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In [29]:

    
import sys
sys.dont_write_bytecode = True
from __future__ import division
import pandas as pd
pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier
from collections import OrderedDict
from dataMethods import *
from testRuns import *



In [6]:

    
lstimLeftHRs = []
lstimRightHRs = []
lstimLeftKs = []
lstimRightKs = []
lstimLeftHitRTs = []
lstimRightHitRTs = []
lstimLeftFaRTs = []
lstimRightFaRTs = []
lstimLeftdPs = []
lstimRightdPs = []

for run in lstimTestRuns:
    stats = extractPerformance(run[0],run[1])
    lHits = stats['lHits']
    rHits = stats['rHits']
    lMisses = stats['lMisses']
    rMisses = stats['rMisses']
    lFAs = stats['lFAs']
    rFAs = stats['rFAs']
    lCRs = stats['lCRs']
    rCRs = stats['rCRs']
    lstimLeftHRs.append(stats['lHRs'])
    lstimRightHRs.append(stats['rHRs'])
    lstimLeftKs.append(stats['lKs'])
    lstimRightKs.append(stats['rKs'])
    lstimLeftHitRTs.append(stats['lHitRTs'])
    lstimRightHitRTs.append(stats['rHitRTs'])
    lstimLeftFaRTs.append(stats['lFaRTs'])
    lstimRightFaRTs.append(stats['rFaRTs'])
    lstimLeftdPs.append(stats['ldPs'])
    lstimRightdPs.append(stats['rdPs'])

lstimLeftHRChart = pd.DataFrame(lstimLeftHRs, index=lstimTestRuns, columns=HFconds.keys())
lstimRightHRChart = pd.DataFrame(lstimRightHRs, index=lstimTestRuns, columns=HFconds.keys())
lstimLeftKChart = pd.DataFrame(lstimLeftKs, index=lstimTestRuns, columns=HFconds.keys())
lstimRightKChart = pd.DataFrame(lstimRightKs, index=lstimTestRuns, columns=HFconds.keys())
lstimLeftHitRTChart = pd.DataFrame(lstimLeftHitRTs, index=lstimTestRuns, columns=HFconds.keys())
lstimRightHitRTChart = pd.DataFrame(lstimRightHitRTs, index=lstimTestRuns, columns=HFconds.keys())
lstimLeftFaRTChart = pd.DataFrame(lstimLeftFaRTs, index=lstimTestRuns, columns=HFconds.keys())
lstimRightFaRTChart = pd.DataFrame(lstimRightFaRTs, index=lstimTestRuns, columns=HFconds.keys())
lstimLeftdPChart = pd.DataFrame(lstimLeftdPs, index=lstimTestRuns, columns=HFconds.keys())
lstimRightdPChart = pd.DataFrame(lstimRightdPs, index=lstimTestRuns, columns=HFconds.keys())



In [7]:

    
rstimLeftHRs = []
rstimRightHRs = []
rstimLeftKs = []
rstimRightKs = []
rstimLeftHitRTs = []
rstimRightHitRTs = []
rstimLeftFaRTs = []
rstimRightFaRTs = []
rstimLeftdPs = []
rstimRightdPs = []

for run in rstimTestRuns:
    stats = extractPerformance(run[0],run[1])
    lHits = stats['lHits']
    rHits = stats['rHits']
    lMisses = stats['lMisses']
    rMisses = stats['rMisses']
    lFAs = stats['lFAs']
    rFAs = stats['rFAs']
    lCRs = stats['lCRs']
    rCRs = stats['rCRs']
    rstimLeftHRs.append(stats['lHRs'])
    rstimRightHRs.append(stats['rHRs'])
    rstimLeftKs.append(stats['lKs'])
    rstimRightKs.append(stats['rKs'])
    rstimLeftHitRTs.append(stats['lHitRTs'])
    rstimRightHitRTs.append(stats['rHitRTs'])
    rstimLeftFaRTs.append(stats['lFaRTs'])
    rstimRightFaRTs.append(stats['rFaRTs'])
    rstimLeftdPs.append(stats['ldPs'])
    rstimRightdPs.append(stats['rdPs'])
        
