diff --git a/actions/comparisons-gridcells/data/20_comparisons_gridcells.html b/actions/comparisons-gridcells/data/20_comparisons_gridcells.html index fcfcbce38..1b86dac7d 100644 --- a/actions/comparisons-gridcells/data/20_comparisons_gridcells.html +++ b/actions/comparisons-gridcells/data/20_comparisons_gridcells.html @@ -13168,7 +13168,7 @@ div#notebook {
@@ -13729,7 +13729,7 @@ div#notebook { -5 rows × 41 columns
+5 rows × 46 columns
@@ -13748,7 +13748,7 @@ div#notebook {data.groupby('stimulated').count()['action']
@@ -13764,7 +13764,7 @@ div#notebook {
- Out[12]:
+ Out[7]:
@@ -13772,7 +13772,7 @@ div#notebook {
@@ -13781,6 +13781,19 @@ Name: action, dtype: int64
data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)
+
query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'
@@ -13814,7 +13827,7 @@ Name: action, dtype: int64
@@ -13826,10 +13839,10 @@ Number of animals 4
baseline = sessions_above_threshold.query('baseline')
+gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]
gridcell_in_baseline = data[data.unit_id.isin(baseline.unit_id)]
-
baseline_i = gridcell_sessions.query('baseline and Hz11')
+stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location=="ms"')
-
baseline_i = gridcell_in_baseline.query('baseline and i')
-stimulated_11 = gridcell_in_baseline.query('frequency==11 and stim_location=="ms" and i')
-
-baseline_ii = gridcell_in_baseline.query('baseline and ii')
-stimulated_30 = gridcell_in_baseline.query('frequency==30 and stim_location=="ms" and ii')
+baseline_ii = gridcell_sessions.query('baseline and Hz30')
+stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location=="ms"')
print("Number of gridcells in baseline i sessions", len(baseline_i))
print("Number of gridcells in stimulated 11Hz ms sessions", len(stimulated_11))
@@ -13882,10 +13882,10 @@ Number of animals 4
baseline_i = baseline_i.drop_duplicates('unit_id')
@@ -13919,7 +13919,7 @@ Number of gridcells in stimulated 30Hz ms sessions 33
print("Number of gridcells in baseline i sessions", len(baseline_i))
@@ -13943,10 +13943,10 @@ Number of gridcells in stimulated 30Hz ms sessions 33
columns = [
@@ -13982,10 +13982,10 @@ Number of gridcells in stimulated 30Hz ms sessions 28
gridcell_in_baseline.groupby('stimulated')[columns].mean()
+gridcell_sessions.groupby('stimulated')[columns].mean()
gridcell_in_baseline.query('baseline')[columns].describe()
+gridcell_sessions.query('baseline')[columns].describe()
gridcell_in_baseline.query("stimulated")[columns].describe()
+gridcell_sessions.query("stimulated")[columns].describe()
def summarize(data):
@@ -14538,16 +14538,17 @@ Number of gridcells in stimulated 30Hz ms sessions 28
_stim_data = gridcell_in_baseline.query('stimulated')
-_base_data = gridcell_in_baseline.query('baseline')
+_stim_data = gridcell_sessions.query('stimulated')
+_base_data = gridcell_sessions.query('baseline')
result = pd.DataFrame()
-result['Stimulated'] = _stim_data[columns].agg(summarize)
result['Baseline'] = _base_data[columns].agg(summarize)
+result['Stimulated'] = _stim_data[columns].agg(summarize)
+
result.index = map(rename, result.index)
@@ -14569,7 +14570,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Out[26]:
+ Out[49]:
@@ -14592,8 +14593,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Stimulated
Baseline
+ Stimulated
MWU
PRS
@@ -14601,94 +14602,94 @@ Number of gridcells in stimulated 30Hz ms sessions 28
Average rate
- 9.55 ± 0.86 (73)
- 9.93 ± 0.64 (144)
- 5120.00, 0.757
- 0.14, 0.868
+ 10.05 ± 0.65 (147)
+ 9.81 ± 0.69 (124)
+ 9040.00, 0.909
+ 0.56, 0.717
Gridness
- 0.37 ± 0.05 (73)
- 0.56 ± 0.03 (144)
- 3718.00, 0.000
- 0.30, 0.000
+ 0.54 ± 0.03 (147)
+ 0.43 ± 0.03 (124)
+ 7516.00, 0.013
+ 0.17, 0.004
Sparsity
- 0.67 ± 0.02 (73)
- 0.66 ± 0.02 (144)
- 5515.00, 0.554
- 0.03, 0.413
+ 0.66 ± 0.02 (147)
+ 0.69 ± 0.02 (124)
+ 10275.00, 0.071
+ 0.04, 0.161
Selectivity
- 6.23 ± 0.67 (73)
- 5.32 ± 0.24 (144)
- 5482.00, 0.606
- 0.21, 0.718
+ 5.35 ± 0.24 (147)
+ 5.28 ± 0.32 (124)
+ 8488.00, 0.330
+ 0.23, 0.450
Information specificity
- 0.19 ± 0.02 (73)
- 0.20 ± 0.01 (144)
- 5094.00, 0.712
- 0.03, 0.501
+ 0.21 ± 0.02 (147)
+ 0.18 ± 0.02 (124)
+ 7883.00, 0.056
+ 0.03, 0.103
Max rate
- 39.86 ± 2.97 (73)
- 37.44 ± 1.38 (144)
- 5256.00, 0.999
- 0.97, 0.592
+ 37.74 ± 1.40 (147)
+ 34.65 ± 1.30 (124)
+ 8165.00, 0.140
+ 2.31, 0.108
Information rate
- 1.06 ± 0.06 (73)
- 1.18 ± 0.05 (144)
- 4681.00, 0.189
- 0.07, 0.426
+ 1.18 ± 0.05 (147)
+ 0.93 ± 0.04 (124)
+ 6772.00, 0.000
+ 0.18, 0.008
Interspike interval cv
- 2.33 ± 0.09 (73)
- 2.35 ± 0.06 (144)
- 5197.00, 0.894
- 0.04, 0.715
+ 2.34 ± 0.06 (147)
+ 2.25 ± 0.07 (124)
+ 8361.00, 0.242
+ 0.07, 0.500
In-field mean rate
- 14.58 ± 1.00 (73)
- 15.71 ± 0.82 (144)
- 5000.00, 0.559
- 0.55, 0.751
+ 15.79 ± 0.82 (147)
+ 14.46 ± 0.79 (124)
+ 8526.00, 0.361
+ 0.67, 0.638
Out-field mean rate
- 7.12 ± 0.76 (73)
- 7.32 ± 0.56 (144)
- 5166.00, 0.838
- 0.10, 0.934
+ 7.41 ± 0.58 (147)
+ 7.43 ± 0.62 (124)
+ 9193.00, 0.903
+ 0.88, 0.456
Burst event ratio
- 0.21 ± 0.01 (73)
- 0.22 ± 0.01 (144)
- 4677.00, 0.186
- 0.01, 0.212
+ 0.22 ± 0.01 (147)
+ 0.21 ± 0.01 (124)
+ 8548.00, 0.379
+ 0.01, 0.370
Specificity
- 0.45 ± 0.03 (73)
- 0.44 ± 0.02 (144)
- 5076.00, 0.681
- 0.02, 0.547
+ 0.45 ± 0.02 (147)
+ 0.42 ± 0.02 (124)
+ 8221.00, 0.165
+ 0.03, 0.167
Speed score
- 0.10 ± 0.01 (73)
- 0.14 ± 0.01 (144)
- 3978.00, 0.003
- 0.04, 0.008
+ 0.13 ± 0.01 (147)
+ 0.11 ± 0.01 (124)
+ 7793.00, 0.040
+ 0.02, 0.046
@@ -14703,7 +14704,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [27]:
+In [47]:
_stim_data = stimulated_11
@@ -14711,8 +14712,9 @@ Number of gridcells in stimulated 30Hz ms sessions 28
result = pd.DataFrame()
-result['Stimulated'] = _stim_data[columns].agg(summarize)
result['Baseline'] = _base_data[columns].agg(summarize)
+result['11 Hz'] = _stim_data[columns].agg(summarize)
+
result.index = map(rename, result.index)
@@ -14735,7 +14737,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Out[27]:
+ Out[47]:
@@ -14758,8 +14760,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Stimulated
Baseline
+ 11 Hz
