In [1]:

    

%matplotlib inline

import numpy as np

try:
    import cPickle as pickle
except ImportError:
    import pickle as pickle
    
import sys
sys.path.append('../src')

import PatternEvaluator as pe
import SamplingPattern as sp

import seaborn as sns
import pandas as pd

mype = pe.PatternEvaluator()
mype.load_sens('../data/sens_31ch_128x128.npz')


    

In [2]:

    

try:
    import mkl
    mkl.set_num_threads(1)
except ImportError:
    pass


    

In [3]:

    

dat  = np.load('../data/capi_pats_128.npz')


    

In [4]:

    

cap4 = dat['cap4']
cap8 = dat['cap8']
cap16 = dat['cap16']


    

In [5]:

    

pat = sp.SamplingPattern()
pat.sampling = np.ones((128,128))
mype.eval_pattern(pat)


    

    




Using normfac of 31
Elapased: 7.50742220879s
Elapased: 7.15339422226s
[ 1.]
[ 1.]

In [ ]:

    

cap4_pats = []
for j in range(cap4.shape[0]):
    pat = sp.SamplingPattern()
    pat.sampling = cap4[j,...].copy()
    mype.eval_pattern(pat)
    cap4_pats.append(pat)


    

In [13]:

    

cap8_pats = []
for j in range(cap8.shape[0]):
    pat = sp.SamplingPattern()
    pat.sampling = cap8[j,...].copy()
    mype.eval_pattern(pat)
    cap8_pats.append(pat)


    

    




Using normfac of 31
Elapased: 6.70600295067s
Elapased: 196.232702971s
[ 0.6917784]
[  2.06371179e-05]
Using normfac of 31
Elapased: 6.63156700134s
Elapased: 93.1075110435s
[ 0.47491738]
[ 0.00018723]
Using normfac of 31
Elapased: 6.59952306747s
Elapased: 74.0610351562s
[ 0.49997587]
[ 0.00033288]
Using normfac of 31
Elapased: 6.70148301125s
Elapased: 52.3364388943s
[ 0.48301431]
[ 0.00033672]
Using normfac of 31
Elapased: 6.75003910065s
Elapased: 66.326485157s
[ 0.52424726]
[ 0.00017438]
Using normfac of 31
Elapased: 6.73936414719s
Elapased: 154.108430147s
[ 0.51099389]
[  8.75151778e-05]
Using normfac of 31
Elapased: 6.72156691551s
Elapased: 136.779484034s
[ 0.48241748]
[ 0.00018214]
Using normfac of 31
Elapased: 7.26346111298s
Elapased: 71.6315910816s
[ 0.4839625]
[ 0.0002807]
Using normfac of 31
Elapased: 6.71988797188s
Elapased: 58.9523699284s
[ 0.47337738]
[ 0.00062355]
Using normfac of 31
Elapased: 6.64993596077s
Elapased: 355.728889942s
[ 0.77576864]
[  2.45833761e-05]
Using normfac of 31
Elapased: 6.64611506462s
Elapased: 141.914307117s
[ 0.54823955]
[  3.51335730e-05]
Using normfac of 31
Elapased: 6.6462700367s
Elapased: 55.4606389999s
[ 0.50684797]
[ 0.00024408]
Using normfac of 31
Elapased: 6.66674113274s
Elapased: 199.927875996s
[ 0.58627025]
[  3.35418506e-05]
Using normfac of 31
Elapased: 7.36388611794s
Elapased: 68.0651478767s
[ 0.51421908]
[ 0.00016897]
Using normfac of 31
Elapased: 7.31943821907s
Elapased: 109.955765009s
[ 0.47397559]
[ 0.00073169]

In [15]:

    

for j in cap8_pats:
    print j.hi_eigs


    

    




[ 0.6917784]
[ 0.47491738]
[ 0.49997587]
[ 0.48301431]
[ 0.52424726]
[ 0.51099389]
[ 0.48241748]
[ 0.4839625]
[ 0.47337738]
[ 0.77576864]
[ 0.54823955]
[ 0.50684797]
[ 0.58627025]
[ 0.51421908]
[ 0.47397559]

In [34]:

    

for j in cap8_pats:
    print j.low_eigs


    

    




[  5.60568869e-09]
[  5.08577252e-08]
[  9.04204765e-08]
[  9.14650032e-08]
[  4.73671534e-08]
[  2.37718680e-08]
[  4.94756957e-08]
[  7.62456159e-08]
[  1.69376657e-07]
[  6.67761624e-09]
[  9.54338073e-09]
[  6.63005986e-08]
[  9.11101899e-09]
[  4.58987545e-08]
[  1.98750738e-07]

In [ ]: