Deepak Mallubhotla
e01d0e14a9
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107 lines
2.2 KiB
Python
107 lines
2.2 KiB
Python
from pdme.model import (
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LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
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)
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import pdme.inputs
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import pdme.measurement.input_types
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import pdme.subspace_simulation
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import numpy
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import logging
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_logger = logging.getLogger(__name__)
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SEED_TO_USE = 42
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def get_cost_function():
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x_min = -10
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x_max = 10
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y_min = -5
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y_max = 5
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z_min = 2
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z_max = 3
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p_fixed = 10
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theta = 0
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phi = 0
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max_frequency = 5
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model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
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x_min,
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x_max,
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y_min,
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y_max,
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z_min,
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z_max,
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0,
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max_frequency,
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p_fixed,
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theta,
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phi,
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1,
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0.5,
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)
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model.rng = numpy.random.default_rng(SEED_TO_USE)
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freqs = [0.01, 0.1, 1, 10, 100]
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dot_positions = [[-1.5, 0, 0], [-0.5, 0, 0], [0.5, 0, 0], [1.5, 0, 0]]
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dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, freqs)
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dot_input_array = pdme.measurement.input_types.dot_inputs_to_array(dot_inputs)
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actual_dipoles = model.get_dipoles(0, numpy.random.default_rng(SEED_TO_USE))
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actual_measurements = actual_dipoles.get_dot_measurements(dot_inputs)
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actual_measurements_array = numpy.array([m.v for m in actual_measurements])
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def cost_to_use(sample_dipoleses: numpy.ndarray) -> numpy.ndarray:
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return pdme.subspace_simulation.proportional_costs_vs_actual_measurement(
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dot_input_array, actual_measurements_array, sample_dipoleses
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)
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return cost_to_use
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def test_log_spaced_fixed_orientation_mcmc_basic(snapshot):
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x_min = -10
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x_max = 10
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y_min = -5
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y_max = 5
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z_min = 2
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z_max = 3
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p_fixed = 10
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theta = 0
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phi = 0
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max_frequency = 5
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model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
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x_min,
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x_max,
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y_min,
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y_max,
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z_min,
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z_max,
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0,
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max_frequency,
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p_fixed,
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theta,
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phi,
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1,
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0.5,
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)
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model.rng = numpy.random.default_rng(1234)
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seed = model.get_monte_carlo_dipole_inputs(1, -1)
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cost_function = get_cost_function()
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stdev = pdme.subspace_simulation.DipoleStandardDeviation(2, 2, 1, 0.25, 0.5, 1)
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stdevs = pdme.subspace_simulation.MCMCStandardDeviation([stdev])
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chain = model.get_mcmc_chain(
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seed[0],
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cost_function,
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10,
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cost_function(seed)[0],
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stdevs,
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rng_arg=numpy.random.default_rng(1515),
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)
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assert chain == snapshot
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