MWU
PRS
@@ -14767,94 +14769,94 @@ Number of gridcells in stimulated 30Hz ms sessions 28
Average rate
- 9.06 ± 1.21 (32)
- 9.65 ± 0.90 (68)
- 1044.00, 0.748
- 0.02, 0.997
+ 9.82 ± 0.91 (70)
+ 9.28 ± 0.90 (65)
+ 2175.00, 0.661
+ 0.18, 0.933
Gridness
- 0.34 ± 0.06 (32)
- 0.58 ± 0.04 (68)
- 676.00, 0.002
- 0.27, 0.003
+ 0.54 ± 0.05 (70)
+ 0.42 ± 0.05 (65)
+ 1822.00, 0.046
+ 0.17, 0.052
Sparsity
- 0.67 ± 0.03 (32)
- 0.65 ± 0.02 (68)
- 1154.00, 0.628
- 0.06, 0.319
+ 0.65 ± 0.02 (70)
+ 0.69 ± 0.02 (65)
+ 2578.00, 0.183
+ 0.06, 0.147
Selectivity
- 5.43 ± 0.47 (32)
- 5.22 ± 0.35 (68)
- 1140.00, 0.704
- 0.29, 0.705
+ 5.25 ± 0.35 (70)
+ 5.43 ± 0.48 (65)
+ 2214.00, 0.790
+ 0.05, 0.961
Information specificity
- 0.19 ± 0.03 (32)
- 0.21 ± 0.02 (68)
- 1005.00, 0.542
- 0.05, 0.095
+ 0.22 ± 0.03 (70)
+ 0.19 ± 0.03 (65)
+ 1888.00, 0.089
+ 0.05, 0.020
Max rate
- 35.53 ± 2.50 (32)
- 36.19 ± 1.79 (68)
- 1063.00, 0.856
- 0.04, 0.972
+ 36.77 ± 1.96 (70)
+ 33.16 ± 1.79 (65)
+ 1971.00, 0.181
+ 3.18, 0.250
Information rate
- 1.04 ± 0.10 (32)
- 1.21 ± 0.06 (68)
- 867.00, 0.103
- 0.12, 0.225
+ 1.22 ± 0.06 (70)
+ 0.89 ± 0.06 (65)
+ 1431.00, 0.000
+ 0.20, 0.006
Interspike interval cv
- 2.29 ± 0.12 (32)
- 2.38 ± 0.10 (68)
- 1053.00, 0.799
- 0.04, 0.891
+ 2.37 ± 0.09 (70)
+ 2.24 ± 0.09 (65)
+ 2022.00, 0.266
+ 0.12, 0.520
In-field mean rate
- 13.87 ± 1.42 (32)
- 15.27 ± 1.12 (68)
- 1024.00, 0.639
- 0.10, 0.948
+ 15.52 ± 1.15 (70)
+ 13.80 ± 1.06 (65)
+ 2064.00, 0.354
+ 0.63, 0.738
Out-field mean rate
- 6.52 ± 1.04 (32)
- 6.98 ± 0.76 (68)
- 1037.00, 0.709
- 0.35, 0.905
+ 7.09 ± 0.77 (70)
+ 7.00 ± 0.80 (65)
+ 2236.00, 0.865
+ 0.01, 0.979
Burst event ratio
- 0.23 ± 0.01 (32)
- 0.23 ± 0.01 (68)
- 1158.00, 0.608
- 0.01, 0.478
+ 0.23 ± 0.01 (70)
+ 0.23 ± 0.01 (65)
+ 2307.00, 0.890
+ 0.01, 0.732
Specificity
- 0.45 ± 0.04 (32)
- 0.45 ± 0.02 (68)
- 1060.00, 0.839
- 0.01, 0.852
+ 0.45 ± 0.03 (70)
+ 0.42 ± 0.03 (65)
+ 2049.00, 0.321
+ 0.01, 0.476
Speed score
- 0.09 ± 0.01 (32)
- 0.14 ± 0.01 (68)
- 736.00, 0.009
- 0.05, 0.011
+ 0.14 ± 0.01 (70)
+ 0.12 ± 0.01 (65)
+ 1939.00, 0.140
+ 0.03, 0.069
@@ -14869,7 +14871,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [28]:
+In [48]:
_stim_data = stimulated_30
@@ -14877,8 +14879,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
result = pd.DataFrame()
-result['Stimulated'] = _stim_data[columns].agg(summarize)
result['Baseline'] = _base_data[columns].agg(summarize)
+result['30 Hz'] = _stim_data[columns].agg(summarize)
result.index = map(rename, result.index)
@@ -14901,7 +14903,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Out[28]:
+ Out[48]:
@@ -14924,8 +14926,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Stimulated
Baseline
+ 30 Hz
MWU
PRS
@@ -14933,94 +14935,94 @@ Number of gridcells in stimulated 30Hz ms sessions 28
Average rate
- 10.11 ± 1.51 (28)
- 10.01 ± 1.06 (58)
- 808.00, 0.974
- 0.07, 0.968
+ 10.08 ± 1.05 (61)
+ 9.94 ± 1.17 (49)
+ 1491.00, 0.986
+ 0.24, 0.763
Gridness
- 0.28 ± 0.08 (28)
- 0.57 ± 0.05 (58)
- 493.00, 0.003
- 0.46, 0.000
+ 0.53 ± 0.05 (61)
+ 0.46 ± 0.06 (49)
+ 1342.00, 0.361
+ 0.08, 0.289
Sparsity
- 0.68 ± 0.04 (28)
- 0.66 ± 0.02 (58)
- 881.00, 0.528
- 0.04, 0.328
+ 0.67 ± 0.02 (61)
+ 0.69 ± 0.03 (49)
+ 1622.00, 0.445
+ 0.03, 0.466
Selectivity
- 7.47 ± 1.63 (28)
- 5.53 ± 0.40 (58)
- 809.00, 0.982
- 0.30, 0.638
+ 5.34 ± 0.38 (61)
+ 5.21 ± 0.46 (49)
+ 1372.00, 0.463
+ 0.37, 0.420
Information specificity
- 0.20 ± 0.03 (28)
- 0.19 ± 0.02 (58)
- 812.00, 0.996
- 0.01, 0.588
+ 0.19 ± 0.02 (61)
+ 0.18 ± 0.03 (49)
+ 1380.00, 0.493
+ 0.01, 0.725
Max rate
- 45.33 ± 6.85 (28)
- 38.95 ± 2.48 (58)
- 797.00, 0.894
- 2.09, 0.451
+ 37.61 ± 2.31 (61)
+ 34.42 ± 1.99 (49)
+ 1342.00, 0.361
+ 2.37, 0.351
Information rate
- 1.04 ± 0.08 (28)
- 1.12 ± 0.09 (58)
- 799.00, 0.908
- 0.03, 0.858
+ 1.08 ± 0.08 (61)
+ 0.95 ± 0.07 (49)
+ 1321.00, 0.298
+ 0.14, 0.413
Interspike interval cv
- 2.28 ± 0.16 (28)
- 2.32 ± 0.09 (58)
- 745.00, 0.540
- 0.16, 0.463
+ 2.28 ± 0.09 (61)
+ 2.24 ± 0.11 (49)
+ 1419.00, 0.652
+ 0.06, 0.740
In-field mean rate
- 14.95 ± 1.71 (28)
- 15.81 ± 1.38 (58)
- 779.00, 0.765
- 0.98, 0.712
+ 15.61 ± 1.32 (61)
+ 14.54 ± 1.29 (49)
+ 1418.00, 0.648
+ 0.64, 0.675
Out-field mean rate
- 7.80 ± 1.35 (28)
- 7.58 ± 0.96 (58)
- 827.00, 0.894
- 0.10, 0.927
+ 7.65 ± 0.96 (61)
+ 7.54 ± 1.06 (49)
+ 1487.00, 0.966
+ 0.37, 0.789
Burst event ratio
- 0.18 ± 0.01 (28)
- 0.21 ± 0.01 (58)
- 641.00, 0.116
- 0.03, 0.099
+ 0.21 ± 0.01 (61)
+ 0.19 ± 0.01 (49)
+ 1241.00, 0.128
+ 0.04, 0.037
Specificity
- 0.43 ± 0.05 (28)
- 0.43 ± 0.03 (58)
- 749.00, 0.565
- 0.02, 0.657
+ 0.42 ± 0.03 (61)
+ 0.42 ± 0.03 (49)
+ 1429.00, 0.696
+ 0.03, 0.495
Speed score
- 0.10 ± 0.02 (28)
- 0.12 ± 0.01 (58)
- 617.00, 0.073
- 0.02, 0.116
+ 0.12 ± 0.01 (61)
+ 0.11 ± 0.01 (49)
+ 1335.00, 0.339
+ 0.01, 0.545
@@ -15035,7 +15037,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [29]:
+In [45]:
_stim_data = stimulated_30
@@ -15043,8 +15045,9 @@ Number of gridcells in stimulated 30Hz ms sessions 28
result = pd.DataFrame()
-result['Stimulated 30Hz'] = _stim_data[columns].agg(summarize)
-result['Stimulated 11Hz'] = _base_data[columns].agg(summarize)
+result['11 Hz'] = _base_data[columns].agg(summarize)
+result['30 Hz'] = _stim_data[columns].agg(summarize)
+
result.index = map(rename, result.index)
@@ -15067,7 +15070,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Out[29]:
+ Out[45]:
@@ -15090,8 +15093,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Stimulated 30Hz
- Stimulated 11Hz
+ 11 Hz
+ 30 Hz
MWU
PRS
@@ -15099,94 +15102,94 @@ Number of gridcells in stimulated 30Hz ms sessions 28
Average rate
- 10.11 ± 1.51 (28)
- 9.06 ± 1.21 (32)
- 463.00, 0.830
- 0.12, 0.978
+ 9.28 ± 0.90 (65)
+ 9.94 ± 1.17 (49)
+ 1641.00, 0.784
+ 0.09, 0.925
Gridness
- 0.28 ± 0.08 (28)
- 0.34 ± 0.06 (32)
- 402.00, 0.500
- 0.15, 0.330
+ 0.42 ± 0.05 (65)
+ 0.46 ± 0.06 (49)
+ 1739.00, 0.403
+ 0.09, 0.420
Sparsity
- 0.68 ± 0.04 (28)
- 0.67 ± 0.03 (32)
- 479.00, 0.651
- 0.03, 0.493
+ 0.69 ± 0.02 (65)
+ 0.69 ± 0.03 (49)
+ 1618.00, 0.886
+ 0.01, 0.660
Selectivity
- 7.47 ± 1.63 (28)
