In [1]:

    

"""Mainly Edited for private usage by:  Ioanna Mitsioni
                                        Ioannis Agriomallos
License: BSD 3 clause
"""
import time
start_time = time.time()
from copy import deepcopy, copy
import math
import scipy.io as sio
import shutil
import os
from random import shuffle
import numpy as np
from pylab import *
# from featext2 import *
import matplotlib.pyplot as plt
%matplotlib inline 
#matplotlib qt
# inline (suitable for ipython only, shown inside browser!) or qt (suitable in general, shown in external window!)
from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D #, axes3d
from sklearn.preprocessing import StandardScaler, MinMaxScaler, normalize
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.neural_network import MLPClassifier
from sklearn.feature_selection import SelectFromModel, SelectKBest, mutual_info_classif
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, confusion_matrix
from collections import OrderedDict
import re
import datetime
import urllib
import tarfile
# import joblib
# from joblib import Parallel, delayed, Memory
from tempfile import mkdtemp
import copy_reg
import types
import itertools
from itertools import compress
from collections import Counter
import glob

#import multiprocessing
def _pickle_method(m):
    if m.im_self is None:
        return getattr, (m.im_class, m.im_func.func_name)
    else:
        return getattr, (m.im_self, m.im_func.func_name)
copy_reg.pickle(types.MethodType, _pickle_method)


h = .2  # step size in the mesh
window = 1024


    

In [2]:

    

############ Feature Names ############
"""features:                                                                       ||      if       
   |--> time domain      :                                                         || samples = 1024
   |----|---> phinyomark : 11+3{shist} --------------------------> = 14+0.0samples ||             14
   |----|---> golz       : 10+samples{acrol} --------------------> = 10+1.0samples ||           1034
   |--> frequency domain :                                                                          
   |----|---> phinyomark : 3{arco}+4{mf}+2(samples/2+1){RF,IF} --> =  9+1.0samples ||           1033
   |----|---> golz       : 2(samples/2+1){AF,PF} ----------------> =  2+1.0samples ||           1026
   |----|----------------|-------alltogether---------------------> = 35+3.0samples || numfeat = 3107
"""
## Time Domain Phinyomark feats
featnames = ['intsgnl', 'meanabs', 'meanabsslp', 'ssi', 'var', 'rms', 'rng', 'wavl', 'zerox', 'ssc', 'wamp', 
             'shist1', 'shist2', 'shist3']                                                   # 11+3{shist}
## Frequency Domain Phinyomark feats
featnames += ['arco1', 'arco2', 'arco3', 'mnf', 'mdf', 'mmnf', 'mmdf']                       # 3{arco}+4{mf}
featnames += ['reFFT{:03d}'.format(i) for i in range(window/2+1)]                            # samples/2+1{RF}
featnames += ['imFFT{:03d}'.format(i) for i in range(window/2+1)]                            # samples/2+1{IF}
## Time Domain Golz feats
featnames += ['meanv', 'stdr', 'mx', 'rngx', 'rngy', 'med', 'hjorth', 'sentr', 'se', 'ssk']  # 10
featnames += ['acrol{:04d}'.format(i) for i in range(window)]                                # samples{acrol}
## Frequency Domain Golz feats
featnames += ['amFFT{:03d}'.format(i) for i in range(window/2+1)]                            # samples/2+1{AF}
featnames += ['phFFT{:03d}'.format(i) for i in range(window/2+1)]                            # samples/2+1{PF}


    

In [3]:

    

############ Prepare the indeces for each feature ############
def get_feat_id(feat_ind, printit=0, sample_window=window): 
    """Find the corresponding indeces of the desired features inside feature vector,
    and link them with their names and level of abstraction
    -> feat_ind        : range of indeces
    -> printit         : print output indeces (1) or not (0)
    -> sample_window   : parameter for accurate computation of feature indeces
    <- full_path_id    : indeces of all features
    <- norm_time_feats : indeces of time features
    <- norm_freq_feats : indeces of frequency features
    """
    # get the feat inds wrt their source : 3rd level
    norm_time_phin = range(0,14)
    norm_freq_phin = range(norm_time_phin[-1] + 1, norm_time_phin[-1] + 9 + sample_window + 1)
    norm_time_golz = range(norm_freq_phin[-1] + 1, norm_freq_phin[-1] + 10 + sample_window + 1)
    norm_freq_golz = range(norm_time_golz[-1] + 1, norm_time_golz[-1] + 2 + sample_window + 1)
    # get the feat inds wrt their domain : 2nd level 
    norm_time_feats = norm_time_phin + norm_time_golz
    norm_freq_feats = norm_freq_phin + norm_freq_golz
    # get the feat inds wrt their prefeat: 1st level 
    norm_feats = norm_time_feats + norm_freq_feats

    # get the feat inds wrt their source : 3rd level
    disp = norm_feats[-1]+1
    ftfn_time_phin = range(disp ,disp + 14)
    ftfn_freq_phin = range(ftfn_time_phin[-1] + 1, ftfn_time_phin[-1] + 9 + sample_window + 1)
    ftfn_time_golz = range(ftfn_freq_phin[-1] + 1, ftfn_freq_phin[-1] + 10 + sample_window + 1)
    ftfn_freq_golz = range(ftfn_time_golz[-1] + 1, ftfn_time_golz[-1] + 2 + sample_window + 1)
    # get the feat inds wrt their domain : 2nd level 
    ftfn_time_feats = ftfn_time_phin + ftfn_time_golz
    ftfn_freq_feats = ftfn_freq_phin + ftfn_freq_golz
    # get the feat inds wrt their prefeat: 1st level 
    ftfn_feats = ftfn_time_feats + ftfn_freq_feats

    # create the final "reference dictionary"
    # 3 np.arrays, id_list[0] = level 1 etc
    id_list = [np.zeros((len(ftfn_feats + norm_feats),1)) for i in range(3)]
    id_list[0][:norm_feats[-1]+1] = 0 # 0 signifies norm / 1 signifies ft/fn
    id_list[0][norm_feats[-1]+1:] = 1

    id_list[1][:norm_time_phin[-1]+1] = 0 # 0 signifies time / 1 signifies freq
    id_list[1][norm_time_phin[-1]+1:norm_freq_phin[-1]+1] = 1
    id_list[1][norm_freq_phin[-1]+1:norm_time_golz[-1]+1] = 0
    id_list[1][norm_time_golz[-1]+1:norm_freq_golz[-1]+1] = 1
    id_list[1][norm_freq_golz[-1]+1:ftfn_time_phin[-1]+1] = 0
    id_list[1][ftfn_time_phin[-1]+1:ftfn_freq_phin[-1]+1] = 1
    id_list[1][ftfn_freq_phin[-1]+1:ftfn_time_golz[-1]+1] = 0
    id_list[1][ftfn_time_golz[-1]+1:] = 1

    id_list[2][:norm_freq_phin[-1]+1] = 0 #0 signifies phinyomark / 1 signifies golz
    id_list[2][norm_freq_phin[-1]+1:norm_freq_golz[-1]+1] = 1
    id_list[2][norm_freq_golz[-1]+1:ftfn_freq_phin[-1]+1] = 0
    id_list[2][ftfn_freq_phin[-1]+1:] = 1 
    
    full_path_id = [np.zeros((len(feat_ind),5)) for i in range(len(feat_ind))]
   
    for ind, val in enumerate(feat_ind):
        full_path_id[ind] = [val, id_list[2][val], id_list[1][val], id_list[0][val]]
        if (printit==1):
            if(full_path_id[ind][1]==0):
                lvl3 = 'Phin'
            else:
                lvl3 = 'Golz'
            if(full_path_id[ind][2]==0):
                lvl2 = 'Time'
            else:
                lvl2 = 'Freq'
            if(full_path_id[ind][3]==0):
                lvl1 = 'Norm'
            else:
                lvl1 = 'Ft/Fn'
            print(feat_ind[ind],featnames[val%(norm_feats[-1]+1)],lvl3,lvl2,lvl1)
    
    return(full_path_id,norm_time_feats,norm_freq_feats)


    

In [4]:

    

def subfeat_inds(ofs=len(featnames)):
    """returns a subfeatures' indeces
    -> ofs                     : number of features in total
    <- amfft, freq, time, both : split featureset indeces for 
                                 amplitude of FFT, all time only,
                                 all frequency only and all features
    """
    _,time,freq = get_feat_id(range(ofs))
    both = range(ofs)
    amfft = []
    for i in range(len(featnames)):
        if (featnames[i].startswith('amFFT')):
            amfft.append(i)
    return amfft, freq, time, both


    

In [69]:

    

def get_tot_feats(fs, subfs, r):
     ###############################################################################################################
    # Version 2, using the bool masks and keeping an array of 6x3000 feats 
    ###############################################################################################################
    # If checking for FnormAll, you end up with 36 models of (trained_on, tested_on) combinations but TECHNICALLY
    # the features are the same for every trained_on "sixplet" so there's no need to iterate over all the tested_on
    # indeces. Therefore, ts = 2 is chosen arbitrarily 
    
#     filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_*.npz")
    filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_tr_" + str(0) + "_ts_" + str(5) + ".npz")
    print filenames
    # the features kept for surface i will be stored in bool_tot_feats[i] (final size: 6x1000)
    bool_tot_feats = []
    best_tot_feats = []
    best_tot_scores = []
    
    for filn in filenames:
        # for every training surface     
        model_file = np.load(filn)
        model = model_file['model']
        #keep a list of the 1000 features kept
        model_feat_scores = model[0].named_steps['feature_selection'].scores_
        bool_model_features = list(model[0].named_steps['feature_selection'].get_support(indices = False))
        if subfs<=2:
            bool_model_features = np.logical_not(np.array(bool_model_features))
            bool_model_features[np.array(model_feat_scores).argsort()[-1000:][::-1].tolist()] = True
            bool_model_features = bool_model_features.tolist()
#         plt.plot(model_feat_scores)
        bool_tot_feats.append(bool_model_features)
        best_tot_scores.append(np.array(model_feat_scores[np.array(model_feat_scores).argsort()[-1000:][::-1].tolist()]))
        best_tot_feats.append(np.array(model_feat_scores).argsort()[-1000:][::-1])
        
    return bool_tot_feats, best_tot_feats, best_tot_scores


    

In [56]:

    

def get_tot_feats_importance_pca(fs, subfs, r):
     ###############################################################################################################
    # Version 2, using the bool masks and keeping an array of 6x3000 feats 
    ###############################################################################################################
    # If checking for FnormAll, you end up with 36 models of (trained_on, tested_on) combinations but TECHNICALLY
    # the features are the same for every trained_on "sixplet" so there's no need to iterate over all the tested_on
    # indeces. Therefore, ts = 2 is chosen arbitrarily 
    