rstimLeftHRChart = pd.DataFrame(rstimLeftHRs, index=rstimTestRuns, columns=HFconds.keys())
rstimRightHRChart = pd.DataFrame(rstimRightHRs, index=rstimTestRuns, columns=HFconds.keys())
rstimLeftKChart = pd.DataFrame(rstimLeftKs, index=rstimTestRuns, columns=HFconds.keys())
rstimRightKChart = pd.DataFrame(rstimRightKs, index=rstimTestRuns, columns=HFconds.keys())
rstimLeftHitRTChart = pd.DataFrame(rstimLeftHitRTs, index=rstimTestRuns, columns=HFconds.keys())
rstimRightHitRTChart = pd.DataFrame(rstimRightHitRTs, index=rstimTestRuns, columns=HFconds.keys())
rstimLeftFaRTChart = pd.DataFrame(rstimLeftFaRTs, index=rstimTestRuns, columns=HFconds.keys())
rstimRightFaRTChart = pd.DataFrame(rstimRightFaRTs, index=rstimTestRuns, columns=HFconds.keys())
rstimLeftdPChart = pd.DataFrame(rstimLeftdPs, index=rstimTestRuns, columns=HFconds.keys())
rstimRightdPChart = pd.DataFrame(rstimRightdPs, index=rstimTestRuns, columns=HFconds.keys())



In [8]:

    
shamLeftHRs = []
shamRightHRs = []
shamLeftKs = []
shamRightKs = []
shamLeftHitRTs = []
shamRightHitRTs = []
shamLeftFaRTs = []
shamRightFaRTs = []
shamLeftdPs = []
shamRightdPs = []

for run in shamTestRuns:
    stats = extractPerformance(run[0],run[1])
    lHits = stats['lHits']
    rHits = stats['rHits']
    lMisses = stats['lMisses']
    rMisses = stats['rMisses']
    lFAs = stats['lFAs']
    rFAs = stats['rFAs']
    lCRs = stats['lCRs']
    rCRs = stats['rCRs']
    shamLeftHRs.append(stats['lHRs'])
    shamRightHRs.append(stats['rHRs'])
    shamLeftKs.append(stats['lKs'])
    shamRightKs.append(stats['rKs'])
    shamLeftHitRTs.append(stats['lHitRTs'])
    shamRightHitRTs.append(stats['rHitRTs'])
    shamLeftFaRTs.append(stats['lFaRTs'])
    shamRightFaRTs.append(stats['rFaRTs'])
    shamLeftdPs.append(stats['ldPs'])
    shamRightdPs.append(stats['rdPs'])
        
shamLeftHRChart = pd.DataFrame(shamLeftHRs, index=shamTestRuns, columns=HFconds.keys())
shamRightHRChart = pd.DataFrame(shamRightHRs, index=shamTestRuns, columns=HFconds.keys())
shamLeftKChart = pd.DataFrame(shamLeftKs, index=shamTestRuns, columns=HFconds.keys())
shamRightKChart = pd.DataFrame(shamRightKs, index=shamTestRuns, columns=HFconds.keys())
shamLeftHitRTChart = pd.DataFrame(shamLeftHitRTs, index=shamTestRuns, columns=HFconds.keys())
shamRightHitRTChart = pd.DataFrame(shamRightHitRTs, index=shamTestRuns, columns=HFconds.keys())
shamLeftFaRTChart = pd.DataFrame(shamLeftFaRTs, index=shamTestRuns, columns=HFconds.keys())
shamRightFaRTChart = pd.DataFrame(shamRightFaRTs, index=shamTestRuns, columns=HFconds.keys())
shamLeftdPChart = pd.DataFrame(shamLeftdPs, index=shamTestRuns, columns=HFconds.keys())
shamRightdPChart = pd.DataFrame(shamRightdPs, index=shamTestRuns, columns=HFconds.keys())



