#  Synthetise new data and analyse it directly to return fit parameters

import numpy as np
import pandas as pd

from fsl_mrs.core import MRS
from fsl_mrs.utils import plotting, mrs_io
from fsl_mrs.utils.synthetic import synthetic_from_basis as synth
from fsl_mrs.utils.misc import parse_metab_groups
from fsl_mrs.utils.fitting import fit_FSLModel


def synth_and_ana(noise_cov,
                  fit_parameters, fit_snr, fit_sdnoise, fit_varnoise,
                  df_parameter_synth, basis_path, output_path):
    """Synthetise spectra with given noise-covariance, analyse the data and
    return table of fitting parameters with a fit-plot.
    We ignore Gly. Independent scaling of Macro Molecules."""

    # Load input
    synth_parameter = df_parameter_synth.mean().to_dict()
    basis, names, Bheader = mrs_io.read_basis(basis_path)

    # Set the adjustment lists from our input
    broadening = [(synth_parameter["gamma_0"], synth_parameter["sigma_0"]),
                  (synth_parameter["gamma_1"], synth_parameter["sigma_1"])]
    shifting = [(synth_parameter["eps_0"]), (synth_parameter["eps_1"])]
    baseline = [synth_parameter["B_real_0"], synth_parameter["B_imag_0"],
                synth_parameter["B_real_1"], synth_parameter["B_imag_1"],
                synth_parameter["B_real_2"], synth_parameter["B_imag_2"]]
    coilphase = [synth_parameter["Phi0"]]

    # Generate synthetic data
    fidS, headerS, concentrationsS = synth.syntheticFromBasisFile(basis_path,
                                                                  concentrations=synth_parameter,
                                                                  ignore=['Gly'], ind_scaling=['mm'],
                                                                  metab_groups='mm', broadening=broadening,  shifting=shifting,
                                                                  # correct for complex noise
                                                                  noisecovariance=np.divide(
                                                                      noise_cov, 2),
                                                                  # CAVE: baseline chosen manually
                                                                  baseline=baseline, baseline_ppm=(
                                                                      .2, 4.2),
                                                                  coilphase=coilphase,
                                                                  bandwidth=6000)
    # Create mrs object for further use
    mrsA = MRS(FID=fidS, header=headerS, basis=basis,
               basis_hdr=Bheader[0], names=names)

    mrsA.ignore(['Gly'])
    mrsA.processForFitting(ind_scaling=['mm'])

    # Scale it to Input data
    # rescaled_FID, __ = rescale_FID(mrsA.FID, 1/mrsA.scaling["FID"]*100)
    mrsA.set_FID(fidS)

    metab_groups = parse_metab_groups(mrsA, 'mm')
    # Use Voigt line broadening, fit between .2 and 4.2,
    #  with a 2nd order polynomial baseline
    FitArgs = {
        'model': 'voigt',
        'metab_groups': metab_groups,
        'ppmlim': (.2, 4.2),
        'baseline_order': 2}
    res = fit_FSLModel(mrsA, **FitArgs)

    # Combine highly correlated metabolites
    combinationList = [['Glu', 'Gln'],
                       ['GPC', 'PCho'],
                       ['Cr', 'PCr'],
                       ['Glc', 'Tau'],
                       ["NAA", "NAAG"]]
    res.combine(combinationList)

    # Store parameters from fit
    fit_parameters.append(res.fitResults)
    fit_snr.append(res.SNR.spectrum)
    # Old version of noise
    # noise_sd=np.max(np.real(mrsA.get_spec(ppmlim=(.2,4.2))))/res.SNR.spectrum
    noise_sd = np.std(mrsA.FID[1000:1600])
    fit_sdnoise.append(noise_sd)
    noise_var = np.var(mrsA.FID[1000:1600])
    fit_varnoise.append(noise_var)

    df_params = pd.concat(fit_parameters, ignore_index=True)
    df_params['SNR'] = fit_snr
    df_params['noise_sd'] = fit_sdnoise
    df_params['noise_var'] = fit_varnoise

    return df_params