#     filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_*.npz")
    filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_tr_" + str(0) + "_ts_" + str(5) + ".npz")
    print filenames
    # the features kept for surface i will be stored in bool_tot_feats[i] (final size: 6x1000)
    bool_tot_feats = []
    best_tot_feats = []
    best_tot_scores = []
    
    for filn in filenames:
        # for every training surface     
        model_file = np.load(filn)
        model = model_file['model'][0]
        #keep a list of the 1000 features kept
        
        model_pca_var = model[0].named_steps['decomp'].explained_variance_
        model_pca_var_rat = model[0].named_steps['decomp'].explained_variance_ratio_
        model_pca_covar = model[0].named_steps['decomp'].get_covariance()
        model_pca_mean = model[0].named_steps['decomp'].mean_
        n_comp = model[0].named_steps['decomp'].n_components_
        comp = model[0].named_steps['decomp'].components_
        print len(model_pca_var), model_pca_var
        print len(model_pca_var_rat), model_pca_var_rat
        print model_pca_covar.shape, model_pca_covar
        # plt.imshow(model_pca_covar)
        print len(model_pca_mean), model_pca_mean
        print n_comp
        print comp.shape, comp
        nfeat = 1000
        feat_importance = np.zeros(nfeat)
        for nc in range(len(comp)):
            feat_importance += comp[nc]*model_pca_var_rat[nc]
        #     plt.plot(range(1000),comp[nc]*model_pca_var_rat[nc])
        plt.plot(range(nfeat),feat_importance/nfeat)
        print feat_importance*nfeat
        sort_feat_imp_ind = np.array(feat_importance).argsort()[:][::-1]
        print np.array(featnames)[sort_feat_imp_ind]
        
        
        model_feat_scores = model[0].named_steps['feature_selection'].scores_
        bool_model_features = list(model[0].named_steps['feature_selection'].get_support(indices = False))
        if subfs<=2:
            bool_model_features = np.logical_not(np.array(bool_model_features))
            bool_model_features[np.array(model_feat_scores).argsort()[-1000:][::-1].tolist()] = True
            bool_model_features = bool_model_features.tolist()
#         plt.plot(model_feat_scores)
        bool_tot_feats.append(bool_model_features)
        best_tot_scores.append(np.array(model_feat_scores[np.array(model_feat_scores).argsort()[-1000:][::-1].tolist()]))
        best_tot_feats.append(np.array(model_feat_scores).argsort()[-1000:][::-1])
        
    return bool_tot_feats, best_tot_feats, best_tot_scores


    

In [7]:

    

def freq_time_counter(full_names, subfs):
    f_c = 0; t_c = 0
    for i in range(len(full_names)):
        if full_names[i][2] == 1:
            if subfs!=2:
                f_c += 1
        else:
            if subfs>=2:
                t_c += 1
    return (f_c, t_c)


    

In [8]:

    

def get_common_feats(bool_tot_feats, subfs, skip_surf = 6, print_common_feats = 0):   
    # skip_surf = 6 by default so you won't skip any surfaces.
    # returns the list of inds for the common feats
    trans_test_bools = []

    for i in range(len(bool_tot_feats)):
        if i != skip_surf:
            trans_test_bools.append(bool_tot_feats[i])
        else: 
            continue
            
    trans_test_bools = np.transpose(trans_test_bools)
    common_feats = []
    matches  = []
    for i in range(len(trans_test_bools)):
        matches.append(np.all(trans_test_bools[i]))
    for ind, val in enumerate(matches):
        if val:
            common_feats.append(ind)
    print("===============================================================")       
    print("%d common feats, out of %d total" %(len(common_feats),len(matches)))
    full_names, _, _ = get_feat_id(common_feats, printit = print_common_feats)
    freq_counter, time_counter = freq_time_counter(full_names, subfs)
    print("of which, %d (%.2f%%) were Freq features and %d (%.2f%%) were Time features" %(freq_counter, (float(freq_counter)/len(common_feats))*100, time_counter, (float(time_counter)/len(common_feats))*100 ))

    print("===============================================================")
    
    return common_feats, full_names


    

In [68]:

    

def get_inv_pca_feat_imp(r, fs, subfs, commonfeats):
#     filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_*.npz")
    filenames = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_tr_" + str(0) + "_ts_" + str(5) + ".npz")
    print filenames
    feat_imp = np.zeros(len(commonfeats))
    for filn in filenames:
        # for every training surface
        model_file = np.load(filn)
        # get the corresponding model
        model = model_file['model'][0]
        # get the pca
        pca = model.named_steps['decomp']
        # get from the inverse pca the importance of each feature
        invpca = pca.inverse_transform(np.eye(20))
        # find the corresponding indexes returned by feature selection step
        model_feat_ind = list(model.named_steps['feature_selection'].get_support(indices = True))
        # use the commonfeats indexes to find the corresponding index inside model_feat_ind to reference invpca
        # and add its importance to feat_imp correctly
        for ci in range(len(commonfeats)):
            curr_ind = model_feat_ind.index(commonfeats[ci])
            feat_imp[ci] += np.mean(invpca[:,curr_ind])
    return feat_imp


    

In [70]:

    

### Example 
fs=0
subfs=3
for r in range(1,2):
    tot_feats, best_tot_feats, best_tot_scores = get_tot_feats(fs=0, subfs=subfs, r=r)
    common_feats, full_names = get_common_feats(bool_tot_feats=tot_feats, subfs=subfs, skip_surf=6, print_common_feats=0)
    fn = np.array(full_names)
    tmp = fn[:,0].astype(int).tolist()
    print np.array(featnames)[tmp]
    feat_imp = get_inv_pca_feat_imp(r,fs,subfs,common_feats)
#     print feat_imp
    plt.figure(figsize=(20,10))
    feat_imp_sort_ind = np.argsort(feat_imp)[::-1]
    feat_imp_sort = np.sort(feat_imp)[::-1]
    plt.plot(feat_imp_sort)
    for t in range(len(feat_imp_sort_ind)):
        print t, np.array(featnames)[tmp[feat_imp_sort_ind[t]]], feat_imp_sort[t]

#     for j in range(0,len(best_tot_feats),1):
#         tmpj = best_tot_feats[j][:5]
#         print len(tmpj)
#         print j, best_tot_scores[j][:10]
#         print j, np.array(featnames)[tmpj]


    

    




