74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
from pdme.model.unrestricted_model import UnrestrictedModel, UnrestrictedDiscretisation
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from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
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import itertools
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import logging
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import multiprocessing
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import numpy
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def get_a_result(discretisation, dots, index):
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return (index, discretisation.solve_for_index(dots, index))
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def main():
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dipoles = OscillatingDipoleArrangement([OscillatingDipole((5.389527566844908, 4.997072803674042, -4.762077256152248), (3.2605114165938183, -3.886070609159507, 3.842556916841345), 10)])
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dot_inputs = list(itertools.chain.from_iterable(
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(([1, 2, 0], f), ([1, 1, 0], f), ([2, 1, 0], f), ([2, 2, 0], f)) for f in numpy.arange(1, 10, 2)
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))
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dots = dipoles.get_dot_measurements(dot_inputs)
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model_low = UnrestrictedModel(-10, 10, -10, 10, 2.5, 3.5, 1)
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discretisation_low = UnrestrictedDiscretisation(model_low, 6, 6, 3, 5, 5, 5, 10)
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model_medium = UnrestrictedModel(-10, 10, -10, 10, 3.5, 4.5, 1)
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discretisation_medium = UnrestrictedDiscretisation(model_medium, 6, 6, 3, 5, 5, 5, 10)
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model_high = UnrestrictedModel(-10, 10, -10, 10, 4.5, 5.5, 1)
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discretisation_high = UnrestrictedDiscretisation(model_high, 6, 6, 3, 5, 5, 5, 10)
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with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
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results_low = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_low), itertools.repeat(dots), discretisation_low.all_indices()))
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with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
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results_medium = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_medium), itertools.repeat(dots), discretisation_medium.all_indices()))
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with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
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results_high = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_high), itertools.repeat(dots), discretisation_high.all_indices()))
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count_low = 0
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success_low = 0
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for idx, result in results_low:
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count_low += 1
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if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
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answer = result.normalised_x
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success_low += 1
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else:
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answer = None
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count_medium = 0
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success_medium = 0
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for idx, result in results_medium:
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count_medium += 1
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if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
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answer = result.normalised_x
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success_medium += 1
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else:
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answer = None
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count_high = 0
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success_high = 0
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for idx, result in results_high:
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count_high += 1
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if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
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answer = result.normalised_x
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success_high += 1
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else:
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answer = None
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logging.info(f"Low : Out of {count_low} cells, {success_low} were successful")
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logging.info(f"Medium: Out of {count_medium} cells, {success_medium} were successful")
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logging.info(f"High : Out of {count_high} cells, {success_high} were successful")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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main()
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