- 5.43 ± 0.47 (32)
- 449.00, 0.994
- 0.00, 0.999
+ 5.43 ± 0.48 (65)
+ 5.21 ± 0.46 (49)
+ 1548.00, 0.801
+ 0.17, 0.835
Information specificity
- 0.20 ± 0.03 (28)
- 0.19 ± 0.03 (32)
- 440.00, 0.912
- 0.01, 0.768
+ 0.19 ± 0.03 (65)
+ 0.18 ± 0.03 (49)
+ 1569.00, 0.895
+ 0.01, 0.783
Max rate
- 45.33 ± 6.85 (28)
- 35.53 ± 2.50 (32)
- 488.00, 0.558
- 1.22, 0.682
+ 33.16 ± 1.79 (65)
+ 34.42 ± 1.99 (49)
+ 1681.00, 0.614
+ 1.38, 0.740
Information rate
- 1.04 ± 0.08 (28)
- 1.04 ± 0.10 (32)
- 475.00, 0.695
- 0.02, 0.775
+ 0.89 ± 0.06 (65)
+ 0.95 ± 0.07 (49)
+ 1701.00, 0.536
+ 0.07, 0.480
Interspike interval cv
- 2.28 ± 0.16 (28)
- 2.29 ± 0.12 (32)
- 411.00, 0.589
- 0.14, 0.659
+ 2.24 ± 0.09 (65)
+ 2.24 ± 0.11 (49)
+ 1583.00, 0.959
+ 0.05, 0.814
In-field mean rate
- 14.95 ± 1.71 (28)
- 13.87 ± 1.42 (32)
- 473.00, 0.717
- 1.02, 0.794
+ 13.80 ± 1.06 (65)
+ 14.54 ± 1.29 (49)
+ 1658.00, 0.710
+ 0.88, 0.678
Out-field mean rate
- 7.80 ± 1.35 (28)
- 6.52 ± 1.04 (32)
- 489.00, 0.548
- 0.17, 0.940
+ 7.00 ± 0.80 (65)
+ 7.54 ± 1.06 (49)
+ 1631.00, 0.828
+ 0.38, 0.923
Burst event ratio
- 0.18 ± 0.01 (28)
- 0.23 ± 0.01 (32)
- 273.00, 0.010
- 0.05, 0.028
+ 0.23 ± 0.01 (65)
+ 0.19 ± 0.01 (49)
+ 1093.00, 0.004
+ 0.05, 0.004
Specificity
- 0.43 ± 0.05 (28)
- 0.45 ± 0.04 (32)
- 400.00, 0.482
- 0.02, 0.570
+ 0.42 ± 0.03 (65)
+ 0.42 ± 0.03 (49)
+ 1559.00, 0.850
+ 0.01, 0.597
Speed score
- 0.10 ± 0.02 (28)
- 0.09 ± 0.01 (32)
- 446.00, 0.982
- 0.01, 0.480
+ 0.12 ± 0.01 (65)
+ 0.11 ± 0.01 (49)
+ 1459.00, 0.446
+ 0.01, 0.397
@@ -15201,7 +15204,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [30]:
+In [46]:
_stim_data = baseline_i
@@ -15209,8 +15212,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
result = pd.DataFrame()
-result['Baseline i'] = _stim_data[columns].agg(summarize)
-result['Baseline ii'] = _base_data[columns].agg(summarize)
+result['Baseline I'] = _stim_data[columns].agg(summarize)
+result['Baseline II'] = _base_data[columns].agg(summarize)
result.index = map(rename, result.index)
@@ -15233,7 +15236,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Out[30]:
+ Out[46]:
@@ -15256,8 +15259,8 @@ Number of gridcells in stimulated 30Hz ms sessions 28
- Baseline i
- Baseline ii
+ Baseline I
+ Baseline II
MWU
PRS
@@ -15265,94 +15268,94 @@ Number of gridcells in stimulated 30Hz ms sessions 28
Average rate
- 9.65 ± 0.90 (68)
- 10.01 ± 1.06 (58)
- 1979.00, 0.975
- 0.20, 0.935
+ 9.82 ± 0.91 (70)
+ 10.08 ± 1.05 (61)
+ 2166.00, 0.888
+ 0.15, 0.852
Gridness
- 0.58 ± 0.04 (68)
- 0.57 ± 0.05 (58)
- 1946.00, 0.901
- 0.04, 0.479
+ 0.54 ± 0.05 (70)
+ 0.53 ± 0.05 (61)
+ 2158.00, 0.917
+ 0.00, 0.983
Sparsity
- 0.65 ± 0.02 (68)
- 0.66 ± 0.02 (58)
- 1870.00, 0.619
- 0.05, 0.253
+ 0.65 ± 0.02 (70)
+ 0.67 ± 0.02 (61)
+ 2001.00, 0.538
+ 0.04, 0.361
Selectivity
- 5.22 ± 0.35 (68)
- 5.53 ± 0.40 (58)
- 1833.00, 0.498
- 0.01, 0.973
+ 5.25 ± 0.35 (70)
+ 5.34 ± 0.38 (61)
+ 2062.00, 0.738
+ 0.25, 0.594
Information specificity
- 0.21 ± 0.02 (68)
- 0.19 ± 0.02 (58)
- 2135.00, 0.426
- 0.05, 0.136
+ 0.22 ± 0.03 (70)
+ 0.19 ± 0.02 (61)
+ 2329.00, 0.372
+ 0.05, 0.143
Max rate
- 36.19 ± 1.79 (68)
- 38.95 ± 2.48 (58)
- 1824.00, 0.470
- 0.84, 0.675
+ 36.77 ± 1.96 (70)
+ 37.61 ± 2.31 (61)
+ 2088.00, 0.830
+ 0.58, 0.784
Information rate
- 1.21 ± 0.06 (68)
- 1.12 ± 0.09 (58)
- 2246.00, 0.181
- 0.13, 0.169
+ 1.22 ± 0.06 (70)
+ 1.08 ± 0.08 (61)
+ 2501.00, 0.092
+ 0.14, 0.151
Interspike interval cv
- 2.38 ± 0.10 (68)
- 2.32 ± 0.09 (58)
- 2055.00, 0.686
- 0.02, 0.805
+ 2.37 ± 0.09 (70)
+ 2.28 ± 0.09 (61)
+ 2257.00, 0.575
+ 0.01, 0.928
In-field mean rate
- 15.27 ± 1.12 (68)
- 15.81 ± 1.38 (58)
- 1926.00, 0.824
- 0.15, 0.931
+ 15.52 ± 1.15 (70)
+ 15.61 ± 1.32 (61)
+ 2162.00, 0.903
+ 0.87, 0.724
Out-field mean rate
- 6.98 ± 0.76 (68)
- 7.58 ± 0.96 (58)
- 1946.00, 0.901
- 0.62, 0.650
+ 7.09 ± 0.77 (70)
+ 7.65 ± 0.96 (61)
+ 2115.00, 0.928
+ 0.02, 0.986
Burst event ratio
- 0.23 ± 0.01 (68)
- 0.21 ± 0.01 (58)
- 2112.00, 0.495
- 0.00, 0.743
+ 0.23 ± 0.01 (70)
+ 0.21 ± 0.01 (61)
+ 2299.00, 0.451
+ 0.00, 0.830
Specificity
- 0.45 ± 0.02 (68)
- 0.43 ± 0.03 (58)
- 2035.00, 0.760
- 0.01, 0.834
+ 0.45 ± 0.03 (70)
+ 0.42 ± 0.03 (61)
+ 2245.00, 0.613
+ 0.01, 0.921
Speed score
- 0.14 ± 0.01 (68)
- 0.12 ± 0.01 (58)
- 2267.00, 0.149
- 0.05, 0.014
+ 0.14 ± 0.01 (70)
+ 0.12 ± 0.01 (61)
+ 2423.00, 0.185
+ 0.04, 0.042
@@ -15374,7 +15377,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [47]:
+In [25]:
%matplotlib inline
@@ -15393,13 +15396,13 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [48]:
+In [51]:
stuff = {
'': {
- 'base': gridcell_in_baseline.query('baseline'),
- 'stim': gridcell_in_baseline.query('stimulated')
+ 'base': gridcell_sessions.query('baseline'),
+ 'stim': gridcell_sessions.query('stimulated')
},
'_11': {
'base': baseline_i,
@@ -15410,6 +15413,12 @@ Number of gridcells in stimulated 30Hz ms sessions 28
'stim': stimulated_30
}
}
+
+label = {
+ '': ['Baseline ', ' Stimulated'],
+ '_11': ['Baseline I ', ' 11 Hz'],
+ '_30': ['Baseline II ', ' 30 Hz']
+}
@@ -15426,7 +15435,99 @@ Number of gridcells in stimulated 30Hz ms sessions 28
-In [49]:
+In [67]:
+
+
+for key, data in stuff.items():
+ baseline = data['base']['information_specificity'].to_numpy()
+ stimulated = data['stim']['information_specificity'].to_numpy()
+ print(key)
+ plt.figure()
+ violinplot(baseline, stimulated, xticks=label[key])
+ plt.title("Spatial information specificity")
+ plt.ylabel("bits/spike")
+ plt.ylim(-0.2, 1.6)
+
+ plt.savefig(output_path / "figures" / f"information_specificity{key}.svg", bbox_inches="tight")
+ plt.savefig(output_path / "figures" / f"information_specificity{key}.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [52]:
for key, data in stuff.items():
@@ -15434,7 +15535,7 @@ Number of gridcells in stimulated 30Hz ms sessions 28
stimulated = data['stim']['information_rate'].to_numpy()
print(key)
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Spatial information")
plt.ylabel("bits/s")
plt.ylim(-0.2, 4)
@@ -15458,11 +15559,11 @@ Number of gridcells in stimulated 30Hz ms sessions 28