['data/results1/fs_0_subfs_3_tr_0_ts_5.npz']
===============================================================
1000 common feats, out of 3107 total
of which, 44 (4.40%) were Freq features and 956 (95.60%) were Time features
===============================================================
['intsgnl' 'meanabs' 'meanabsslp' 'ssi' 'var' 'rms' 'rng' 'mmnf' 'mmdf'
 'reFFT000' 'reFFT001' 'reFFT002' 'reFFT003' 'reFFT004' 'reFFT005'
 'reFFT006' 'reFFT007' 'reFFT008' 'reFFT009' 'reFFT010' 'reFFT011'
 'reFFT012' 'reFFT015' 'reFFT016' 'reFFT017' 'reFFT018' 'imFFT003'
 'imFFT004' 'imFFT006' 'imFFT008' 'imFFT009' 'imFFT010' 'imFFT011'
 'imFFT012' 'imFFT021' 'imFFT023' 'imFFT028' 'imFFT029' 'imFFT034' 'meanv'
 'stdr' 'mx' 'rngy' 'med' 'hjorth' 'se' 'acrol0000' 'acrol0001' 'acrol0002'
 'acrol0003' 'acrol0004' 'acrol0005' 'acrol0006' 'acrol0007' 'acrol0008'
 'acrol0009' 'acrol0010' 'acrol0011' 'acrol0012' 'acrol0013' 'acrol0014'
 'acrol0015' 'acrol0016' 'acrol0017' 'acrol0018' 'acrol0019' 'acrol0020'
 'acrol0021' 'acrol0022' 'acrol0023' 'acrol0024' 'acrol0025' 'acrol0026'
 'acrol0027' 'acrol0028' 'acrol0029' 'acrol0030' 'acrol0031' 'acrol0032'
 'acrol0033' 'acrol0034' 'acrol0035' 'acrol0036' 'acrol0037' 'acrol0038'
 'acrol0039' 'acrol0040' 'acrol0041' 'acrol0042' 'acrol0043' 'acrol0044'
 'acrol0045' 'acrol0046' 'acrol0047' 'acrol0048' 'acrol0049' 'acrol0050'
 'acrol0051' 'acrol0052' 'acrol0053' 'acrol0054' 'acrol0055' 'acrol0056'
 'acrol0057' 'acrol0058' 'acrol0059' 'acrol0060' 'acrol0061' 'acrol0062'
 'acrol0063' 'acrol0064' 'acrol0065' 'acrol0066' 'acrol0067' 'acrol0068'
 'acrol0069' 'acrol0070' 'acrol0071' 'acrol0072' 'acrol0073' 'acrol0074'
 'acrol0075' 'acrol0076' 'acrol0077' 'acrol0078' 'acrol0079' 'acrol0080'
 'acrol0081' 'acrol0082' 'acrol0083' 'acrol0084' 'acrol0085' 'acrol0086'
 'acrol0087' 'acrol0088' 'acrol0089' 'acrol0090' 'acrol0091' 'acrol0092'
 'acrol0093' 'acrol0094' 'acrol0095' 'acrol0096' 'acrol0097' 'acrol0098'
 'acrol0099' 'acrol0100' 'acrol0101' 'acrol0102' 'acrol0103' 'acrol0104'
 'acrol0105' 'acrol0106' 'acrol0107' 'acrol0108' 'acrol0109' 'acrol0110'
 'acrol0111' 'acrol0112' 'acrol0113' 'acrol0114' 'acrol0115' 'acrol0116'
 'acrol0117' 'acrol0118' 'acrol0119' 'acrol0120' 'acrol0121' 'acrol0122'
 'acrol0123' 'acrol0124' 'acrol0125' 'acrol0126' 'acrol0127' 'acrol0128'
 'acrol0129' 'acrol0130' 'acrol0131' 'acrol0132' 'acrol0133' 'acrol0134'
 'acrol0135' 'acrol0136' 'acrol0137' 'acrol0138' 'acrol0139' 'acrol0140'
 'acrol0141' 'acrol0142' 'acrol0143' 'acrol0144' 'acrol0145' 'acrol0146'
 'acrol0147' 'acrol0148' 'acrol0149' 'acrol0150' 'acrol0151' 'acrol0152'
 'acrol0153' 'acrol0154' 'acrol0155' 'acrol0156' 'acrol0157' 'acrol0158'
 'acrol0159' 'acrol0160' 'acrol0161' 'acrol0162' 'acrol0163' 'acrol0164'
 'acrol0165' 'acrol0166' 'acrol0167' 'acrol0168' 'acrol0169' 'acrol0170'
 'acrol0171' 'acrol0172' 'acrol0173' 'acrol0174' 'acrol0175' 'acrol0176'
 'acrol0177' 'acrol0178' 'acrol0179' 'acrol0180' 'acrol0181' 'acrol0182'
 'acrol0183' 'acrol0184' 'acrol0185' 'acrol0186' 'acrol0187' 'acrol0188'
 'acrol0189' 'acrol0190' 'acrol0194' 'acrol0195' 'acrol0196' 'acrol0197'
 'acrol0198' 'acrol0199' 'acrol0200' 'acrol0201' 'acrol0202' 'acrol0203'
 'acrol0204' 'acrol0205' 'acrol0206' 'acrol0208' 'acrol0209' 'acrol0213'
 'acrol0214' 'acrol0215' 'acrol0216' 'acrol0217' 'acrol0218' 'acrol0220'
 'acrol0221' 'acrol0223' 'acrol0224' 'acrol0227' 'acrol0228' 'acrol0229'
 'acrol0230' 'acrol0231' 'acrol0232' 'acrol0233' 'acrol0234' 'acrol0235'
 'acrol0236' 'acrol0237' 'acrol0238' 'acrol0239' 'acrol0240' 'acrol0241'
 'acrol0242' 'acrol0243' 'acrol0244' 'acrol0245' 'acrol0246' 'acrol0247'
 'acrol0248' 'acrol0249' 'acrol0250' 'acrol0251' 'acrol0252' 'acrol0253'
 'acrol0254' 'acrol0255' 'acrol0256' 'acrol0257' 'acrol0258' 'acrol0259'
 'acrol0260' 'acrol0261' 'acrol0262' 'acrol0263' 'acrol0264' 'acrol0265'
 'acrol0266' 'acrol0267' 'acrol0268' 'acrol0269' 'acrol0270' 'acrol0271'
 'acrol0272' 'acrol0273' 'acrol0274' 'acrol0275' 'acrol0276' 'acrol0277'
 'acrol0278' 'acrol0279' 'acrol0280' 'acrol0281' 'acrol0282' 'acrol0283'
 'acrol0284' 'acrol0285' 'acrol0286' 'acrol0287' 'acrol0288' 'acrol0289'
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 'acrol0942' 'acrol0943' 'acrol0944' 'acrol0945' 'acrol0946' 'acrol0947'
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 'acrol0972' 'acrol0973' 'acrol0974' 'acrol0975' 'acrol0976' 'acrol0977'
 'acrol0978' 'acrol1020' 'acrol1021' 'amFFT000' 'amFFT008' 'amFFT009'
 'amFFT010' 'amFFT011' 'amFFT012' 'phFFT000' 'phFFT008' 'phFFT009'
 'phFFT010' 'phFFT011' 'phFFT012']
['data/results1/fs_0_subfs_3_tr_0_ts_5.npz']
0 reFFT010 0.0766293408706
1 reFFT018 0.0447414833478
2 reFFT004 0.0435756307039
3 mmdf 0.0427619960262
4 phFFT012 0.0409017707002
5 mmnf 0.0398151146058
6 phFFT009 0.0391392334455
7 phFFT011 0.037165513411
8 phFFT010 0.0342619814778
9 phFFT008 0.0341353010342
10 reFFT017 0.0308057578195
11 amFFT012 0.0304204922638
12 stdr 0.0292270519868
13 reFFT009 0.0240039463317
14 amFFT009 0.0221175965759
15 rng 0.022048362632
16 rngy 0.022048362632
17 hjorth 0.0219253534307
18 amFFT011 0.0198409229263
19 reFFT015 0.0187915593818
20 amFFT010 0.0182735340709
21 amFFT008 0.0175001756734
22 reFFT016 0.0165941225547
23 reFFT002 0.00653771301826
24 acrol0032 0.00340014577388
25 acrol0031 0.00340013319156
26 acrol0033 0.00340006155269
27 acrol0030 0.00340001154337
28 acrol0034 0.00339986928283
29 acrol0029 0.00339986311755
30 acrol0028 0.00339962581376
31 acrol0035 0.00339961364424
32 acrol0027 0.00339932175299
33 acrol0036 0.00339927996672
34 acrol0026 0.00339890435312
35 acrol0037 0.00339886889514
36 acrol0025 0.00339844385987
37 acrol0038 0.00339835666302
38 acrol0024 0.00339785363518
39 acrol0039 0.00339773935668
40 acrol0023 0.00339731398713
41 acrol0040 0.0033970942003
42 acrol0022 0.00339664286186
43 acrol0041 0.00339630493581
44 acrol0021 0.00339594798976
45 acrol0042 0.00339541909452
46 acrol0020 0.00339508366624
47 acrol0043 0.003394449804
48 acrol0019 0.00339422193922
49 acrol0044 0.00339333469331
50 acrol0018 0.00339331132714
51 acrol0017 0.0033923844778
52 acrol0045 0.00339213678646
53 acrol0016 0.00339145191175
54 acrol0046 0.00339086292815
55 acrol0015 0.00339048089589
56 acrol0014 0.00338946088926
57 acrol0047 0.00338943214227
58 acrol0013 0.00338846063886
59 acrol0048 0.00338796374664
60 acrol0012 0.0033874741378
61 acrol0011 0.00338645068003
62 acrol0049 0.00338637249578
63 acrol0010 0.00338548139388
64 acrol0050 0.00338470110041
65 acrol0009 0.00338450441255
66 acrol0008 0.0033835589125
67 acrol0051 0.00338294317041
68 acrol0007 0.00338259942963
69 acrol0006 0.00338162777416
70 acrol0052 0.00338116625971
71 acrol0005 0.00338072451587
72 acrol0004 0.00337980966957
73 acrol0053 0.00337926344988
74 acrol0003 0.00337892611007
75 acrol0002 0.00337801551798
76 acrol0054 0.0033773530283
77 acrol0001 0.00337711918594
78 se 0.00337628133105
79 ssi 0.00337628133105
80 acrol0000 0.00337628133105
81 var 0.00337628133105
82 acrol0055 0.0033753646736
83 acrol0056 0.00337335037384
84 acrol0057 0.00337123229495
85 acrol0058 0.00336904338874
86 acrol0059 0.00336683858823
87 acrol0060 0.00336449503489
88 acrol0061 0.00336216789163
89 acrol0062 0.00335975362529
90 acrol0063 0.00335737992064
91 acrol0064 0.00335487484311
92 acrol0065 0.00335235007025
93 acrol0066 0.00334975492777
94 acrol0067 0.00334718545701
95 acrol0068 0.00334454413031
96 acrol0069 0.00334188317042
97 acrol0070 0.00333925279626
98 acrol0071 0.00333659329207
99 acrol0072 0.00333389265225
100 acrol0073 0.00333115022255
101 acrol0074 0.00332844142271
102 acrol0075 0.0033257281867
103 acrol0076 0.00332300757562
104 acrol0077 0.00332025757737
105 acrol0078 0.00331751716552
106 acrol0079 0.0033147416914
107 acrol0080 0.00331198657774
108 acrol0081 0.0033092046539
109 acrol0082 0.00330640884562
110 acrol0083 0.00330362061947
111 acrol0084 0.00330086759701
112 acrol0085 0.00329801628173
113 acrol0086 0.00329511541027
114 acrol0087 0.00329228582721
115 acrol0088 0.00328937272865
116 acrol0089 0.00328650235901
117 acrol0090 0.00328349980116
118 acrol0091 0.00328050085726
119 acrol0092 0.00327750205723
120 acrol0093 0.00327442366333
121 acrol0094 0.00327134130687
122 acrol0095 0.00326824757775
123 acrol0096 0.00326510570231
124 acrol0097 0.00326198090682
125 acrol0098 0.00325879531535
126 acrol0099 0.00325566151283
127 acrol0100 0.00325247761694
128 acrol0101 0.00324926223733
129 acrol0102 0.00324604936978
130 acrol0103 0.00324280360982
131 acrol0104 0.00323959940934
132 acrol0105 0.00323630807866
133 acrol0106 0.00323303320614
134 acrol0107 0.00322976883152
135 acrol0108 0.00322646478929
136 acrol0109 0.00322323502859
137 acrol0110 0.00322001007512
138 acrol0111 0.00321673326776
139 acrol0112 0.00321357835145
140 acrol0113 0.00321035439503
141 acrol0114 0.00320720906884
142 acrol0115 0.00320409237246
143 acrol0116 0.00320109640294
144 acrol0117 0.00319811969749
145 acrol0118 0.00319523084868
146 acrol0119 0.00319244085766
147 acrol0120 0.00318970218864
148 acrol0121 0.00318707462348
149 acrol0122 0.00318461959591
150 acrol0123 0.00318225052029
151 acrol0171 0.00318036236512
152 acrol0172 0.00318031896645
153 acrol0170 0.00318027090269
154 acrol0173 0.00318018501354
155 acrol0169 0.00318006473636
156 acrol0124 0.00317996504254
157 acrol0174 0.0031799630715
158 acrol0168 0.00317975626255
159 acrol0175 0.00317954298357
160 acrol0167 0.00317940996668
161 acrol0176 0.00317901739831
162 acrol0166 0.00317888286614
163 acrol0177 0.00317838246418
164 acrol0165 0.00317836498014
165 acrol0125 0.00317785556424
166 acrol0164 0.0031777668953
167 acrol0178 0.00317764800404
168 acrol0163 0.00317712574209
169 acrol0179 0.00317668496886
170 acrol0162 0.00317642302892
171 acrol0126 0.00317582815273
172 acrol0180 0.00317570187115
173 acrol0161 0.00317566860898
174 acrol0160 0.00317483553278
175 acrol0181 0.0031746081007
176 acrol0159 0.00317404057154
177 acrol0127 0.00317398350265
178 acrol0182 0.00317337846576
179 acrol0158 0.00317317216478
180 acrol0157 0.00317233710683
181 acrol0128 0.00317220349251
182 acrol0183 0.00317202163442
183 acrol0156 0.00317141039729
184 acrol0129 0.00317057296704
185 acrol0155 0.00317050246336
186 acrol0184 0.00317049177988
187 acrol0154 0.0031695881597
188 acrol0130 0.00316911244831
