diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/synth_and_ana.py b/synth_and_ana.py
deleted file mode 100644
index 9acd7ee..0000000
--- a/synth_and_ana.py
+++ /dev/null
@@ -1,90 +0,0 @@
-#  Synthetise new data and analyse it directly to return fit parameters
-
-import numpy as np
-import panda 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