log_step=4,
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ss_dump_last_generation=False,
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write_output_to_bayesruncsv=False,
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ss_initial_costs_chunk_size=1000,
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)
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result = square_run.go()
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assert result == snapshot
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def test_bayesss_with_tighter_cost(snapshot):
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dot_positions = [[0, 0, 0], [0, 1, 0]]
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freqs = [1, 10, 100]
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models = []
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orientations = ["free", "fixedxy", "fixedz"]
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for orientation in orientations:
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models.append(get_model(orientation))
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_logger.info(f"have {len(models)} models to look at")
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if len(models) == 1:
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_logger.info(f"only one model, name: {models[0][0]}")
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square_run = deepdog.BayesRunWithSubspaceSimulation(
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dot_positions,
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freqs,
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models,
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models[0][1],
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filename_slug="test",
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end_threshold=0.9,
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ss_n_c=5,
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ss_n_s=2,
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ss_m_max=10,
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ss_target_cost=1.5,
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ss_level_0_seed=200,
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ss_mcmc_seed=20,
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ss_use_adaptive_steps=True,
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ss_default_phi_step=0.01,
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ss_default_theta_step=0.01,
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ss_default_r_step=0.01,
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ss_default_w_log_step=0.01,
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ss_default_upper_w_log_step=4,
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ss_dump_last_generation=False,
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write_output_to_bayesruncsv=False,
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ss_initial_costs_chunk_size=1
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)
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result = square_run.go()
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assert result == snapshot
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