189 acrol0185 0.00316894330036
190 acrol0153 0.0031686495305
191 acrol0152 0.00316777317592
192 acrol0131 0.00316772098472
193 acrol0186 0.0031672553976
194 acrol0151 0.00316687550567
195 acrol0132 0.00316644831852
196 acrol0150 0.00316596885591
197 acrol0187 0.00316546929052
198 acrol0133 0.00316528146392
199 acrol0149 0.00316513131769
200 acrol0148 0.00316437747951
201 acrol0134 0.00316432510959
202 acrol0147 0.0031636414099
203 acrol0188 0.00316354111148
204 acrol0135 0.00316351420327
205 acrol0146 0.00316298301971
206 acrol0136 0.00316278329427
207 acrol0145 0.00316244259434
208 acrol0137 0.00316221335902
209 acrol0144 0.00316202299333
210 acrol0138 0.00316172623487
211 acrol0143 0.00316163889731
212 acrol0189 0.00316154598508
213 acrol0142 0.00316138659466
214 acrol0139 0.0031613565086
215 acrol0140 0.00316123647067
216 acrol0141 0.00316121200102
217 acrol0190 0.00315943434892
218 acrol0194 0.00314964298804
219 acrol0195 0.00314694037402
220 acrol0196 0.00314401986302
221 acrol0197 0.00314105488245
222 acrol0198 0.00313793308538
223 acrol0199 0.00313476762783
224 acrol0200 0.00313148739651
225 acrol0201 0.00312803396944
226 acrol0202 0.00312444809825
227 acrol0203 0.00312079671851
228 acrol0204 0.00311697036119
229 acrol0205 0.00311304148549
230 acrol0206 0.00310902873511
231 acrol0208 0.00310065305243
232 acrol0209 0.00309628764064
233 acrol0213 0.00307800474017
234 acrol0214 0.00307323527626
235 acrol0215 0.00306841582697
236 acrol0216 0.00306345857308
237 acrol0217 0.00305847355468
238 acrol0218 0.00305341389412
239 acrol0220 0.00304322103087
240 acrol0221 0.00303809691734
241 acrol0223 0.00302758532291
242 acrol0224 0.00302231603066
243 acrol0227 0.00300656724293
244 acrol0228 0.00300135224791
245 acrol0229 0.00299608620809
246 acrol0230 0.00299091779968
247 acrol0231 0.00298572392226
248 acrol0232 0.00298062501718
249 acrol0233 0.00297550534133
250 acrol0234 0.00297047696242
251 acrol0235 0.00296539794933
252 acrol0236 0.00296041249555
253 acrol0237 0.00295554992247
254 acrol0238 0.00295068783164
255 acrol0239 0.00294586170353
256 acrol0240 0.00294116274541
257 acrol0241 0.00293654168268
258 acrol0242 0.00293199036896
259 acrol0243 0.00292740968881
260 acrol0244 0.00292294756328
261 acrol0245 0.00291854973241
262 acrol0246 0.00291418173322
263 acrol0247 0.00290986080057
264 acrol0248 0.00290555371274
265 acrol0249 0.00290141458441
266 acrol0250 0.00289724182417
267 acrol0251 0.00289302725413
268 acrol0252 0.00288887419935
269 acrol0253 0.00288474527645
270 acrol0254 0.00288059959492
271 acrol0255 0.00287649802138
272 acrol0256 0.00287234815878
273 acrol0257 0.0028682296794
274 acrol0258 0.0028640925749
275 acrol0259 0.00286003940513
276 acrol0260 0.00285586930717
277 acrol0261 0.00285162641353
278 acrol0262 0.00284743609044
279 acrol0263 0.00284323788204
280 acrol0264 0.00283908618992
281 acrol0265 0.00283478759578
282 acrol0266 0.00283048790098
283 acrol0267 0.00282610829701
284 acrol0268 0.0028218418708
285 acrol0269 0.00281743425906
286 acrol0270 0.00281304095166
287 acrol0271 0.00280860463869
288 acrol0272 0.00280415890364
289 acrol0273 0.00279961246926
290 acrol0274 0.00279510879442
291 acrol0275 0.00279051132366
292 acrol0276 0.00278594474607
293 acrol0277 0.00278129288879
294 acrol0278 0.00277662744558
295 acrol0279 0.00277191072685
296 acrol0280 0.00276716851282
297 acrol0281 0.00276238474118
298 acrol0282 0.00275759132269
299 acrol0283 0.00275273773762
300 acrol0284 0.00274791142243
301 acrol0285 0.00274301085421
302 acrol0286 0.00273807423522
303 acrol0287 0.00273318141075
304 acrol0288 0.00272828210718
305 acrol0289 0.00272337662028
306 acrol0290 0.00271842682674
307 acrol0291 0.00271352036768
308 acrol0292 0.00270866672364
309 acrol0293 0.00270381622681
310 acrol0294 0.00269887043327
311 acrol0295 0.00269397867114
312 acrol0296 0.00268907118885
313 acrol0297 0.0026842507489
314 acrol0298 0.00267938455671
315 acrol0299 0.00267454657876
316 acrol0300 0.00266969338618
317 acrol0301 0.00266492549726
318 acrol0302 0.00266015537081
319 acrol0303 0.00265534915361
320 acrol0304 0.0026505797966
321 acrol0305 0.00264586499601
322 acrol0306 0.00264110989389
323 acrol0307 0.00263634554499
324 acrol0308 0.00263165292856
325 acrol0309 0.00262682018566
326 acrol0310 0.00262203559752
327 acrol0311 0.00261715425485
328 acrol0312 0.00261232325712
329 acrol0313 0.00260752122524
330 acrol0314 0.00260272800843
331 acrol0315 0.00259798511388
332 acrol0316 0.00259319358106
333 acrol0317 0.00258848289349
334 acrol0318 0.0025838311585
335 acrol0319 0.00257923320399
336 acrol0320 0.00257467888338
337 acrol0321 0.00257017451821
338 acrol0322 0.0025656610156
339 acrol0323 0.00256124194049
340 acrol0324 0.00255688517545
341 acrol0325 0.00255261523199
342 acrol0326 0.00254839082721
343 acrol0327 0.00254426447056
344 acrol0328 0.00254021580285
345 acrol0329 0.00253614630599
346 acrol0330 0.00253218064243
347 acrol0331 0.00252824853365
348 acrol0332 0.00252442152595
349 acrol0333 0.00252059370572
350 acrol0334 0.00251685883087
351 acrol0335 0.00251317065344
352 acrol0336 0.00250953661201
353 acrol0337 0.00250588240079
354 acrol0338 0.00250234666933
355 acrol0339 0.00249885930584
356 acrol0340 0.00249551828143
357 acrol0341 0.00249217543461
358 acrol0342 0.00248884006955
359 acrol0343 0.00248564783615
360 acrol0344 0.00248245645485
361 acrol0345 0.00247935242318
362 acrol0346 0.00247625574166
363 acrol0347 0.00247325524385
364 acrol0348 0.00247036163994
365 acrol0349 0.00246738003648
366 acrol0350 0.00246445313882
367 acrol0351 0.00246157429188
368 acrol0352 0.00245874198366
369 acrol0353 0.00245592886533
370 acrol0354 0.00245313664329
371 acrol0355 0.00245030329684
372 acrol0356 0.00244748189552
373 acrol0357 0.00244469145401
374 acrol0358 0.00244188043037
375 acrol0359 0.00243909548773
376 acrol0360 0.0024362758287
377 acrol0361 0.00243348480201
378 acrol0362 0.00243064389806
379 acrol0363 0.00242778222156
380 acrol0364 0.00242489108174
381 acrol0365 0.00242209072594
382 acrol0366 0.00241917854891
383 acrol0367 0.00241625985597
384 acrol0368 0.00241326143207
385 acrol0369 0.00241021272807
386 acrol0370 0.00240719961845
387 acrol0371 0.0024039822655
388 acrol0372 0.00240075075427
389 acrol0373 0.0023974436581
390 acrol0374 0.00239406046175
391 acrol0375 0.00239054535774
392 acrol0376 0.00238706524359
393 acrol0377 0.00238349457541
394 acrol0378 0.00237977646993
395 acrol0379 0.00237603502592
396 acrol0380 0.00237210094317
397 acrol0381 0.00236815940942
398 acrol0382 0.00236412956845
399 acrol0383 0.00236004637482
400 acrol0384 0.00235581004723
401 acrol0385 0.00235144674895
402 acrol0386 0.00234702299353
403 acrol0387 0.00234251899559
404 acrol0388 0.00233784366193
405 acrol0389 0.00233310478469
406 acrol0390 0.0023282570876
407 acrol0391 0.00232340560948
408 acrol0392 0.00231833912932
409 acrol0393 0.00231316157106
410 acrol0394 0.0023078790913
411 acrol0395 0.00230250218087
412 acrol0396 0.00229709004424
413 acrol0397 0.00229152896682
414 acrol0398 0.0022858653603
415 phFFT000 0.00228538634172
416 acrol0399 0.00228009291619
417 acrol0400 0.00227410884139
418 acrol0401 0.00226807312488
419 acrol0402 0.00226203387422
420 acrol0403 0.00225577985049
421 acrol0404 0.00224958523684
422 acrol0405 0.00224319559376
423 acrol0406 0.00223680523741
424 acrol0407 0.00223035565463
425 acrol0408 0.00222395951693
426 acrol0409 0.00221746831935
427 acrol0410 0.00221085625057
428 acrol0411 0.00220428506356
429 acrol0412 0.00219769182118
430 acrol0413 0.00219113153036
431 acrol0414 0.00218442791232
432 acrol0415 0.00217770875064
433 acrol0416 0.00217097587083
434 acrol0417 0.0021641742097
435 acrol0418 0.00215735879827
436 acrol0419 0.00215051660846
437 acrol0420 0.00214359686073
438 acrol0421 0.00213665040495
439 acrol0422 0.00212966273583
440 acrol0423 0.00212265950344
441 acrol0424 0.00211573232671
442 acrol0425 0.00210876184637
443 acrol0426 0.00210176278317
444 acrol0427 0.00209478130169
445 acrol0428 0.00208778022005
446 acrol0429 0.00208096772831
447 acrol0430 0.00207407385765
448 acrol0431 0.00206715027415
449 acrol0432 0.00206035390267
450 acrol0433 0.00205366832498
451 acrol0434 0.00204694540424
452 acrol0435 0.0020401595879
453 acrol0436 0.00203338604211
454 acrol0437 0.00202663465336
455 acrol0438 0.00201990231593
456 acrol0439 0.00201319288511
457 acrol0440 0.00200643124029
458 acrol0441 0.00199973010095
459 acrol0442 0.00199299331095
460 acrol0443 0.00198629072543
461 acrol0444 0.00197958551461
462 acrol0445 0.00197284918624
463 acrol0446 0.00196612945593
464 acrol0447 0.00195941640644
465 acrol0448 0.00195275653278
466 acrol0449 0.00194607052032
467 acrol0450 0.00193944098143
468 acrol0451 0.00193282759007
469 acrol0452 0.00192630375667
470 acrol0453 0.00191972267598
471 acrol0454 0.00191317285337
472 acrol0455 0.00190678939484
473 acrol0458 0.00188737934701
474 acrol0459 0.00188088161072
475 acrol0460 0.0018743762877
476 acrol0461 0.00186790120394
477 acrol0462 0.00186142984246
478 acrol0463 0.00185496718941
479 acrol0464 0.00184860635776
480 acrol0465 0.00184212113973
481 acrol0466 0.00183561718686
482 acrol0467 0.00182921242052
483 acrol0468 0.0018228223511
484 acrol0469 0.0018163478142
485 acrol0470 0.00180992170476
486 acrol0471 0.00180356393271
487 acrol0472 0.0017972435552
488 acrol0473 0.00179096834731
489 acrol0474 0.00178455426839
490 acrol0475 0.0017783132554
491 acrol0476 0.00177209450936
492 acrol0477 0.00176574621202
493 acrol0478 0.00175952102268
494 acrol0479 0.00175323871747
495 acrol0480 0.00174697726184
496 acrol0481 0.00174076056056
497 acrol0482 0.00173442078235
498 acrol0483 0.00172808495822
499 acrol0484 0.00172176467193
500 acrol0485 0.00171544714271
501 acrol0486 0.00170913192658
502 acrol0487 0.00170271774003
503 acrol0488 0.0016963141502
504 acrol0489 0.00168991742959
505 acrol0490 0.00168348700877
506 acrol0491 0.00167698271327
507 acrol0492 0.00167049137005
508 acrol0493 0.00166406356522
509 acrol0494 0.0016574664598
510 acrol0495 0.00165097420856
511 acrol0496 0.00164448756197
512 acrol0497 0.00163806909321
513 acrol0498 0.0016316342853
514 acrol0499 0.00162516431884
515 acrol0500 0.00161876681713
516 acrol0501 0.00161231787322
517 acrol0502 0.00160597217174
518 acrol0503 0.00159965544866
519 acrol0504 0.00159316822309