@@ -15475,7 +15576,7 @@ U-test: U value 825.0 p value 0.9082875409541091
@@ -15490,7 +15591,7 @@ U-test: U value 825.0 p value 0.9082875409541091
@@ -15505,7 +15606,7 @@ U-test: U value 825.0 p value 0.9082875409541091
@@ -15518,14 +15619,14 @@ U-test: U value 825.0 p value 0.9082875409541091
-In [50]:
+In [53]:
for key, data in stuff.items():
baseline = data['base']['specificity'].to_numpy()
stimulated = data['stim']['specificity'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Spatial specificity")
plt.ylabel("")
plt.ylim(-0.02, 1.25)
@@ -15547,9 +15648,9 @@ U-test: U value 825.0 p value 0.9082875409541091
@@ -15562,7 +15663,7 @@ U-test: U value 875.0 p value 0.5646191993775383
@@ -15577,7 +15678,7 @@ U-test: U value 875.0 p value 0.5646191993775383
@@ -15592,7 +15693,7 @@ U-test: U value 875.0 p value 0.5646191993775383
@@ -15605,14 +15706,14 @@ U-test: U value 875.0 p value 0.5646191993775383
-In [51]:
+In [54]:
for key, data in stuff.items():
baseline = data['base']['average_rate'].to_numpy()
stimulated = data['stim']['average_rate'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Average rate")
plt.ylabel("spikes/s")
plt.ylim(-0.2, 40)
@@ -15635,9 +15736,9 @@ U-test: U value 875.0 p value 0.5646191993775383
@@ -15650,7 +15751,7 @@ U-test: U value 816.0 p value 0.9742681632988652
@@ -15665,7 +15766,7 @@ U-test: U value 816.0 p value 0.9742681632988652
@@ -15680,7 +15781,7 @@ U-test: U value 816.0 p value 0.9742681632988652
@@ -15693,14 +15794,14 @@ U-test: U value 816.0 p value 0.9742681632988652
-In [52]:
+In [55]:
for key, data in stuff.items():
baseline = data['base']['max_rate'].to_numpy()
stimulated = data['stim']['max_rate'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Max rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.2, 45)
@@ -15723,9 +15824,9 @@ U-test: U value 816.0 p value 0.9742681632988652
@@ -15738,7 +15839,7 @@ U-test: U value 827.0 p value 0.8936946693232326
@@ -15753,7 +15854,7 @@ U-test: U value 827.0 p value 0.8936946693232326
@@ -15768,7 +15869,7 @@ U-test: U value 827.0 p value 0.8936946693232326
@@ -15781,14 +15882,14 @@ U-test: U value 827.0 p value 0.8936946693232326
-In [53]:
+In [56]:
for key, data in stuff.items():
baseline = data['base']['interspike_interval_cv'].to_numpy()
stimulated = data['stim']['interspike_interval_cv'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("ISI CV")
plt.ylabel("Coefficient of variation")
# plt.ylim(0.9, 5)
@@ -15811,9 +15912,9 @@ U-test: U value 827.0 p value 0.8936946693232326
@@ -15826,7 +15927,7 @@ U-test: U value 879.0 p value 0.5399704510090971
@@ -15841,7 +15942,7 @@ U-test: U value 879.0 p value 0.5399704510090971
@@ -15856,7 +15957,7 @@ U-test: U value 879.0 p value 0.5399704510090971
@@ -15869,14 +15970,14 @@ U-test: U value 879.0 p value 0.5399704510090971
-In [54]:
+In [57]:
for key, data in stuff.items():
baseline = data['base']['in_field_mean_rate'].to_numpy()
stimulated = data['stim']['in_field_mean_rate'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("In-field rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.1, 18)
@@ -15899,9 +16000,9 @@ U-test: U value 879.0 p value 0.5399704510090971
@@ -15914,7 +16015,7 @@ U-test: U value 845.0 p value 0.764545672323149
@@ -15929,7 +16030,7 @@ U-test: U value 845.0 p value 0.764545672323149
@@ -15944,7 +16045,7 @@ U-test: U value 845.0 p value 0.764545672323149
@@ -15957,14 +16058,14 @@ U-test: U value 845.0 p value 0.764545672323149
-In [55]:
+In [58]:
for key, data in stuff.items():
baseline = data['base']['out_field_mean_rate'].to_numpy()
stimulated = data['stim']['out_field_mean_rate'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Out-of-field rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.2, 8)
@@ -15987,9 +16088,9 @@ U-test: U value 845.0 p value 0.764545672323149
@@ -16002,7 +16103,7 @@ U-test: U value 797.0 p value 0.8936946693232326
@@ -16017,7 +16118,7 @@ U-test: U value 797.0 p value 0.8936946693232326
@@ -16032,7 +16133,7 @@ U-test: U value 797.0 p value 0.8936946693232326
@@ -16045,14 +16146,14 @@ U-test: U value 797.0 p value 0.8936946693232326
-In [56]:
+In [59]:
for key, data in stuff.items():
baseline = data['base']['burst_event_ratio'].to_numpy()
stimulated = data['stim']['burst_event_ratio'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Bursting ratio")
plt.ylabel("")
# plt.ylim(-0.02, 0.60)
@@ -16075,9 +16176,9 @@ U-test: U value 797.0 p value 0.8936946693232326
@@ -16090,7 +16191,7 @@ U-test: U value 983.0 p value 0.11611024526570707
@@ -16105,7 +16206,7 @@ U-test: U value 983.0 p value 0.11611024526570707
@@ -16120,7 +16221,7 @@ U-test: U value 983.0 p value 0.11611024526570707
@@ -16133,14 +16234,14 @@ U-test: U value 983.0 p value 0.11611024526570707
-In [57]:
+In [60]:
for key, data in stuff.items():
baseline = data['base']['max_field_mean_rate'].to_numpy()
stimulated = data['stim']['max_field_mean_rate'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Mean rate of max field")
plt.ylabel("(spikes/s)")
# plt.ylim(-0.5,25)
@@ -16163,9 +16264,9 @@ U-test: U value 983.0 p value 0.11611024526570707
@@ -16178,7 +16279,7 @@ U-test: U value 889.0 p value 0.4807998283550271
@@ -16193,7 +16294,7 @@ U-test: U value 889.0 p value 0.4807998283550271
@@ -16208,7 +16309,7 @@ U-test: U value 889.0 p value 0.4807998283550271
@@ -16221,14 +16322,14 @@ U-test: U value 889.0 p value 0.4807998283550271
-In [58]:
+In [61]:
for key, data in stuff.items():
baseline = data['base']['bursty_spike_ratio'].to_numpy()
stimulated = data['stim']['bursty_spike_ratio'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("ratio of spikes per burst")
plt.ylabel("")
# plt.ylim(-0.03,0.9)
@@ -16251,9 +16352,9 @@ U-test: U value 889.0 p value 0.4807998283550271
@@ -16266,7 +16367,7 @@ U-test: U value 998.0 p value 0.08734905208437223
@@ -16281,7 +16382,7 @@ U-test: U value 998.0 p value 0.08734905208437223
@@ -16296,7 +16397,7 @@ U-test: U value 998.0 p value 0.08734905208437223
@@ -16309,17 +16410,17 @@ U-test: U value 998.0 p value 0.08734905208437223
-In [59]:
+In [62]:
for key, data in stuff.items():
