import numpy as np
import matplotlib.pyplot as plt
import time
import sys
sys.path.append("C://Users//xbrjos//Desktop//Python")
from gepard import dataset

from methods import IvlevaSubsampling, RandomSampling, SizeBinFractioning
from geometricMethods import BoxSelectionCreator
from helpers import ParticleBinSorter
from evaluation import ResultComparer

fname: str = r'C:\Users\xbrjos\Desktop\temp MP\190313_Soil_5_A_50_5_1_50_1\190313_Soil_5_A_50_5_1_50_1.pkl'
# fname: str = r'C:\Users\xbrjos\Desktop\temp MP\190326_MCII_WWTP_SB_50_2\190326_MCII_WWTP_SB_50_2.pkl'
# fname: str = r'C:\Users\xbrjos\Desktop\temp MP\190326_MCII_WWTP_SB_50_1\190326_MCII_WWTP_SB_50_1.pkl'
# fname: str = r'C:\Users\xbrjos\Desktop\temp MP\KWS_CT_3_ds1_all_10_2\KWS_CT_3_ds1_all_10_2.pkl'  #legacy convert not working..
# fname: str = r'C:\Users\xbrjos\Desktop\temp MP\190201_BSB_Stroomi_ds2_R1_R2_50\190201_BSB_Stroomi_ds2_R1_R2_50.pkl'  #zvalues image missing, legacy convert fails..


dset = dataset.loadData(fname)
print('loaded dataset')

boxCreator = BoxSelectionCreator(dset)
center, size = boxCreator.get_filterDimensions_from_dataset()
print(center, size)
print(dset.mapToPixel(center, force=True))

# pc = dset.particleContainer
# origParticles = pc.particles

# resultComparer = ResultComparer()
# numOrigMP = resultComparer._get_number_of_MP_particles(origParticles)
# print(f'orig particles: {len(origParticles)}, of which are mp: {numOrigMP}')
# # ivlevaSampling = IvlevaSubsampling(pc)
# # ivlevaFraction, ivlevaParticles = ivlevaSampling.apply_subsampling_method()

# t0 = time.time()
# fractions = np.arange(0.05, .55, 0.05)
# errors = []
# binErrors = []
# numIterations = 1000

# for fraction in fractions:
#     print('random sampling, fraction:', fraction)
# #    randomSampling = RandomSampling(pc, desiredFraction=fraction)
#     randomSampling = SizeBinFractioning(pc, fraction)
#     iterErrors = []
#     binIterErrors = []
#     for _ in range(numIterations):
#         randomFraction, randomParticles = randomSampling.apply_subsampling_method()
#         iterErrors.append(resultComparer._get_mp_count_error(origParticles, randomParticles, randomFraction))
#         bins, errorsPerBin = resultComparer._get_mp_count_error_per_bin(origParticles, randomParticles, randomFraction)
#         binIterErrors.append(errorsPerBin)

#     errors.append(round(np.mean(iterErrors)*100)) #from fraction to %
#     fractionBinErrors = []
#     for binIndex in range(len(bins)+1):
#         binError = round(np.mean([binIterErrors[i][binIndex] for i in range(numIterations)]) * 100)
#         fractionBinErrors.append(binError)
#     binErrors.append(fractionBinErrors)

# print('random sampling took', np.round(time.time()-t0, 2), 'seonds')
# binLowerLimits = bins.copy()
# binLowerLimits.insert(0, 0)
# plt.subplot(121)
# plt.plot(fractions, errors)
# # plt.title(f'Random Sampling, averaged from {numIterations} trials, orig particle count: {len(origParticles)}')
# plt.xlabel('Fraction measured')
# plt.ylabel('Average error in MP particle count (%)')

# plt.subplot(122)
# for fracMeas, curBinErrors in zip(fractions, binErrors):
#     plt.plot(binLowerLimits, curBinErrors, label=np.round(fracMeas, 1))
# # plt.title('Error in MP count (%) per size bin')
# plt.xlabel('particle size')
# plt.ylabel('Average error in MP particle count (%)')
# plt.legend()
# plt.show()


# # sizeBinSampling = SizeBinFractioning(pc)
# # sizeBinParticles = sizeBinSampling.apply_subsampling_method()