import msipy
import numpy as np
import h5py
from outliners import reject_outliers

def find_nearest(data_vector,value):
    idx = (np.abs(data_vector-value)).argmin()
    return idx

source_path_str = r'C:\data'
dimensions = 2
save_path_hdf5 = source_path_str+'.hdf5'
load_path_hdf5 = source_path_str+'.hdf5'
f = h5py.File(load_path_hdf5,'r')
msipy.hdf5_struct(load_path_hdf5)

dset_standard_list_raw = [msipy.import_dataset(f['/NXentry/NXdata/S1_region_substracted_raw_data']), msipy.import_dataset(f['/NXentry/NXdata/S2_region_substracted_raw_data']), msipy.import_dataset(f['/NXentry/NXdata/S3_region_substracted_raw_data']), msipy.import_dataset(f['/NXentry/NXdata/S4_region_substracted_raw_data'])]
dset_standard_list = []
#outliers verwijderen? --> Gaat lastig doen ivm de blanco meetwaarden. + eigenlijk overbodig in dit geval.

#Integratie van standaarden: Som nemen, delen door aantal metingen en matrix vullen met deze waarden.
for standard in dset_standard_list_raw:
    mean_array = np.ones(shape=standard.shape)
    for idx_nuclide in range(1, np.size(standard, 3)):
        mean = np.mean(standard[:,:,:,idx_nuclide,:])
        mean_array[:,:,:,idx_nuclide,:] = mean_array[:,:,:,idx_nuclide,:] * mean

    dset_standard_list.append(msipy.dsarray(mean_array, standard.attrs))

#Sensitivity berekenen via load_calib2
#Probleem: Zoekt waarden op in files, aanpassen zodat het mogelijk is om bepaalde waarden mee te geven? + enkel waarden voor In en Ho beschikbaar..


msipy.load_calib_values_list(dset_standard_list, [{'115In':100, '165Ho':1}, {'115In':100, '165Ho':5}, {'115In':100, '165Ho':20}, {'115In':100, '165Ho':100}])

f.close()