baseline = data['base']['gridness'].to_numpy()
stimulated = data['stim']['gridness'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Gridness")
plt.ylabel("Gridness")
- plt.ylim(-0.005, 1.5)
+ plt.ylim(-0.6, 1.5)
plt.savefig(output_path / "figures" / f"gridness{key}.svg", bbox_inches="tight")
plt.savefig(output_path / "figures" / f"gridness{key}.png", dpi=600, bbox_inches="tight")
@@ -16339,9 +16440,9 @@ U-test: U value 998.0 p value 0.08734905208437223
@@ -16354,7 +16455,7 @@ U-test: U value 1131.0 p value 0.0033326217809176804
@@ -16369,7 +16470,7 @@ U-test: U value 1131.0 p value 0.0033326217809176804
@@ -16384,7 +16485,7 @@ U-test: U value 1131.0 p value 0.0033326217809176804
@@ -16397,14 +16498,14 @@ U-test: U value 1131.0 p value 0.0033326217809176804
-In [60]:
+In [63]:
-for key, data in stuff.items():
+for key, data in stuff.items(): #TODO narrow broad spiking
baseline = data['base']['speed_score'].to_numpy()
stimulated = data['stim']['speed_score'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated)
+ violinplot(baseline, stimulated, xticks=label[key])
plt.title("Speed score")
plt.ylabel("Speed score")
# plt.ylim(-0.1, 0.5)
@@ -16427,9 +16528,9 @@ U-test: U value 1131.0 p value 0.0033326217809176804
@@ -16442,7 +16543,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
@@ -16457,7 +16558,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
@@ -16472,7 +16573,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
@@ -16482,6 +16583,31 @@ U-test: U value 1007.0 p value 0.07305394917377077
+
+
+
+In [39]:
+
+
+# fig, (ax1, ax2) = plt.subplots(2,1, figsize=(6,6), sharey=True)
+# for key, data in stuff.items():
+# ax1.set_title('Baseline')
+# peak_rate = data['base']['max_rate'].to_numpy()
+# spacing = data['base']['spacing'].to_numpy()
+# ax1.scatter(spacing, peak_rate)
+
+# ax2.set_title('Stim')
+# peak_rate = data['stim']['max_rate'].to_numpy()
+# spacing = data['stim']['spacing'].to_numpy()
+# ax2.scatter(spacing, peak_rate, label=key)
+
+# ax2.legend()
+
+
+
+
+
+
@@ -16492,7 +16618,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
-In [44]:
+In [40]:
action = project.require_action("comparisons-gridcells")
@@ -16505,7 +16631,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
-In [45]:
+In [41]:
copy_tree(output_path, str(action.data_path()))
@@ -16521,7 +16647,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
- Out[45]:
+ Out[41]:
@@ -16619,7 +16745,7 @@ U-test: U value 1007.0 p value 0.07305394917377077
-In [46]:
+In [42]:
septum_mec.analysis.registration.store_notebook(action, "20_comparisons_gridcells.ipynb")
diff --git a/actions/comparisons-gridcells/data/20_comparisons_gridcells.ipynb b/actions/comparisons-gridcells/data/20_comparisons_gridcells.ipynb
index 344fab66f..1f0f48309 100644
--- a/actions/comparisons-gridcells/data/20_comparisons_gridcells.ipynb
+++ b/actions/comparisons-gridcells/data/20_comparisons_gridcells.ipynb
@@ -19,7 +19,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "15:56:22 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+ "19:21:26 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
@@ -244,7 +244,7 @@
" \n",
" \n",
"\n",
- "5 rows × 34 columns
\n",
+ "5 rows × 39 columns
\n",
"
"
],
"text/plain": [
@@ -262,19 +262,19 @@
"3 NaN False baseline ii ... 0.097464 \n",
"4 NaN False baseline ii ... 0.248036 \n",
"\n",
- " bursty_spike_ratio gridness border_score information_rate \\\n",
- "0 0.676486 -0.459487 0.078474 0.965845 \n",
- "1 0.277121 -0.615405 0.311180 0.191375 \n",
- "2 0.658748 -0.527711 0.131660 3.833587 \n",
- "3 0.196189 -0.641543 0.274989 0.153740 \n",
- "4 0.461250 -0.085292 0.198676 0.526720 \n",
+ " bursty_spike_ratio gridness border_score information_rate \\\n",
+ "0 0.676486 -0.459487 0.078474 0.965845 \n",
+ "1 0.277121 -0.615405 0.311180 0.191375 \n",
+ "2 0.658748 -0.527711 0.131660 3.833587 \n",
+ "3 0.196189 -0.641543 0.274989 0.153740 \n",
+ "4 0.461250 -0.085292 0.198676 0.526720 \n",
"\n",
- " information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
- "0 0.309723 5.788704 0.043321 0.624971 \n",
- "1 0.032266 1.821598 0.014624 0.753333 \n",
- "2 0.336590 4.407614 0.121115 0.542877 \n",
- "3 0.068626 6.128601 0.099223 0.484916 \n",
- "4 0.033667 1.602362 0.051825 0.646571 \n",
+ " information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
+ "0 0.309723 5.788704 0.043321 0.624971 \n",
+ "1 0.032266 1.821598 0.014624 0.753333 \n",
+ "2 0.336590 4.407614 0.121115 0.542877 \n",
+ "3 0.068626 6.128601 0.099223 0.484916 \n",
+ "4 0.033667 1.602362 0.051825 0.646571 \n",
"\n",
" orientation \n",
"0 22.067900 \n",
@@ -283,7 +283,7 @@
"3 11.309932 \n",
"4 0.000000 \n",
"\n",
- "[5 rows x 34 columns]"
+ "[5 rows x 39 columns]"
]
},
"execution_count": 4,
@@ -603,7 +603,7 @@
" \n",
" \n",
"\n",
- "5 rows × 41 columns
\n",
+ "5 rows × 46 columns
\n",
""
],
"text/plain": [
@@ -621,12 +621,12 @@
"3 NaN False baseline ii ... 0.099223 0.484916 \n",
"4 NaN False baseline ii ... 0.051825 0.646571 \n",
"\n",
- " orientation border_score_threshold gridness_threshold \\\n",
- "0 22.067900 0.332548 0.229073 \n",
- "1 0.000000 0.354830 0.089333 \n",
- "2 27.758541 0.264610 -0.121081 \n",
- "3 11.309932 0.344280 0.215829 \n",
- "4 0.000000 0.342799 0.218967 \n",
+ " orientation border_score_threshold gridness_threshold \\\n",
+ "0 22.067900 0.332548 0.229073 \n",
+ "1 0.000000 0.354830 0.089333 \n",
+ "2 27.758541 0.264610 -0.121081 \n",
+ "3 11.309932 0.344280 0.215829 \n",
+ "4 0.000000 0.342799 0.218967 \n",
"\n",
" head_mean_ang_threshold head_mean_vec_len_threshold \\\n",
"0 6.029431 0.205362 \n",
@@ -635,14 +635,14 @@
"3 6.033364 0.110495 \n",
"4 5.768170 0.054762 \n",
"\n",
- " information_rate_threshold speed_score_threshold specificity \n",
- "0 1.115825 0.066736 0.445206 \n",
- "1 0.223237 0.052594 0.097485 \n",
- "2 4.964984 0.027120 0.393687 \n",
- "3 0.239996 0.054074 0.262612 \n",
- "4 0.524990 0.144702 0.133677 \n",
+ " information_rate_threshold speed_score_threshold specificity \n",
+ "0 1.115825 0.066736 0.445206 \n",
+ "1 0.223237 0.052594 0.097485 \n",