520 acrol0505 0.0015868420406
521 acrol0506 0.00158059844485
522 acrol0507 0.00157431914063
523 acrol0508 0.00156802885057
524 acrol0509 0.00156181002894
525 acrol0510 0.00155551701383
526 acrol0511 0.00154926412717
527 acrol0512 0.0015430150562
528 acrol0513 0.00153676253984
529 acrol0514 0.00153048969467
530 acrol0515 0.00152414975586
531 acrol0516 0.0015178538055
532 acrol0517 0.00151148953423
533 acrol0518 0.00150515663341
534 acrol0519 0.00149879728314
535 acrol0520 0.00149240272698
536 acrol0521 0.00148582567336
537 acrol0522 0.00147938761291
538 acrol0523 0.00147293741932
539 acrol0524 0.00146639941787
540 acrol0525 0.00145987309509
541 acrol0526 0.00145330504679
542 acrol0527 0.00144675099881
543 acrol0528 0.00144020883794
544 acrol0529 0.00143358146306
545 acrol0530 0.00142697079455
546 acrol0531 0.00142023067562
547 acrol0532 0.00141365723724
548 acrol0533 0.00140701061777
549 acrol0534 0.00140033469889
550 acrol0535 0.00139375693111
551 acrol0536 0.00138720616784
552 acrol0537 0.00138062741273
553 acrol0538 0.00137405210866
554 acrol0539 0.001367570147
555 acrol0540 0.00136108701007
556 acrol0541 0.00135458534929
557 acrol0542 0.00134808071102
558 acrol0543 0.00134157766339
559 acrol0544 0.00133518415805
560 acrol0545 0.00132877599623
561 acrol0546 0.00132231385543
562 acrol0547 0.0013159177704
563 acrol0548 0.00130938784414
564 acrol0549 0.00130297209438
565 acrol0550 0.0012965786626
566 acrol0551 0.0012899266488
567 acrol0552 0.00128341886282
568 acrol0553 0.00127684984222
569 acrol0554 0.00127024980332
570 acrol0555 0.00126356318192
571 acrol0556 0.001256797417
572 acrol0557 0.00125013733989
573 acrol0558 0.00124343195695
574 acrol0559 0.0012366117164
575 acrol0560 0.00122981371759
576 acrol0561 0.00122310790668
577 acrol0562 0.00121632418213
578 acrol0563 0.00120946107597
579 acrol0564 0.0012026198477
580 acrol0565 0.0011957082206
581 acrol0566 0.00118878296754
582 acrol0567 0.0011819138229
583 mx 0.0011815647824
584 acrol0568 0.00117500050023
585 acrol0569 0.00116804828089
586 acrol0570 0.00116122308669
587 acrol0571 0.00115419236306
588 acrol0572 0.00114709442472
589 acrol0573 0.00114007645505
590 acrol0574 0.00113292094703
591 acrol0575 0.0011256907605
592 acrol0576 0.00111843371053
593 acrol0577 0.00111115898773
594 acrol0578 0.00110377394477
595 acrol0579 0.00109638954713
596 acrol0580 0.00108895954464
597 acrol0581 0.00108156097321
598 acrol0582 0.00107412393362
599 acrol0583 0.00106664245594
600 acrol0584 0.0010591569989
601 acrol0585 0.00105161029332
602 acrol0586 0.0010440856466
603 acrol0587 0.00103650867989
604 acrol0588 0.00102890176531
605 acrol0589 0.00102125393007
606 acrol0590 0.00101352284231
607 acrol0591 0.00100584615444
608 acrol0592 0.000998232116238
609 acrol0593 0.00099043813734
610 acrol0594 0.000982452760026
611 acrol0595 0.000974470882749
612 acrol0596 0.000966563840005
613 acrol0597 0.000958385954178
614 acrol0598 0.000950195578451
615 acrol0599 0.000941972512132
616 acrol0600 0.000933760173095
617 acrol0601 0.000925576897823
618 acrol0602 0.000917252862743
619 acrol0603 0.000908929119152
620 acrol0604 0.000900658053843
621 acrol0605 0.000892367990878
622 acrol0606 0.000884196633625
623 acrol0607 0.000876026849989
624 acrol0608 0.000867837560555
625 acrol0609 0.000859755760915
626 acrol0610 0.000851654193561
627 acrol0611 0.000843593979892
628 acrol0612 0.00083570482655
629 acrol0613 0.00082775133083
630 acrol0614 0.000819824658647
631 acrol0615 0.000811811508965
632 acrol0616 0.000803942714897
633 acrol0617 0.000796194381609
634 acrol0618 0.000788379977658
635 acrol0619 0.000780643025702
636 acrol0620 0.000772889333996
637 acrol0621 0.000765405486598
638 acrol0622 0.000757845624913
639 acrol0623 0.000750549557627
640 acrol0624 0.000743301228976
641 acrol0625 0.00073610606131
642 acrol0626 0.000729226030246
643 acrol0627 0.000722515999064
644 acrol0628 0.000715958436713
645 acrol0629 0.000709663199424
646 acrol0630 0.000703422943907
647 acrol0631 0.000697333514701
648 acrol0632 0.000691440557301
649 acrol0633 0.000685705760933
650 acrol0634 0.000680384366149
651 acrol0635 0.000675017449836
652 acrol0636 0.00066982778636
653 acrol0637 0.000664806851308
654 acrol0638 0.000660036738648
655 acrol0639 0.000655428351033
656 acrol0640 0.00065089116442
657 acrol0641 0.000646588764202
658 acrol0642 0.000642523904952
659 acrol0643 0.000638565771857
660 acrol0644 0.000634762111686
661 acrol0645 0.000630959802622
662 acrol0646 0.000627513441681
663 acrol0647 0.00062411698894
664 acrol0648 0.000620969153448
665 acrol0649 0.000617999165916
666 acrol0650 0.00061499549712
667 acrol0651 0.000612243235018
668 acrol0652 0.000609655068923
669 acrol0653 0.00060720899174
670 acrol0654 0.000604847322635
671 acrol0655 0.000602779404895
672 acrol0656 0.000600727082434
673 acrol0657 0.000598773330359
674 acrol0658 0.000596999403157
675 acrol0659 0.000595259001142
676 acrol0660 0.000593761764988
677 acrol0661 0.000592069235516
678 acrol0662 0.000590463087011
679 acrol0663 0.000588927438237
680 acrol0664 0.000587487248192
681 acrol0665 0.00058599995167
682 acrol0666 0.000584619469165
683 acrol0667 0.000583147867638
684 acrol0668 0.00058176441364
685 acrol0669 0.000580298822011
686 acrol0670 0.000578886353691
687 acrol0671 0.000577538148985
688 acrol0672 0.000576181129716
689 acrol0673 0.000574781838978
690 acrol0674 0.000573310471944
691 acrol0675 0.000571740626667
692 acrol0676 0.000570121928687
693 acrol0677 0.000568621089212
694 acrol0678 0.000566981801692
695 acrol0679 0.000565113473872
696 acrol0680 0.000563239552181
697 acrol0681 0.000561236956906
698 acrol0682 0.000559199121902
699 acrol0683 0.000556888893083
700 acrol0684 0.000554584775334
701 acrol0685 0.000552275014856
702 acrol0686 0.000549696758482
703 acrol0687 0.00054696312058
704 acrol0688 0.000544037563221
705 acrol0689 0.000541101855636
706 acrol0690 0.000538080160074
707 acrol0691 0.000534932567818
708 acrol0692 0.000531613047829
709 acrol0693 0.00052821701708
710 acrol0694 0.000524634457439
711 acrol0695 0.000520927862119
712 acrol0696 0.000517021271923
713 acrol0697 0.000513115208162
714 acrol0698 0.000509029557504
715 acrol0699 0.000504680182791
716 acrol0700 0.000500255883781
717 acrol0701 0.000495680700867
718 acrol0702 0.000490891985395
719 acrol0703 0.000485924793299
720 acrol0704 0.000480914170937
721 acrol0705 0.000475830242936
722 acrol0706 0.000470593683248
723 acrol0707 0.000465270768962
724 acrol0708 0.000459773678501
725 acrol0709 0.000454066758718
726 acrol0710 0.000448544765212
727 acrol0711 0.000442818102534
728 acrol0712 0.000437102888832
729 acrol0713 0.0004312351119
730 acrol0714 0.000425277601281
731 acrol0715 0.00041948294261
732 acrol0716 0.000413573926363
733 acrol0717 0.000407965447027
734 acrol0718 0.000402173420429
735 acrol0719 0.000396339601555
736 acrol0720 0.000390644872976
737 acrol0721 0.000384810836325
738 acrol0722 0.000378912654989
739 acrol0723 0.000373047242726
740 acrol0724 0.000367177821497
741 acrol0725 0.000361088704744
742 acrol0726 0.00035502451336
743 acrol0727 0.000348930559403
744 acrol0728 0.000342866562396
745 acrol0729 0.000336594883198
746 acrol0730 0.00033040674556
747 acrol0731 0.000324025334748
748 acrol0732 0.000317868281208
749 acrol0733 0.000311651317179
750 acrol0734 0.000305336571235
751 acrol0735 0.000298835877516
752 acrol0736 0.000292426784191
753 acrol0737 0.000286018635548
754 acrol0738 0.000279620654215
755 acrol0739 0.000273109056933
756 acrol0740 0.00026663593327
757 acrol0741 0.000260311137886
758 acrol0742 0.000253944001381
759 acrol0743 0.000247657697037
760 acrol0744 0.000241396525197
761 acrol0745 0.000235212368076
762 acrol0746 0.000229021969015
763 acrol0747 0.00022298633069
764 acrol0748 0.000216926661768
765 acrol0749 0.000211044189275
766 acrol0750 0.000205075695294
767 acrol0751 0.000199349777029
768 acrol0752 0.00019343230264
769 acrol0753 0.000187802345195
770 acrol0754 0.000182197264817
771 acrol0755 0.000176678772546
772 acrol0902 0.000171162152852
773 acrol0756 0.000171047130592
774 acrol0903 0.000170502562478
775 acrol0900 0.000170398386704
776 acrol0899 0.000168436146718
777 acrol0898 0.00016589613929
778 acrol0757 0.000165751730196
779 acrol0905 0.000165504195039
780 acrol0897 0.000162249995952
781 acrol0906 0.000162038638946
782 acrol0758 0.000160125910767
783 acrol0896 0.000157926849259
784 acrol0907 0.000157543282244
785 acrol0759 0.000154668187361
786 acrol0895 0.00015282905126
787 acrol0908 0.000152319885209
788 acrol0760 0.000149131839674
789 acrol0909 0.000146685898956
790 acrol0894 0.000146477800868
791 acrol0761 0.000143989476145
792 acrol0893 0.000139979419237
793 acrol0910 0.000139978616127
794 acrol0762 0.000138718481029
795 acrol0763 0.00013341255111
796 acrol0911 0.000132666320257
797 acrol0892 0.000132599978603
798 acrol0764 0.000127906840515
799 acrol0891 0.000124657915725
800 acrol0912 0.000124471645113
801 acrol0765 0.000122627559751
802 acrol0766 0.000117618732872
803 acrol0913 0.000116502211445
804 acrol0890 0.000115593709981
805 acrol0914 0.000107390448773
806 acrol0768 0.00010700562555
807 acrol0889 0.000105682303548
808 acrol0769 0.000101589635641
809 acrol0915 9.83537485023e-05
810 acrol0888 9.54639267851e-05
811 acrol0916 8.89839750941e-05
812 acrol0887 8.42597691057e-05
813 imFFT010 8.18447902946e-05
814 acrol0917 7.86381368471e-05
815 acrol0886 7.29180620042e-05
816 acrol0918 6.83572290424e-05
817 acrol0885 6.08146101796e-05
818 acrol0884 4.7478669803e-05
819 acrol0920 4.63544794916e-05
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977 amFFT000 -0.00375649582584
978 intsgnl -0.00375649582584
979 reFFT000 -0.00375649582584
980 meanv -0.00375649582584
981 imFFT004 -0.00455053619553
982 imFFT011 -0.00505660882015
983 imFFT034 -0.00586626634597
984 meanabsslp -0.00699308156457
985 imFFT021 -0.00702156923283
986 imFFT023 -0.00762655971935
987 imFFT009 -0.00792303491442
988 imFFT028 -0.00792506431111
989 imFFT029 -0.00811903256971
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991 imFFT012 -0.00855434588748
992 imFFT006 -0.00933011302069
993 reFFT011 -0.0111941662133
994 imFFT008 -0.0143431740844
995 reFFT005 -0.0196819692878
996 reFFT008 -0.0423091638791
997 reFFT012 -0.0448433819424
998 reFFT007 -0.0527126994613
999 reFFT006 -0.103489473087