In [9]:

    
lstimLeftHRChart



In [10]:

    
lstimRightHRChart



In [11]:

    
rstimLeftHRChart



In [12]:

    
rstimRightHRChart



In [13]:

    
shamLeftHRChart



In [14]:

    
shamRightHRChart



In [15]:

    
lstimLeftKChart



In [16]:

    
lstimRightKChart



In [17]:

    
rstimLeftKChart



In [18]:

    
rstimRightKChart



In [19]:

    
shamLeftKChart



In [20]:

    
shamRightKChart



In [21]:

    
lstimLeftdPChart



In [22]:

    
lstimRightdPChart



In [23]:

    
rstimLeftdPChart



In [24]:

    
rstimRightdPChart



In [25]:

    
shamLeftdPChart



In [26]:

    
shamRightdPChart



In [ ]:

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	0.8750	0.8125	0.8750	0.7500	0.8750	0.7500
(3, 2)	0.8125	0.8125	0.7500	0.5625	0.5000	0.6250
(4, 3)	1.0000	1.0000	0.9375	1.0000	0.9375	1.0000
(5, 2)	0.8125	1.0000	0.9375	0.8125	0.5625	0.5625
(6, 3)	0.6875	0.5625	0.5625	0.6875	0.5000	0.6875

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	1.000	0.5000	0.5000	0.9375	0.8125	0.8750
(3, 2)	0.875	0.5000	0.3125	0.6250	0.5625	0.4375
(4, 3)	1.000	0.6250	0.7500	0.9375	1.0000	0.9375
(5, 2)	1.000	0.5000	0.5000	0.6250	0.8125	0.6250
(6, 3)	0.750	0.3125	0.1875	0.5625	0.5625	0.6250

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	0.8125	1.0000	0.8750	0.9375	0.750000	0.8125
(3, 1)	0.8000	0.6250	1.0000	0.4000	0.533333	0.4375
(4, 1)	1.0000	1.0000	1.0000	1.0000	1.000000	0.9375
(5, 3)	0.9375	0.9375	0.9375	0.8125	0.687500	0.8750
(6, 2)	0.5000	0.7500	0.5625	0.8125	0.625000	0.5000

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	0.875000	0.4375	0.3750	0.7500	0.7500	0.750000
(3, 1)	0.933333	0.4375	0.4375	0.6250	0.4375	0.533333
(4, 1)	1.000000	0.5625	0.5625	0.8750	0.8125	0.937500
(5, 3)	0.812500	0.5000	0.3750	0.6875	0.5000	0.562500
(6, 2)	0.687500	0.3750	0.1250	0.5625	0.5000	0.375000

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 2)	0.8125	1.0000	0.9375	0.8125	0.8750	0.9375
(3, 3)	0.6250	0.6875	0.7500	0.6250	0.5625	0.6875
(4, 2)	1.0000	1.0000	1.0000	0.9375	0.9375	1.0000
(5, 1)	1.0000	0.9375	0.9375	0.9375	0.5625	0.6250
(6, 1)	0.8125	0.6250	0.6250	0.6250	0.3125	0.3125

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	0.785714	0.769231	0.846154	1.225806	1.636364	1.272727
(3, 2)	0.735294	0.775000	0.720930	0.833333	0.769231	1.121951
(4, 3)	1.000000	1.000000	0.888889	2.000000	1.750000	2.000000
(5, 2)	0.653846	1.000000	0.921053	1.379310	0.551724	0.551724
(6, 3)	0.558824	0.475000	0.533333	1.189189	0.702703	1.210526

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	1.000000	0.294118	0.272727	1.823529	1.437500	1.657143
(3, 2)	0.828571	0.314286	0.083333	1.027027	0.950000	0.578947
(4, 3)	1.000000	0.142857	0.500000	1.769231	2.000000	1.727273
(5, 2)	1.000000	0.142857	0.225806	0.615385	1.454545	0.800000
(6, 3)	0.657143	0.083333	0.000000	0.800000	0.894737	1.027027

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	0.727273	1.000000	0.853659	1.833333	1.272727	1.470588
(3, 1)	0.709677	0.560976	1.000000	0.484211	0.875214	0.608553
(4, 1)	1.000000	1.000000	1.000000	2.000000	2.000000	1.714286
(5, 3)	0.909091	0.918919	0.925000	1.500000	1.166667	1.675676
(6, 2)	0.314286	0.700000	0.533333	1.538462	1.052632	0.857143