+ "2 4.964984 0.027120 0.393687 \n",
+ "3 0.239996 0.054074 0.262612 \n",
+ "4 0.524990 0.144702 0.133677 \n",
"\n",
- "[5 rows x 41 columns]"
+ "[5 rows x 46 columns]"
]
},
"execution_count": 6,
@@ -668,7 +668,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -676,11 +676,11 @@
"text/plain": [
"stimulated\n",
"False 624\n",
- "True 674\n",
+ "True 660\n",
"Name: action, dtype: int64"
]
},
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -689,6 +689,15 @@
"data.groupby('stimulated').count()['action']"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -698,14 +707,14 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of gridcells 226\n",
+ "Number of gridcells 225\n",
"Number of animals 4\n"
]
}
@@ -719,44 +728,35 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
- "baseline = sessions_above_threshold.query('baseline')"
+ "gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "gridcell_in_baseline = data[data.unit_id.isin(baseline.unit_id)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of gridcells in baseline i sessions 78\n",
- "Number of gridcells in stimulated 11Hz ms sessions 35\n",
- "Number of gridcells in baseline ii sessions 66\n",
- "Number of gridcells in stimulated 30Hz ms sessions 33\n"
+ "Number of gridcells in baseline i sessions 76\n",
+ "Number of gridcells in stimulated 11Hz ms sessions 68\n",
+ "Number of gridcells in baseline ii sessions 64\n",
+ "Number of gridcells in stimulated 30Hz ms sessions 52\n"
]
}
],
"source": [
- "baseline_i = gridcell_in_baseline.query('baseline and i')\n",
- "stimulated_11 = gridcell_in_baseline.query('frequency==11 and stim_location==\"ms\" and i')\n",
+ "baseline_i = gridcell_sessions.query('baseline and Hz11')\n",
+ "stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location==\"ms\"')\n",
"\n",
- "baseline_ii = gridcell_in_baseline.query('baseline and ii')\n",
- "stimulated_30 = gridcell_in_baseline.query('frequency==30 and stim_location==\"ms\" and ii')\n",
+ "baseline_ii = gridcell_sessions.query('baseline and Hz30')\n",
+ "stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location==\"ms\"')\n",
"\n",
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
@@ -774,7 +774,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -786,17 +786,17 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of gridcells in baseline i sessions 68\n",
- "Number of gridcells in stimulated 11Hz ms sessions 32\n",
- "Number of gridcells in baseline ii sessions 58\n",
- "Number of gridcells in stimulated 30Hz ms sessions 28\n"
+ "Number of gridcells in baseline i sessions 70\n",
+ "Number of gridcells in stimulated 11Hz ms sessions 65\n",
+ "Number of gridcells in baseline ii sessions 61\n",
+ "Number of gridcells in stimulated 30Hz ms sessions 49\n"
]
}
],
@@ -817,7 +817,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -831,7 +831,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -889,35 +889,35 @@
" \n",
" \n",
" False \n",
- " 9.928801 \n",
- " 0.562122 \n",
- " 0.656634 \n",
- " 5.320886 \n",
- " 0.200475 \n",
- " 37.440262 \n",
- " 1.178277 \n",
- " 2.348004 \n",
- " 15.711336 \n",
- " 7.319828 \n",
- " 0.219627 \n",
- " 0.444021 \n",
- " 0.136236 \n",
+ " 10.046219 \n",
+ " 0.537204 \n",
+ " 0.656641 \n",
+ " 5.347833 \n",
+ " 0.205817 \n",
+ " 37.735779 \n",
+ " 1.175931 \n",
+ " 2.344483 \n",
+ " 15.790391 \n",
+ " 7.405761 \n",
+ " 0.219892 \n",
+ " 0.445701 \n",
+ " 0.132422 \n",
" \n",
" \n",
" True \n",
- " 9.554733 \n",
- " 0.365628 \n",
- " 0.666120 \n",
- " 6.232196 \n",
- " 0.194542 \n",
- " 39.864795 \n",
- " 1.063930 \n",
- " 2.328150 \n",
- " 14.582445 \n",
- " 7.121023 \n",
- " 0.205369 \n",
- " 0.445532 \n",
- " 0.102438 \n",
+ " 9.814609 \n",
+ " 0.433530 \n",
+ " 0.692547 \n",
+ " 5.280295 \n",
+ " 0.182564 \n",
+ " 34.650917 \n",
+ " 0.933478 \n",
+ " 2.247505 \n",
+ " 14.455320 \n",
+ " 7.429762 \n",
+ " 0.213281 \n",
+ " 0.419822 \n",
+ " 0.111848 \n",
" \n",
" \n",
"\n",
@@ -926,37 +926,37 @@
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
- "False 9.928801 0.562122 0.656634 5.320886 \n",
- "True 9.554733 0.365628 0.666120 6.232196 \n",
+ "False 10.046219 0.537204 0.656641 5.347833 \n",
+ "True 9.814609 0.433530 0.692547 5.280295 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
- "False 0.200475 37.440262 1.178277 \n",
- "True 0.194542 39.864795 1.063930 \n",
+ "False 0.205817 37.735779 1.175931 \n",
+ "True 0.182564 34.650917 0.933478 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
- "False 2.348004 15.711336 7.319828 \n",
- "True 2.328150 14.582445 7.121023 \n",
+ "False 2.344483 15.790391 7.405761 \n",
+ "True 2.247505 14.455320 7.429762 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
- "False 0.219627 0.444021 0.136236 \n",
- "True 0.205369 0.445532 0.102438 "
+ "False 0.219892 0.445701 0.132422 \n",
+ "True 0.213281 0.419822 0.111848 "
]
},
- "execution_count": 22,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "gridcell_in_baseline.groupby('stimulated')[columns].mean()"
+ "gridcell_sessions.groupby('stimulated')[columns].mean()"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -998,131 +998,131 @@
" \n",
" \n",
" count \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
- " 144.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
+ " 147.000000 \n",
" \n",
" \n",
" mean \n",
- " 9.928801 \n",
- " 0.562122 \n",
- " 0.656634 \n",
- " 5.320886 \n",
- " 0.200475 \n",
- " 37.440262 \n",
- " 1.178277 \n",
- " 2.348004 \n",
- " 15.711336 \n",
- " 7.319828 \n",
- " 0.219627 \n",
- " 0.444021 \n",
- " 0.136236 \n",
+ " 10.046219 \n",
+ " 0.537204 \n",
+ " 0.656641 \n",
+ " 5.347833 \n",
+ " 0.205817 \n",
+ " 37.735779 \n",
+ " 1.175931 \n",
+ " 2.344483 \n",
+ " 15.790391 \n",
+ " 7.405761 \n",
+ " 0.219892 \n",
+ " 0.445701 \n",
+ " 0.132422 \n",
" \n",
" \n",
" std \n",
- " 7.727249 \n",
- " 0.338826 \n",
- " 0.186070 \n",
- " 2.885443 \n",
- " 0.175036 \n",
- " 16.512138 \n",
- " 0.570617 \n",
- " 0.743517 \n",
- " 9.798591 \n",
- " 6.760978 \n",
- " 0.082774 \n",
- " 0.206192 \n",
- " 0.075267 \n",
+ " 7.913344 \n",
