from pdme.model.unrestricted_model import UnrestrictedModel, UnrestrictedDiscretisation
from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
import itertools
import logging
import multiprocessing
import numpy


def get_a_result(discretisation, dots, index):
	return (index, discretisation.solve_for_index(dots, index))

def main():
	dipoles = OscillatingDipoleArrangement([OscillatingDipole((0.9602915510740146, -4.026029745743671, -9.049015619049385), (8.414994532717355, -4.618786432010118, 4.124722380672804), 7)])
	dot_inputs = list(itertools.chain.from_iterable(
		(([1, 2, 0], f), ([1, 1, 0], f), ([2, 1, 0], f), ([2, 2, 0], f)) for f in numpy.arange(1, 10, 2)
	))
	dots = dipoles.get_dot_measurements(dot_inputs)

	model_low = UnrestrictedModel(-10, 10, -10, 10, 2.5, 3.5, 1)
	discretisation_low = UnrestrictedDiscretisation(model_low, 6, 6, 3, 5, 5, 5, 10)

	model_medium = UnrestrictedModel(-10, 10, -10, 10, 3.5, 4.5, 1)
	discretisation_medium = UnrestrictedDiscretisation(model_medium, 6, 6, 3, 5, 5, 5, 10)

	model_high = UnrestrictedModel(-10, 10, -10, 10, 4.5, 5.5, 1)
	discretisation_high = UnrestrictedDiscretisation(model_high, 6, 6, 3, 5, 5, 5, 10)
	
	with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
		results_low = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_low), itertools.repeat(dots), discretisation_low.all_indices()))

	with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
		results_medium = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_medium), itertools.repeat(dots), discretisation_medium.all_indices()))

	with multiprocessing.Pool(multiprocessing.cpu_count()-1 or 1) as pool:
		results_high = pool.starmap(get_a_result, zip(itertools.repeat(discretisation_high), itertools.repeat(dots), discretisation_high.all_indices()))
	
	count_low = 0
	success_low = 0
	for idx, result in results_low:
		count_low += 1
		if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
			answer = result.normalised_x
			success_low += 1
		else:
			answer = None

	count_medium = 0
	success_medium = 0
	for idx, result in results_medium:
		count_medium += 1
		if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
			answer = result.normalised_x
			success_medium += 1
		else:
			answer = None

	count_high = 0
	success_high = 0
	for idx, result in results_high:
		count_high += 1
		if result.success and result.cost <= 1e-10 and numpy.linalg.norm(result.x[0:3]) < 10:
			answer = result.normalised_x
			success_high += 1
		else:
			answer = None

	logging.info(f"Low   : Out of {count_low} cells, {success_low} were successful")
	logging.info(f"Medium: Out of {count_medium} cells, {success_medium} were successful")
	logging.info(f"High  : Out of {count_high} cells, {success_high} were successful")

if __name__ == "__main__":
	logging.basicConfig(level=logging.INFO)
	main()