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	0.823529	0.129032	0.090909	1.272727	1.272727	1.272727
(3, 1)	0.909091	0.289474	0.062500	1.140244	0.601351	0.880000
(4, 1)	1.000000	0.192308	0.160000	1.538462	1.250000	1.714286
(5, 3)	0.709677	0.200000	0.032258	1.117647	0.545455	0.687500
(6, 2)	0.605263	0.117647	-0.105263	0.800000	0.666667	0.500000

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 2)	0.742857	1.000000	0.925000	1.437500	1.647059	1.828571
(3, 3)	0.454545	0.625000	0.700000	1.027027	0.833333	1.189189
(4, 2)	1.000000	1.000000	1.000000	1.769231	1.760000	2.000000
(5, 1)	1.000000	0.916667	0.911765	1.823529	0.645161	0.500000
(6, 1)	0.769231	0.571429	0.571429	1.162791	0.390244	0.428571

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 2)	0.647059	0.250000	0.272727	1.636364	1.454545	1.250000
(3, 3)	0.921053	0.351351	0.333333	1.210526	1.384615	1.000000
(4, 2)	1.000000	0.666667	0.045455	1.520000	2.000000	1.760000
(5, 1)	1.000000	0.172414	0.379310	1.000000	0.875000	0.800000
(6, 1)	0.294118	0.210526	0.108108	0.054054	0.744186	0.604651

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	1.360778	1.774293	2.037496	1.048585	1.639126	1.163266
(3, 2)	1.435669	1.854568	1.932651	0.831800	0.887147	1.373112
(4, 3)	2.126879	2.020043	1.691431	2.020043	1.534121	1.810487
(5, 2)	0.991780	2.537222	2.346338	1.151294	0.421458	0.421458
(6, 3)	1.037299	1.124732	1.691431	1.230370	0.741594	1.300994

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 1)	2.181371	0.548522	0.488776	2.082643	1.317874	1.760644
(3, 2)	1.760644	0.610295	0.185713	1.060233	1.124732	0.654907
(4, 3)	2.126879	0.161329	0.674490	1.638754	1.914977	1.429487
(5, 2)	2.126879	0.210428	0.374095	0.423273	1.375923	0.637279
(6, 3)	1.284784	0.185713	0.000000	0.767605	0.969528	1.060233

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	1.375923	2.830153	2.204822	2.208610	1.163266	1.435669
(3, 1)	1.334325	1.373112	2.704353	0.558871	1.036977	0.715110
(4, 1)	2.181371	2.351508	2.293459	2.073160	2.020043	1.376810
(5, 3)	2.022897	2.275715	2.501542	1.561636	1.163266	1.891943
(6, 2)	0.610295	1.641911	1.691431	1.774293	1.130857	1.150349

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 3)	1.698872	0.216785	0.170137	1.163266	1.163266	1.163266
(3, 1)	2.124012	0.654907	0.096036	1.456164	0.699944	1.051073
(4, 1)	2.181371	0.261944	0.209556	1.254983	0.887147	1.376810
(5, 3)	1.261242	0.318639	0.055456	1.037299	0.488776	0.588038
(6, 2)	1.300994	0.229883	-0.338132	0.767605	0.674490	0.648782

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 2)	1.497441	2.830153	2.501542	1.317874	1.698872	2.144415
(3, 3)	0.807416	1.456198	1.641911	1.060233	0.831800	1.230370
(4, 2)	2.236827	2.181371	2.126879	1.638754	1.586366	1.967365
(5, 1)	2.181371	2.208610	2.082643	2.082643	0.531406	0.318639
(6, 1)	1.774293	1.468989	1.468989	1.576801	0.565696	0.661573

	t1d0	t1d1	t1d2	t2d0	t2d1	t2d2
(2, 2)	1.223012	0.430727	0.488776	1.639126	1.375923	1.105217
(3, 3)	2.346338	0.741594	0.674490	1.300994	1.561636	0.993129
(4, 2)	2.236827	1.044457	0.052677	1.202595	1.967365	1.586366
(5, 1)	2.181371	0.264147	0.582786	0.807416	0.749367	0.541022
(6, 1)	0.548522	0.493578	0.252818	0.067104	1.100851	0.939522