+ " 0.372942 \n",
+ " 0.190573 \n",
+ " 2.938819 \n",
+ " 0.192815 \n",
+ " 16.976912 \n",
+ " 0.582747 \n",
+ " 0.748791 \n",
+ " 9.952409 \n",
+ " 6.971963 \n",
+ " 0.083408 \n",
+ " 0.211635 \n",
+ " 0.075334 \n",
" \n",
" \n",
" min \n",
" 0.516375 \n",
- " -0.360777 \n",
- " 0.261912 \n",
- " 1.842905 \n",
- " 0.011661 \n",
+ " -0.599569 \n",
+ " 0.220235 \n",
+ " 1.762785 \n",
+ " 0.005947 \n",
" 3.013150 \n",
- " 0.122324 \n",
- " 1.361275 \n",
+ " 0.102101 \n",
+ " 1.067244 \n",
" 0.993877 \n",
- " 0.257364 \n",
+ " 0.185332 \n",
" 0.027228 \n",
- " 0.128469 \n",
+ " 0.072063 \n",
" -0.023795 \n",
" \n",
" \n",
" 25% \n",
- " 3.833480 \n",
- " 0.350175 \n",
- " 0.517566 \n",
- " 3.108402 \n",
- " 0.072654 \n",
- " 25.189028 \n",
- " 0.748273 \n",
- " 1.772429 \n",
- " 7.649203 \n",
- " 1.863476 \n",
- " 0.162862 \n",
- " 0.289491 \n",
- " 0.082031 \n",
+ " 3.811514 \n",
+ " 0.324174 \n",
+ " 0.515183 \n",
+ " 3.107181 \n",
+ " 0.071747 \n",
+ " 25.148584 \n",
+ " 0.737153 \n",
+ " 1.749688 \n",
+ " 7.628858 \n",
+ " 1.800796 \n",
+ " 0.162830 \n",
+ " 0.289405 \n",
+ " 0.078827 \n",
" \n",
" \n",
" 50% \n",
- " 7.101159 \n",
- " 0.595244 \n",
- " 0.701089 \n",
- " 4.682344 \n",
- " 0.139185 \n",
- " 34.014566 \n",
- " 1.064148 \n",
- " 2.170671 \n",
- " 12.863627 \n",
- " 4.773814 \n",
- " 0.213065 \n",
- " 0.383049 \n",
- " 0.130958 \n",
+ " 7.129568 \n",
+ " 0.579600 \n",
+ " 0.698596 \n",
+ " 4.675862 \n",
+ " 0.141391 \n",
+ " 34.348592 \n",
+ " 1.055340 \n",
+ " 2.173263 \n",
+ " 13.000207 \n",
+ " 4.835608 \n",
+ " 0.213831 \n",
+ " 0.390758 \n",
+ " 0.124640 \n",
" \n",
" \n",
" 75% \n",
- " 15.349392 \n",
- " 0.802880 \n",
- " 0.820432 \n",
- " 6.619374 \n",
- " 0.261063 \n",
- " 45.689916 \n",
- " 1.562027 \n",
- " 2.688595 \n",
- " 23.123564 \n",
- " 10.952948 \n",
- " 0.280340 \n",
- " 0.570619 \n",
- " 0.188830 \n",
+ " 15.685084 \n",
+ " 0.798542 \n",
+ " 0.823981 \n",
+ " 6.646175 \n",
+ " 0.265521 \n",
+ " 47.346567 \n",
+ " 1.570106 \n",
+ " 2.691555 \n",
+ " 22.415152 \n",
+ " 10.981344 \n",
+ " 0.282480 \n",
+ " 0.572782 \n",
+ " 0.183005 \n",
" \n",
" \n",
" max \n",
- " 31.367451 \n",
+ " 35.560173 \n",
" 1.174288 \n",
- " 0.954505 \n",
+ " 0.980148 \n",
" 17.011330 \n",
- " 0.918520 \n",
+ " 1.202862 \n",
" 90.839266 \n",
" 3.540663 \n",
" 5.240845 \n",
" 45.349506 \n",
- " 28.721619 \n",
+ " 32.997789 \n",
" 0.400014 \n",
" 0.975050 \n",
- " 0.323278 \n",
+ " 0.333463 \n",
" \n",
" \n",
"\n",
@@ -1130,58 +1130,58 @@
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
- "count 144.000000 144.000000 144.000000 144.000000 \n",
- "mean 9.928801 0.562122 0.656634 5.320886 \n",
- "std 7.727249 0.338826 0.186070 2.885443 \n",
- "min 0.516375 -0.360777 0.261912 1.842905 \n",
- "25% 3.833480 0.350175 0.517566 3.108402 \n",
- "50% 7.101159 0.595244 0.701089 4.682344 \n",
- "75% 15.349392 0.802880 0.820432 6.619374 \n",
- "max 31.367451 1.174288 0.954505 17.011330 \n",
+ "count 147.000000 147.000000 147.000000 147.000000 \n",
+ "mean 10.046219 0.537204 0.656641 5.347833 \n",
+ "std 7.913344 0.372942 0.190573 2.938819 \n",
+ "min 0.516375 -0.599569 0.220235 1.762785 \n",
+ "25% 3.811514 0.324174 0.515183 3.107181 \n",
+ "50% 7.129568 0.579600 0.698596 4.675862 \n",
+ "75% 15.685084 0.798542 0.823981 6.646175 \n",
+ "max 35.560173 1.174288 0.980148 17.011330 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
- "count 144.000000 144.000000 144.000000 \n",
- "mean 0.200475 37.440262 1.178277 \n",
- "std 0.175036 16.512138 0.570617 \n",
- "min 0.011661 3.013150 0.122324 \n",
- "25% 0.072654 25.189028 0.748273 \n",
- "50% 0.139185 34.014566 1.064148 \n",
- "75% 0.261063 45.689916 1.562027 \n",
- "max 0.918520 90.839266 3.540663 \n",
+ "count 147.000000 147.000000 147.000000 \n",
+ "mean 0.205817 37.735779 1.175931 \n",
+ "std 0.192815 16.976912 0.582747 \n",
+ "min 0.005947 3.013150 0.102101 \n",
+ "25% 0.071747 25.148584 0.737153 \n",
+ "50% 0.141391 34.348592 1.055340 \n",
+ "75% 0.265521 47.346567 1.570106 \n",
+ "max 1.202862 90.839266 3.540663 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
- "count 144.000000 144.000000 144.000000 \n",
- "mean 2.348004 15.711336 7.319828 \n",
- "std 0.743517 9.798591 6.760978 \n",
- "min 1.361275 0.993877 0.257364 \n",
- "25% 1.772429 7.649203 1.863476 \n",
- "50% 2.170671 12.863627 4.773814 \n",
- "75% 2.688595 23.123564 10.952948 \n",
- "max 5.240845 45.349506 28.721619 \n",
+ "count 147.000000 147.000000 147.000000 \n",
+ "mean 2.344483 15.790391 7.405761 \n",
+ "std 0.748791 9.952409 6.971963 \n",
+ "min 1.067244 0.993877 0.185332 \n",
+ "25% 1.749688 7.628858 1.800796 \n",
+ "50% 2.173263 13.000207 4.835608 \n",
+ "75% 2.691555 22.415152 10.981344 \n",
+ "max 5.240845 45.349506 32.997789 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
- "count 144.000000 144.000000 144.000000 \n",
- "mean 0.219627 0.444021 0.136236 \n",
- "std 0.082774 0.206192 0.075267 \n",
- "min 0.027228 0.128469 -0.023795 \n",
- "25% 0.162862 0.289491 0.082031 \n",
- "50% 0.213065 0.383049 0.130958 \n",
- "75% 0.280340 0.570619 0.188830 \n",
- "max 0.400014 0.975050 0.323278 "
+ "count 147.000000 147.000000 147.000000 \n",
+ "mean 0.219892 0.445701 0.132422 \n",
+ "std 0.083408 0.211635 0.075334 \n",
+ "min 0.027228 0.072063 -0.023795 \n",
+ "25% 0.162830 0.289405 0.078827 \n",
+ "50% 0.213831 0.390758 0.124640 \n",
+ "75% 0.282480 0.572782 0.183005 \n",
+ "max 0.400014 0.975050 0.333463 "
]
},
- "execution_count": 23,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "gridcell_in_baseline.query('baseline')[columns].describe()"
+ "gridcell_sessions.query('baseline')[columns].describe()"
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -1223,185 +1223,185 @@
" \n",
" \n",
" count \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
- " 73.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
+ " 124.000000 \n",
" \n",
" \n",
" mean \n",
- " 9.554733 \n",
- " 0.365628 \n",
- " 0.666120 \n",
- " 6.232196 \n",
- " 0.194542 \n",
- " 39.864795 \n",
- " 1.063930 \n",
- " 2.328150 \n",
