[074d3d]: / doc / sphinxext / prs / 10071.json

Download this file

139 lines (139 with data), 2.9 kB

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
{
"merge_commit_sha": "3aad5b1aa7f74ea0f8d927664d8b844afbc40cea",
"authors": [
{
"n": "Eric Larson",
"e": "larson.eric.d@gmail.com"
}
],
"changes": {
".circleci/config.yml": {
"a": 0,
"d": 1
},
".github/workflows/circle_artifacts.yml": {
"a": 1,
"d": 1
},
"examples/decoding/decoding_csp_timefreq.py": {
"a": 4,
"d": 4
},
"examples/decoding/decoding_spoc_CMC.py": {
"a": 7,
"d": 7
},
"examples/inverse/covariance_whitening_dspm.py": {
"a": 0,
"d": 188
},
"examples/inverse/mixed_norm_inverse.py": {
"a": 5,
"d": 1
},
"examples/inverse/mixed_source_space_inverse.py": {
"a": 2,
"d": 2
},
"examples/inverse/mne_cov_power.py": {
"a": 10,
"d": 12
},
"examples/inverse/vector_mne_solution.py": {
"a": 7,
"d": 4
},
"examples/preprocessing/ica_comparison.py": {
"a": 2,
"d": 3
},
"examples/time_frequency/source_power_spectrum_opm.py": {
"a": 8,
"d": 37
},
"mne/inverse_sparse/mxne_inverse.py": {
"a": 2,
"d": 1
},
"mne/preprocessing/ieeg/_projection.py": {
"a": 6,
"d": 8
},
"mne/transforms.py": {
"a": 2,
"d": 0
},
"tutorials/clinical/10_ieeg_localize.py": {
"a": 6,
"d": 6
},
"tutorials/clinical/20_seeg.py": {
"a": 1,
"d": 1
},
"tutorials/clinical/30_ecog.py": {
"a": 6,
"d": 9
},
"tutorials/forward/30_forward.py": {
"a": 16,
"d": 12
},
"tutorials/forward/90_compute_covariance.py": {
"a": 5,
"d": 5
},
"tutorials/intro/70_report.py": {
"a": 30,
"d": 25
},
"tutorials/inverse/40_mne_fixed_free.py": {
"a": 4,
"d": 3
},
"tutorials/inverse/50_beamformer_lcmv.py": {
"a": 7,
"d": 5
},
"tutorials/inverse/60_visualize_stc.py": {
"a": 7,
"d": 6
},
"tutorials/machine-learning/50_decoding.py": {
"a": 13,
"d": 9
},
"tutorials/preprocessing/30_filtering_resampling.py": {
"a": 6,
"d": 6
},
"tutorials/preprocessing/40_artifact_correction_ica.py": {
"a": 10,
"d": 8
},
"tutorials/preprocessing/50_artifact_correction_ssp.py": {
"a": 13,
"d": 13
},
"tutorials/stats-sensor-space/20_erp_stats.py": {
"a": 4,
"d": 1
},
"tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.py": {
"a": 1,
"d": 1
},
"tutorials/stats-source-space/30_cluster_ftest_spatiotemporal.py": {
"a": 9,
"d": 6
},
"tutorials/stats-source-space/60_cluster_rmANOVA_spatiotemporal.py": {
"a": 9,
"d": 9
},
"tutorials/time-freq/20_sensors_time_frequency.py": {
"a": 3,
"d": 3
}
}
}