    

In [43]:

    

r, fs, subfs = 1, 0, 3
filn = glob.glob("data/results" + str(r) + "/fs_" + str(fs) + "_subfs_" + str(subfs) + "_*.npz")[0]
print filn
model_file = np.load(filn)
model = model_file['model']
#keep a list of the 1000 features kept
model_feat_scores = model[0].named_steps['feature_selection'].scores_
model_feat_scores = model[0].named_steps['feature_selection'].get_support(indices = True)
model_pca_var = model[0].named_steps['decomp'].explained_variance_
model_pca_var_rat = model[0].named_steps['decomp'].explained_variance_ratio_
model_pca_covar = model[0].named_steps['decomp'].get_covariance()
model_pca_mean = model[0].named_steps['decomp'].mean_
n_comp = model[0].named_steps['decomp'].n_components_
comp = model[0].named_steps['decomp'].components_
print len(model_pca_var), model_pca_var
print len(model_pca_var_rat), model_pca_var_rat
print model_pca_covar.shape, model_pca_covar
# plt.imshow(model_pca_covar)
print len(model_pca_mean), model_pca_mean
print n_comp
print comp.shape, comp
nfeat = 1000
feat_importance = np.zeros(nfeat)
for nc in range(len(comp)):
    feat_importance += comp[nc]*model_pca_var_rat[nc]
#     plt.plot(range(1000),comp[nc]*model_pca_var_rat[nc])
plt.plot(range(nfeat),feat_importance/nfeat)
print feat_importance*nfeat
sort_feat_imp_ind = np.array(feat_importance).argsort()[:][::-1]
print np.array(featnames)[sort_feat_imp_ind]


    

    




data/results1/fs_0_subfs_3_tr_5_ts_4.npz
20 [ 635.27304376  278.33611227   30.69938174    6.93535896    2.28809825
    2.09643049    1.78208177    1.6961141     1.55467524    1.51123534
    1.3664692     1.30327787    1.21026144    1.15230296    1.13857686
    1.09402407    1.07557543    1.0462369     0.99788984    0.99201795]
20 [ 0.63527304  0.27833611  0.03069938  0.00693536  0.0022881   0.00209643
  0.00178208  0.00169611  0.00155468  0.00151124  0.00136647  0.00130328
  0.00121026  0.0011523   0.00113858  0.00109402  0.00107558  0.00104624
  0.00099789  0.00099202]
(1000, 1000) [[ 27.37368911   0.92285356   0.0595558  ...,   0.12837082   0.12743588
    0.21891365]
 [  0.92285356  27.37368911   0.0595558  ...,   0.12837082   0.12743588
    0.21891365]
 [  0.0595558    0.0595558   27.24602051 ...,   0.79672983   0.79658456
    0.68288268]
 ..., 
 [  0.12837082   0.12837082   0.79672983 ...,  27.29621694   0.84533024
    0.7324894 ]
 [  0.12743588   0.12743588   0.79658456 ...,   0.84533024  27.29611624
    0.73233508]
 [  0.21891365   0.21891365   0.68288268 ...,   0.7324894    0.73233508
   27.09834563]]
1000 [ -1.74010466e-15  -1.74010466e-15   2.29597143e-17   1.26640950e-15
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  -1.57092782e-17  -1.02714511e-17   0.00000000e+00   1.20840601e-18
   3.62521804e-18  -3.14185563e-17   1.32924661e-17   3.62521804e-17
   1.20840601e-17   4.59194285e-17   9.30472630e-17  -1.81260902e-18
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   6.82749397e-17  -1.46217128e-16  -7.25043608e-18  -2.36847579e-16
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   1.29299443e-16  -1.28091037e-16  -4.22942105e-17  -2.41681203e-18
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   3.67355428e-15  -2.70682947e-16   7.73379848e-17   2.04945660e-15
  -8.12048841e-16   5.02696901e-16   1.58542869e-15  -3.86689924e-16
   1.27607675e-15  -4.64027909e-16  -5.80034886e-16   3.17085738e-15
   1.12140078e-15  -4.64027909e-16   3.90556823e-15  -3.09351939e-15
  -2.32013955e-15  -1.00539380e-15  -5.41365894e-16  -1.85611164e-15
   2.20413257e-15   1.27607675e-15  -7.73379848e-16  -1.12140078e-15
   2.62949148e-15  -1.43075272e-15   1.08273179e-15   3.01618141e-15
   2.62949148e-15  -1.93344962e-15   1.70143567e-15   9.66724811e-16
  -2.35880854e-15   6.57372871e-16   1.81744264e-15   3.09351939e-16
   2.12679458e-15  -3.20952637e-15   1.12140078e-15   6.57372871e-16
   9.66724811e-16  -1.23740776e-15   2.90017443e-15   1.23740776e-15
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   6.18703879e-16   8.50717833e-16  -3.86689924e-16  -3.32553335e-15
   1.54675970e-16  -8.89386826e-16   1.46942171e-15   2.35880854e-15
  -1.93344962e-16  -1.35341473e-15   4.25358917e-15  -3.09351939e-16
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  -2.12679458e-15   1.89478063e-15  -1.89478063e-15  -5.41365894e-16
   7.34710856e-16   7.73379848e-16  -3.01618141e-15  -2.78416745e-15
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   1.12140078e-15  -2.62949148e-15   9.66724811e-16  -2.04945660e-15
  -2.43614652e-15   3.09351939e-16   1.97211861e-15   3.09351939e-16
  -9.66724811e-16  -1.54675970e-16   6.18703879e-16  -1.46942171e-15
  -8.50717833e-16  -1.27607675e-15   5.22031398e-15   1.08273179e-15
   8.12048841e-16  -1.93344962e-15   3.86689924e-17  -1.54675970e-15
   2.20413257e-15  -2.32013955e-16   1.27607675e-15   9.66724811e-16
   2.90017443e-15  -9.66724811e-16  -1.16006977e-16  -6.96041864e-16
   1.97211861e-15   2.51348451e-15   1.16006977e-16  -1.97211861e-15
  -8.50717833e-16   8.12048841e-16  -1.46942171e-15  -4.25358917e-16
  -1.31474574e-15  -7.73379848e-17   1.74010466e-15   9.28055818e-16
   1.23740776e-15  -1.08273179e-15   2.70682947e-16  -2.16546358e-15
   1.54675970e-16  -1.16006977e-16   1.66276667e-15  -8.12048841e-16
  -1.93344962e-15   6.96041864e-16   7.34710856e-16  -1.00539380e-15
  -7.73379848e-16  -1.62409768e-15  -6.96041864e-16  -2.90017443e-15
   7.73379848e-17  -3.48020932e-16   1.16006977e-16  -7.34710856e-16
   5.41365894e-16  -2.32013955e-16   5.02696901e-16  -1.08273179e-15
   1.97211861e-15   8.12048841e-16  -1.35341473e-15  -1.58542869e-15
  -8.50717833e-16  -1.04406280e-15  -2.12679458e-15   4.64027909e-16
  -1.00539380e-15   1.62409768e-15   1.19873877e-15   3.09351939e-16
   1.85611164e-15   4.64027909e-16   2.12679458e-15   5.02696901e-16
   1.93344962e-16   1.23740776e-15   1.54675970e-16  -3.48020932e-16
  -1.97211861e-15   0.00000000e+00   4.64027909e-16   1.19873877e-15
  -2.43614652e-15  -8.89386826e-16  -3.48020932e-16   1.16006977e-16
  -6.57372871e-16   8.12048841e-16  -3.48020932e-16   1.54675970e-16
   1.54675970e-15   1.19873877e-15   1.08273179e-15   7.73379848e-17
  -4.25358917e-16   5.41365894e-16  -2.43614652e-15  -1.43075272e-15
  -3.13218839e-15  -9.28055818e-16  -1.66276667e-15  -1.81744264e-15
  -1.77877365e-15   1.31474574e-15  -1.97211861e-15   5.02696901e-16
   3.63488529e-15  -4.25358917e-16   2.08812559e-15  -8.89386826e-16
   1.39208373e-15   2.08812559e-15   2.39747753e-15  -4.25358917e-16
   3.51887831e-15   3.51887831e-15   7.34710856e-16   1.27607675e-15
   5.41365894e-16  -7.73379848e-17  -1.08273179e-15  -1.46942171e-15
   2.32013955e-16  -8.50717833e-16  -2.20413257e-15   7.73379848e-16
   1.04406280e-15   2.24280156e-15  -1.62409768e-15   3.09351939e-15
   3.24819536e-15  -9.28055818e-16  -1.77877365e-15  -1.12140078e-15
   8.50717833e-16  -1.54675970e-15  -1.08273179e-15   6.18703879e-16
  -2.16546358e-15   0.00000000e+00   7.73379848e-17   2.35880854e-15
  -3.32553335e-15  -3.09351939e-16   3.86689924e-17   1.74010466e-15
   1.16006977e-15  -6.57372871e-16   1.12140078e-15   1.00539380e-15
  -8.89386826e-16   1.89478063e-15   6.57372871e-16   1.97211861e-15
   1.31474574e-15   6.18703879e-16  -2.20413257e-15   2.78416745e-15
  -1.58542869e-15   2.16546358e-15   1.46942171e-15   1.23740776e-15
  -3.09351939e-16  -4.25358917e-16   2.20413257e-15  -1.08273179e-15
   1.35341473e-15   1.54675970e-15  -6.18703879e-16   8.12048841e-16
   4.64027909e-16  -1.16006977e-15   1.16006977e-16  -3.09351939e-16
  -3.17085738e-15   2.04945660e-15  -3.20952637e-15  -4.25358917e-16
   1.66276667e-15  -5.02696901e-16   1.54675970e-16  -1.66276667e-15
  -1.93344962e-15   3.09351939e-16   8.12048841e-16   5.02696901e-16
  -1.23740776e-15  -6.57372871e-16   2.08812559e-15  -1.04406280e-15
   1.35341473e-15   1.16006977e-16   6.96041864e-16  -1.16006977e-15
  -1.27607675e-15   1.85611164e-15   6.96041864e-16   1.31474574e-15
   1.12140078e-15  -8.89386826e-16  -2.86150544e-15   2.70682947e-16
  -1.12140078e-15  -8.89386826e-16  -2.55215350e-15  -1.62409768e-15
   3.01618141e-15   3.28686436e-15  -1.81744264e-15   2.32013955e-15
  -7.73379848e-16   1.97211861e-15   0.00000000e+00   1.93344962e-15
   9.28055818e-16   4.25358917e-16   1.39208373e-15   2.35880854e-15
   5.80034886e-16  -1.54675970e-15  -1.16006977e-16  -2.04945660e-15
  -1.54675970e-15   7.73379848e-17   7.34710856e-16  -7.73379848e-16
  -3.86689924e-16   1.43075272e-15   1.23740776e-15  -2.20413257e-15
  -2.55215350e-15  -3.48020932e-16  -3.86689924e-16  -3.09351939e-16
  -1.54675970e-16  -1.58542869e-15   1.00539380e-15   2.78416745e-15
  -1.54675970e-16   2.12679458e-15   2.51348451e-15   8.89386826e-16
   3.36420234e-15  -2.39747753e-15   2.74549846e-15   2.59082249e-15
  -1.08273179e-15   3.09351939e-16  -3.86689924e-17  -1.74010466e-15
   3.09351939e-16   7.73379848e-17  -3.13218839e-15   2.62949148e-15
  -8.50717833e-16   1.35341473e-15   3.17085738e-15  -4.09891320e-15
  -5.41365894e-16  -7.73379848e-17   4.64027909e-16   5.41365894e-16
   3.67355428e-15   2.86150544e-15   5.02696901e-16  -7.34710856e-16
   1.77877365e-15  -1.12140078e-15   5.41365894e-16   1.00539380e-15
  -1.31474574e-15  -1.00539380e-15  -3.86689924e-17   1.74010466e-15
   2.20413257e-15   7.73379848e-16   1.23740776e-15  -4.25358917e-16
  -1.31474574e-15   1.08273179e-15  -6.57372871e-16   8.12048841e-16
   7.73379848e-16  -2.16546358e-15  -2.70682947e-16  -5.41365894e-16
  -1.04406280e-15  -1.00539380e-15   5.80034886e-16   7.73379848e-17
   3.24819536e-15   8.89386826e-16   1.31474574e-15  -6.96041864e-16
   3.20952637e-15  -3.48020932e-16  -3.51887831e-15  -8.12048841e-16
  -4.64027909e-16  -2.66816048e-15  -2.66816048e-15   0.00000000e+00
   6.18703879e-16   3.48020932e-16   1.62409768e-15   9.66724811e-16
  -3.09351939e-16  -1.46942171e-15   7.73379848e-17   1.77877365e-15
  -1.54675970e-16   1.54675970e-16   7.73379848e-16   2.66816048e-15
   5.41365894e-16  -5.80034886e-16  -8.89386826e-16   1.46942171e-15
   5.80034886e-16   1.54675970e-16   5.02696901e-16  -6.18703879e-16
   1.66276667e-15   2.32013955e-16  -1.89478063e-15   3.86689924e-16
  -1.27607675e-15   1.66276667e-15  -7.34710856e-16   1.31474574e-15
  -3.86689924e-17   9.66724811e-16   8.12048841e-16   1.93344962e-15
  -2.43614652e-15   2.32013955e-16   1.85611164e-15  -9.28055818e-16
   2.78416745e-15   1.08273179e-15  -1.31474574e-15  -1.16006977e-16
  -4.25358917e-16   6.18703879e-16  -3.48020932e-16   6.57372871e-16
  -2.70682947e-16  -2.47481552e-15  -7.73379848e-17   1.39208373e-15
  -1.46942171e-15   1.54675970e-15   4.25358917e-16   5.80034886e-16
  -3.48020932e-16   2.20413257e-15   8.50717833e-16   1.43075272e-15
   0.00000000e+00   1.00539380e-15  -1.27607675e-15  -1.62409768e-15
   2.90017443e-15  -1.43075272e-15  -5.41365894e-16  -1.39208373e-15
  -1.74010466e-15  -1.00539380e-15   1.19873877e-15   2.70682947e-16
  -2.08812559e-15  -1.66276667e-15   1.35341473e-15   1.43075272e-15
   1.31474574e-15   2.47481552e-15   5.80034886e-16  -1.77877365e-15
  -3.67355428e-15  -1.81744264e-15  -1.23740776e-15  -4.06024420e-15
  -2.04945660e-15  -2.12679458e-15  -6.57372871e-16  -7.73379848e-16
  -3.86689924e-16   1.27607675e-15   1.23740776e-15  -1.93344962e-16
   1.35341473e-15   3.86689924e-17  -1.31474574e-15  -6.57372871e-16
   1.89478063e-15   2.86150544e-15  -7.73379848e-17   2.32013955e-16
  -1.19873877e-15   1.16006977e-16  -3.13218839e-15   1.74010466e-15
  -1.70143567e-15   8.50717833e-16   2.93884342e-15  -2.24280156e-15
  -1.54675970e-16  -1.08273179e-15   3.86689924e-16   2.70682947e-16
  -1.70143567e-15   3.86689924e-16  -1.74010466e-15  -8.21716089e-17
   2.22346706e-16   9.90892931e-16   5.70367638e-16   1.21807326e-15
  -5.89702134e-16   1.74010466e-16  -1.16973702e-15   7.73379848e-17
  -4.20525293e-16  -4.44693413e-16   5.80034886e-17   5.05113714e-16
   6.13870255e-16  -5.80034886e-16  -1.11173353e-16  -1.06339729e-16
   1.12623440e-15   2.80350195e-16  -3.28686436e-16  -2.36847579e-16
  -1.25674225e-16  -1.79327452e-15   8.41050585e-16  -4.97863277e-16
   7.58878976e-16  -1.41141822e-15   5.94535758e-16   8.41050585e-16
   5.51033142e-16   9.18388570e-17   3.52854556e-16  -5.41365894e-16
  -1.38725010e-15  -2.99684691e-16  -2.17513082e-16  -1.16006977e-16
  -1.11173353e-16  -3.33520060e-16  -9.66724811e-17   3.14185563e-16
  -3.77022676e-16   2.41681203e-17   4.97863277e-16  -1.93344962e-16
  -6.09036631e-16   9.37723066e-16   4.15691669e-16   1.20840601e-16
  -7.15376360e-16  -5.31698646e-17  -1.13590165e-15  -4.64027909e-16
  -6.28371127e-16   2.22346706e-16  -2.32013955e-16  -2.61015699e-16
  -1.64343218e-16  -2.36847579e-16   9.47390314e-16   6.09036631e-16
  -4.97863277e-16   2.78900108e-15  -9.08721322e-16   3.28686436e-16
   2.61015699e-16  -7.25043608e-17  -5.55866766e-16   2.61015699e-16
  -1.08273179e-15  -6.76707367e-16   4.39859789e-16   1.72077016e-15
   2.51348451e-16  -4.78528781e-16  -2.70682947e-16  -9.66724811e-17
   4.35026165e-16   1.13590165e-15   1.74010466e-16  -4.78528781e-16
   2.65849323e-16   8.41050585e-16   6.42871999e-16  -4.97863277e-16
   8.16882465e-16  -4.54360661e-16   4.15691669e-16  -4.59194285e-16
  -4.35026165e-16   7.54045352e-16  -1.61443043e-15  -1.54675970e-16
  -3.57688180e-16  -9.18388570e-17  -2.90017443e-16  -1.93344962e-17
  -9.66724811e-18  -8.45884209e-16   6.42871999e-16  -1.16006977e-16
   2.56182075e-16   3.86689924e-17   2.94851067e-16  -7.34710856e-16
   6.04203007e-16  -2.03012210e-16  -2.41681203e-17  -5.80034886e-17
   2.03012210e-16   2.22346706e-16  -3.86689924e-16  -3.28686436e-16
   3.62521804e-16  -6.42871999e-16   4.54360661e-16  -1.83677714e-16
  -3.57688180e-16   3.86689924e-17  -6.38038375e-16  -1.11173353e-16
  -4.25358917e-16  -5.99369383e-16   7.00875488e-16  -6.23537503e-16
  -9.66724811e-18   3.33520060e-16   2.41681203e-16   5.75201262e-16
  -6.91208240e-16   2.80350195e-16  -7.25043608e-17  -1.74010466e-16
   4.39859789e-16   1.45008722e-17   7.73379848e-17  -1.27607675e-15
  -2.65849323e-16  -1.49842346e-16  -8.70052330e-17   2.03012210e-16
   2.12679458e-16   3.86689924e-17   3.19019187e-16  -2.17513082e-16
   4.83362405e-18   2.07845834e-16   3.67355428e-16  -5.46199518e-16
  -4.83362405e-17   4.83362405e-18  -2.70682947e-16  -4.64027909e-16
   9.18388570e-17  -6.76707367e-17   2.99684691e-16   7.25043608e-17
  -2.41681203e-17  -7.34710856e-16  -2.12679458e-16   5.22031398e-16
  -2.07845834e-16  -7.39544480e-16   2.41681203e-17  -2.70682947e-16
  -6.47705623e-16   6.28371127e-17  -1.45008722e-17  -3.28686436e-16
  -3.23852812e-16  -4.83362405e-16  -5.80034886e-16   6.86374615e-16
   1.54675970e-16   5.80034886e-16   3.52854556e-16  -1.74010466e-16
   0.00000000e+00  -5.94535758e-16   4.35026165e-16  -2.46514827e-16
   8.36216961e-16   1.06339729e-16   1.45008722e-16   8.07215217e-16
   3.38353684e-17   2.46514827e-16   4.88196029e-16   3.96357172e-16
  -3.14185563e-16   2.75516571e-16  -1.45008722e-17   5.02696901e-16
  -4.35026165e-17  -1.69176842e-16   3.23852812e-16  -7.25043608e-16
  -2.12679458e-16   4.83362405e-17  -8.55551457e-16   6.18703879e-16]
20
(20, 1000) [[  3.87903357e-02   3.87903357e-02   5.58406726e-03 ...,   8.50946222e-03
    8.47000548e-03   1.18427268e-02]
 [ -5.15837819e-03  -5.15837819e-03   5.48118904e-02 ...,   5.62580581e-02
    5.62652862e-02   4.71093465e-02]
 [  3.83242683e-03   3.83242683e-03  -6.76627946e-02 ...,   3.10152767e-02
    3.14011154e-02   2.70820650e-02]
 ..., 
 [ -1.40854392e-02  -1.40854392e-02  -1.71955764e-03 ...,  -1.17382366e-03
   -4.45750840e-03   2.05775486e-01]
 [  7.36843671e-03   7.36843671e-03   2.73403361e-04 ...,  -1.24472476e-04
   -2.00925955e-03   9.97355238e-03]
 [  1.93997330e-03   1.93997330e-03  -5.64179297e-06 ...,  -2.04918990e-03
    1.73513780e-03   3.36356746e-02]]
[ 23.34370374  23.34370374  16.61966863  24.59337382  17.58007415
 -13.31345094 -17.64292953 -16.35837236  23.34370374  -4.93201122
  -5.33392351  -3.72544098  -1.08593751  -3.83144071  -1.51437273
  -0.3707299   -0.82366687  -2.08798002  -1.97964752  -2.1104275
  -2.13099999  -6.33189097  -3.70836569  -3.61885884  -3.66415485
  -4.12549493  -2.74075285  -7.31359563  -3.81129878  -4.61390076
  -5.02176815  -6.6458385   -7.42215539  -5.00225347  -7.66177402
  -6.5303392   -9.94540774  -9.89590205  -7.58627561 -10.62367032
  -9.3713918  -10.82788577  -8.42562972 -12.77832342 -11.84205457
  -9.98992749 -13.94986308 -10.61376492 -12.79321626 -11.95581506
 -14.12203012 -13.82524306  14.99547382  16.01645531  16.10108802
  15.90718883  15.62909803  16.36813014  15.83768238  15.07927745
  15.51937334  15.62583688  14.7696783   16.37899354  14.68148892
  15.71767608  15.92499038  16.19507901  16.20228653  16.03924705
  17.0408461   16.17014716  16.00829776  15.73784521  15.92823243
  16.04662889  16.43946159  16.04509546  16.25112413  16.18520059
  16.2506335   16.62860837  16.21061602  16.31789604  16.45220651
  16.15682145  16.45784587  16.39196259  16.46294381  16.09588712
  16.73562891  16.38138645  16.41815771  16.72044556  16.18452329
  16.28621861  16.43992859  16.46525449  16.22964573  16.39244814
  16.5925863   16.14982363  16.25767396  16.57212095  16.33263859
  16.4366363   16.50525387  16.3895086   16.12065998  16.65100568
  16.26770928  16.53717918  16.51672547  16.40420088  16.25258873
  16.34449122  16.48048366  16.2680085   16.4972413   16.47054934
  16.36036095  16.46724212  16.42685679  16.4725842   16.25508862
  16.56478593  16.49054347  16.34180295  16.4743532   16.35515529
  16.37893972  15.96796879  16.72770495  16.12257531  16.14115609
  16.53573432  16.40159832  16.46675438  16.55574958  16.59381505
  16.19040632  16.38480735  16.59975844  16.13292753  16.48966916
  16.29187138  16.34598639  16.4283661   16.49201293  16.36110308
  16.15090263  16.38063545  16.328005    16.30814992  16.49232946
  16.60055537  16.49370318  16.38522426  16.4888997   16.38572502
  16.43203807  16.45612615  16.48539634  16.34320529  16.66525239
  16.53000761  16.20136069  16.64168786  16.55398922  16.16929881
  16.32732084  16.4041925   16.39003725  16.47898871  16.53087241
  23.34370374  22.87632887  26.18264313  24.59337382  23.52115504
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 'reFFT467' 'reFFT468' 'imFFT325' 'reFFT469' 'imFFT337' 'imFFT339'
 'reFFT470' 'reFFT471' 'reFFT472' 'reFFT473' 'imFFT329' 'reFFT474'
 'reFFT475' 'reFFT476' 'reFFT477' 'reFFT478' 'imFFT381' 'reFFT479'
 'reFFT480' 'reFFT481' 'imFFT315' 'reFFT482' 'reFFT483' 'imFFT346'
 'reFFT484' 'reFFT485' 'reFFT486' 'reFFT487' 'imFFT365' 'reFFT488'
 'reFFT489' 'reFFT490' 'reFFT491' 'reFFT492' 'reFFT493' 'reFFT494'
 'reFFT495' 'imFFT342' 'reFFT496' 'reFFT497' 'reFFT498' 'reFFT499'
 'reFFT500' 'imFFT327' 'reFFT501' 'imFFT341' 'reFFT502' 'reFFT503'
 'reFFT504' 'reFFT505' 'reFFT506' 'reFFT507' 'reFFT508' 'reFFT509'
 'imFFT333' 'reFFT510' 'reFFT511' 'imFFT340' 'imFFT330' 'reFFT512'
 'imFFT350' 'imFFT000' 'imFFT001' 'imFFT002' 'imFFT003' 'imFFT360'
 'imFFT352' 'imFFT004' 'imFFT338' 'imFFT005' 'imFFT006' 'imFFT007'
 'imFFT008' 'imFFT009' 'imFFT010' 'imFFT011' 'imFFT012' 'imFFT359'
 'imFFT013' 'imFFT014' 'imFFT015' 'imFFT016' 'imFFT017' 'imFFT347'
 'imFFT018' 'imFFT019' 'imFFT020' 'imFFT021' 'imFFT022' 'imFFT023'
 'imFFT024' 'imFFT025' 'imFFT026' 'imFFT027' 'imFFT028' 'imFFT029'
 'imFFT030' 'imFFT031' 'imFFT348' 'imFFT032' 'imFFT033' 'imFFT034'
 'imFFT035' 'imFFT036' 'imFFT037' 'imFFT038' 'imFFT377' 'imFFT266'
 'imFFT039' 'imFFT040' 'imFFT041' 'imFFT042' 'imFFT043' 'imFFT044'
 'imFFT045' 'imFFT046' 'imFFT047' 'imFFT048' 'imFFT355' 'imFFT357'
 'imFFT049' 'imFFT050' 'imFFT051' 'imFFT052' 'imFFT354' 'imFFT353'
 'imFFT053' 'imFFT362' 'imFFT349' 'imFFT384' 'imFFT356' 'imFFT054'
 'imFFT055' 'imFFT056' 'imFFT057' 'imFFT058' 'imFFT059' 'imFFT060'
 'imFFT061' 'imFFT062' 'imFFT063' 'imFFT351' 'imFFT064' 'imFFT363'
 'imFFT065' 'imFFT066' 'imFFT067' 'imFFT068' 'imFFT069' 'imFFT070'
 'imFFT071' 'imFFT072' 'imFFT073' 'imFFT074' 'imFFT075' 'imFFT076'
 'imFFT077' 'imFFT078' 'imFFT079' 'imFFT080' 'imFFT081' 'imFFT082'
 'imFFT358' 'imFFT083' 'imFFT391' 'imFFT084' 'imFFT383' 'imFFT085'
 'imFFT086' 'imFFT087' 'imFFT088' 'imFFT089' 'imFFT090' 'imFFT091'
 'imFFT092' 'imFFT093' 'imFFT094' 'imFFT095' 'imFFT096' 'imFFT097'
 'imFFT098' 'imFFT099' 'imFFT100' 'imFFT101' 'imFFT102' 'imFFT103'
 'imFFT104' 'imFFT105' 'imFFT106' 'imFFT107' 'imFFT108' 'imFFT371'
 'imFFT361' 'imFFT109' 'imFFT110' 'imFFT111' 'imFFT112' 'imFFT113'
 'imFFT114' 'imFFT115' 'imFFT116' 'imFFT117' 'imFFT118' 'imFFT119'
 'imFFT120' 'imFFT121' 'imFFT122' 'imFFT123' 'imFFT124' 'imFFT125'
 'imFFT126' 'imFFT127' 'imFFT128' 'imFFT129' 'imFFT382' 'imFFT130'
 'imFFT131' 'imFFT132' 'imFFT133' 'imFFT134' 'imFFT135' 'imFFT392'
 'imFFT136' 'imFFT137' 'imFFT138' 'imFFT139' 'imFFT140' 'imFFT393'
 'imFFT141' 'imFFT142' 'imFFT143' 'imFFT144' 'imFFT145' 'imFFT146'
 'imFFT147' 'imFFT148' 'imFFT149' 'imFFT150' 'reFFT155' 'imFFT151'
 'imFFT152' 'imFFT153' 'imFFT396' 'imFFT154' 'imFFT155' 'imFFT156'
 'imFFT157' 'imFFT158' 'imFFT159' 'imFFT160' 'imFFT161' 'imFFT162'
 'imFFT163' 'imFFT164' 'imFFT165' 'imFFT166' 'imFFT167' 'imFFT168'
 'imFFT169' 'imFFT170' 'imFFT171' 'imFFT172' 'imFFT173' 'imFFT394'
 'imFFT174' 'imFFT399' 'imFFT175' 'imFFT176' 'imFFT177' 'imFFT178'
 'imFFT385' 'imFFT179' 'imFFT180' 'imFFT181' 'imFFT182' 'imFFT183'
 'imFFT184' 'imFFT185' 'imFFT186' 'imFFT187' 'imFFT188' 'imFFT189'
 'imFFT190' 'imFFT191' 'imFFT192' 'imFFT193' 'imFFT194' 'imFFT397'
 'imFFT195' 'imFFT196' 'imFFT197' 'imFFT198' 'imFFT199' 'imFFT200'
 'imFFT201' 'imFFT202' 'imFFT203' 'imFFT204' 'imFFT205' 'imFFT206'
 'imFFT445' 'imFFT389' 'imFFT207' 'imFFT390' 'imFFT403' 'imFFT378'
 'imFFT408' 'imFFT401' 'imFFT437' 'imFFT402' 'imFFT411' 'imFFT208'
 'imFFT209' 'imFFT210' 'imFFT398' 'imFFT211' 'imFFT212' 'imFFT213'
 'imFFT405' 'imFFT414' 'imFFT214' 'imFFT215' 'imFFT216' 'imFFT395'
 'imFFT217' 'imFFT409' 'imFFT400' 'imFFT218' 'imFFT422' 'imFFT406'
 'imFFT219' 'imFFT220' 'imFFT221' 'imFFT222' 'imFFT223' 'imFFT429'
 'imFFT224' 'imFFT225' 'imFFT226' 'imFFT227' 'imFFT410' 'imFFT228'
 'imFFT424' 'imFFT370' 'imFFT412' 'imFFT446' 'imFFT447' 'imFFT436'
 'imFFT415' 'imFFT407' 'imFFT420' 'imFFT432' 'imFFT427' 'imFFT421'
 'imFFT413' 'imFFT438' 'imFFT449' 'imFFT443' 'imFFT441' 'imFFT444'
 'imFFT433' 'imFFT426' 'imFFT430' 'imFFT448' 'imFFT423' 'imFFT442'
 'imFFT453' 'imFFT456' 'imFFT458' 'imFFT461' 'imFFT428' 'imFFT460'
 'imFFT451' 'imFFT416' 'imFFT463' 'imFFT418' 'imFFT435' 'imFFT454'
 'imFFT452' 'imFFT419' 'imFFT431' 'imFFT457' 'imFFT417' 'imFFT425'
 'imFFT439' 'imFFT404' 'imFFT455' 'imFFT440' 'imFFT229' 'imFFT434'
 'imFFT450' 'imFFT464' 'imFFT369' 'imFFT459' 'imFFT462' 'imFFT230'
 'imFFT231' 'imFFT232' 'imFFT233' 'imFFT234' 'imFFT235' 'imFFT236'
 'imFFT237' 'imFFT238' 'imFFT239' 'imFFT240' 'imFFT241' 'imFFT242'
 'imFFT243' 'imFFT244' 'imFFT245' 'imFFT246' 'imFFT247' 'imFFT248'
 'imFFT364' 'imFFT368' 'imFFT249' 'imFFT250' 'imFFT251' 'imFFT252'
 'imFFT253' 'imFFT254' 'imFFT255' 'imFFT256' 'imFFT257' 'imFFT258'
 'imFFT259' 'imFFT260' 'imFFT261' 'imFFT262' 'imFFT263' 'imFFT465'
 'imFFT265' 'imFFT367' 'reFFT159' 'var' 'imFFT366' 'reFFT049' 'reFFT069'
 'reFFT111' 'reFFT072' 'reFFT143' 'reFFT088' 'reFFT146' 'reFFT060'
 'meanabsslp' 'reFFT134' 'reFFT121' 'reFFT118' 'reFFT079' 'reFFT082'
 'reFFT104' 'reFFT117' 'reFFT147' 'reFFT090' 'reFFT114' 'reFFT153'
 'reFFT144' 'reFFT091' 'reFFT085' 'reFFT097' 'reFFT135' 'reFFT133'
 'reFFT127' 'reFFT105' 'reFFT123' 'reFFT137' 'reFFT141' 'reFFT095'
 'reFFT152' 'reFFT107' 'reFFT102' 'reFFT098' 'reFFT100' 'reFFT116'
 'reFFT076' 'reFFT067' 'reFFT065' 'reFFT140' 'reFFT063' 'reFFT075'
 'reFFT055' 'reFFT084' 'reFFT139' 'reFFT126' 'reFFT101' 'reFFT071'
 'reFFT092' 'reFFT150' 'reFFT115' 'reFFT078' 'reFFT066' 'reFFT151'
 'reFFT086' 'reFFT138' 'reFFT136' 'reFFT120' 'reFFT070' 'reFFT130'
 'reFFT042' 'reFFT109' 'reFFT036' 'reFFT128' 'reFFT099' 'reFFT108'
 'reFFT125' 'reFFT094' 'reFFT142' 'reFFT106' 'reFFT083' 'reFFT131'
 'reFFT149' 'reFFT062' 'reFFT132' 'reFFT124' 'reFFT074' 'reFFT096'
 'reFFT089' 'reFFT081' 'reFFT103' 'reFFT093' 'reFFT057' 'reFFT059'
 'reFFT077' 'reFFT061' 'reFFT047' 'reFFT145' 'reFFT046' 'reFFT119'
 'reFFT058' 'reFFT073' 'reFFT050' 'reFFT148' 'reFFT064' 'reFFT129'
 'reFFT080' 'reFFT113' 'reFFT122' 'reFFT112' 'reFFT087' 'reFFT033'
 'reFFT068' 'reFFT054' 'reFFT056' 'reFFT048' 'reFFT032' 'reFFT051'
 'reFFT110' 'reFFT053' 'reFFT045' 'reFFT034' 'reFFT037' 'reFFT052'
 'reFFT044' 'reFFT035' 'reFFT040' 'reFFT039' 'reFFT038' 'reFFT031'
 'reFFT041' 'reFFT043' 'reFFT160' 'arco2' 'arco3' 'shist2' 'arco1' 'mdf'
 'mnf' 'mmnf' 'mmdf' 'reFFT005' 'reFFT002' 'reFFT003' 'reFFT001' 'shist1'
 'reFFT007' 'shist3' 'reFFT004' 'reFFT008' 'ssc' 'reFFT012' 'reFFT009'
 'wamp' 'reFFT000' 'reFFT014' 'reFFT010' 'reFFT006' 'reFFT011' 'reFFT017'
 'reFFT013' 'reFFT021' 'reFFT019' 'reFFT016' 'reFFT015' 'reFFT024'
 'reFFT026' 'reFFT018' 'reFFT020' 'reFFT023' 'reFFT028' 'reFFT022'
 'reFFT027' 'rms' 'reFFT030' 'reFFT025' 'reFFT029' 'wavl' 'rng']