- " 14.582445 \n",
- " 7.121023 \n",
- " 0.205369 \n",
- " 0.445532 \n",
- " 0.102438 \n",
+ " 9.814609 \n",
+ " 0.433530 \n",
+ " 0.692547 \n",
+ " 5.280295 \n",
+ " 0.182564 \n",
+ " 34.650917 \n",
+ " 0.933478 \n",
+ " 2.247505 \n",
+ " 14.455320 \n",
+ " 7.429762 \n",
+ " 0.213281 \n",
+ " 0.419822 \n",
+ " 0.111848 \n",
" \n",
" \n",
" std \n",
- " 7.334232 \n",
- " 0.397430 \n",
- " 0.194908 \n",
- " 5.760291 \n",
- " 0.161491 \n",
- " 25.342874 \n",
- " 0.478339 \n",
- " 0.731921 \n",
- " 8.551638 \n",
- " 6.482068 \n",
- " 0.075895 \n",
- " 0.230698 \n",
- " 0.077154 \n",
+ " 7.676536 \n",
+ " 0.387343 \n",
+ " 0.197445 \n",
+ " 3.520949 \n",
+ " 0.208775 \n",
+ " 14.511629 \n",
+ " 0.492383 \n",
+ " 0.750923 \n",
+ " 8.796338 \n",
+ " 6.881408 \n",
+ " 0.077978 \n",
+ " 0.231655 \n",
+ " 0.076247 \n",
" \n",
" \n",
" min \n",
- " 1.371102 \n",
- " -0.482293 \n",
- " 0.297108 \n",
- " 1.920211 \n",
- " 0.020735 \n",
- " 10.492070 \n",
- " 0.292174 \n",
- " 1.332239 \n",
- " 3.531824 \n",
- " 0.573040 \n",
- " 0.042956 \n",
- " 0.137978 \n",
- " -0.072000 \n",
+ " 0.571675 \n",
+ " -0.509346 \n",
+ " 0.161197 \n",
+ " 1.502176 \n",
+ " 0.005851 \n",
+ " 8.703201 \n",
+ " 0.096607 \n",
+ " 1.060662 \n",
+ " 2.327366 \n",
+ " 0.212979 \n",
+ " 0.041561 \n",
+ " 0.075519 \n",
+ " -0.073931 \n",
" \n",
" \n",
" 25% \n",
- " 3.596484 \n",
- " 0.052315 \n",
- " 0.466494 \n",
- " 3.531741 \n",
- " 0.072324 \n",
- " 25.324427 \n",
- " 0.707876 \n",
- " 1.742983 \n",
- " 8.127398 \n",
- " 1.813572 \n",
- " 0.159265 \n",
- " 0.248962 \n",
- " 0.052617 \n",
+ " 3.835569 \n",
+ " 0.194332 \n",
+ " 0.552817 \n",
+ " 2.819310 \n",
+ " 0.062615 \n",
+ " 24.286536 \n",
+ " 0.552133 \n",
+ " 1.671374 \n",
+ " 8.097415 \n",
+ " 2.038374 \n",
+ " 0.160874 \n",
+ " 0.243125 \n",
+ " 0.065039 \n",
" \n",
" \n",
" 50% \n",
- " 7.237246 \n",
- " 0.290593 \n",
- " 0.729540 \n",
- " 4.476625 \n",
- " 0.113483 \n",
- " 33.048050 \n",
- " 0.993926 \n",
- " 2.212266 \n",
- " 12.308800 \n",
- " 4.675997 \n",
- " 0.199137 \n",
- " 0.365960 \n",
- " 0.094618 \n",
+ " 7.690325 \n",
+ " 0.413583 \n",
+ " 0.733832 \n",
+ " 4.446917 \n",
+ " 0.109036 \n",
+ " 32.040628 \n",
+ " 0.879876 \n",
+ " 2.098479 \n",
+ " 12.325347 \n",
+ " 5.718993 \n",
+ " 0.204469 \n",
+ " 0.361016 \n",
+ " 0.105714 \n",
" \n",
" \n",
" 75% \n",
- " 14.029394 \n",
- " 0.679854 \n",
- " 0.853552 \n",
- " 7.867471 \n",
- " 0.307852 \n",
- " 46.159854 \n",
- " 1.242135 \n",
- " 2.822726 \n",
- " 19.752448 \n",
- " 10.723556 \n",
- " 0.261186 \n",
- " 0.670842 \n",
- " 0.139564 \n",
+ " 14.035706 \n",
+ " 0.723850 \n",
+ " 0.861439 \n",
+ " 6.438574 \n",
+ " 0.219362 \n",
+ " 42.320860 \n",
+ " 1.196084 \n",
+ " 2.651945 \n",
+ " 19.237536 \n",
+ " 10.972856 \n",
+ " 0.266557 \n",
+ " 0.561412 \n",
+ " 0.159393 \n",
" \n",
" \n",
" max \n",
- " 31.800150 \n",
- " 1.110681 \n",
- " 0.925871 \n",
- " 45.427380 \n",
- " 0.678935 \n",
- " 199.999821 \n",
+ " 34.844930 \n",
+ " 1.230658 \n",
+ " 0.983263 \n",
+ " 25.599598 \n",
+ " 1.296616 \n",
+ " 76.146357 \n",
" 2.918984 \n",
- " 4.604317 \n",
- " 39.093347 \n",
- " 25.836762 \n",
+ " 5.324055 \n",
+ " 42.803943 \n",
+ " 31.519482 \n",
" 0.406678 \n",
- " 0.966010 \n",
- " 0.336072 \n",
+ " 1.077313 \n",
+ " 0.349283 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " average_rate gridness sparsity selectivity \\\n",
- "count 73.000000 73.000000 73.000000 73.000000 \n",
- "mean 9.554733 0.365628 0.666120 6.232196 \n",
- "std 7.334232 0.397430 0.194908 5.760291 \n",
- "min 1.371102 -0.482293 0.297108 1.920211 \n",
- "25% 3.596484 0.052315 0.466494 3.531741 \n",
- "50% 7.237246 0.290593 0.729540 4.476625 \n",
- "75% 14.029394 0.679854 0.853552 7.867471 \n",
- "max 31.800150 1.110681 0.925871 45.427380 \n",
+ " average_rate gridness sparsity selectivity \\\n",
+ "count 124.000000 124.000000 124.000000 124.000000 \n",
+ "mean 9.814609 0.433530 0.692547 5.280295 \n",
+ "std 7.676536 0.387343 0.197445 3.520949 \n",
+ "min 0.571675 -0.509346 0.161197 1.502176 \n",
+ "25% 3.835569 0.194332 0.552817 2.819310 \n",
+ "50% 7.690325 0.413583 0.733832 4.446917 \n",
+ "75% 14.035706 0.723850 0.861439 6.438574 \n",
+ "max 34.844930 1.230658 0.983263 25.599598 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
- "count 73.000000 73.000000 73.000000 \n",
- "mean 0.194542 39.864795 1.063930 \n",
- "std 0.161491 25.342874 0.478339 \n",
- "min 0.020735 10.492070 0.292174 \n",
- "25% 0.072324 25.324427 0.707876 \n",
- "50% 0.113483 33.048050 0.993926 \n",
- "75% 0.307852 46.159854 1.242135 \n",
- "max 0.678935 199.999821 2.918984 \n",
+ "count 124.000000 124.000000 124.000000 \n",
+ "mean 0.182564 34.650917 0.933478 \n",
+ "std 0.208775 14.511629 0.492383 \n",
+ "min 0.005851 8.703201 0.096607 \n",
+ "25% 0.062615 24.286536 0.552133 \n",
+ "50% 0.109036 32.040628 0.879876 \n",
+ "75% 0.219362 42.320860 1.196084 \n",
+ "max 1.296616 76.146357 2.918984 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
- "count 73.000000 73.000000 73.000000 \n",
- "mean 2.328150 14.582445 7.121023 \n",
- "std 0.731921 8.551638 6.482068 \n",
- "min 1.332239 3.531824 0.573040 \n",
- "25% 1.742983 8.127398 1.813572 \n",
- "50% 2.212266 12.308800 4.675997 \n",
- "75% 2.822726 19.752448 10.723556 \n",
- "max 4.604317 39.093347 25.836762 \n",
+ "count 124.000000 124.000000 124.000000 \n",
+ "mean 2.247505 14.455320 7.429762 \n",
+ "std 0.750923 8.796338 6.881408 \n",
+ "min 1.060662 2.327366 0.212979 \n",
+ "25% 1.671374 8.097415 2.038374 \n",
+ "50% 2.098479 12.325347 5.718993 \n",
+ "75% 2.651945 19.237536 10.972856 \n",
+ "max 5.324055 42.803943 31.519482 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
- "count 73.000000 73.000000 73.000000 \n",
- "mean 0.205369 0.445532 0.102438 \n",
- "std 0.075895 0.230698 0.077154 \n",
- "min 0.042956 0.137978 -0.072000 \n",
- "25% 0.159265 0.248962 0.052617 \n",
- "50% 0.199137 0.365960 0.094618 \n",
- "75% 0.261186 0.670842 0.139564 \n",
- "max 0.406678 0.966010 0.336072 "
+ "count 124.000000 124.000000 124.000000 \n",
+ "mean 0.213281 0.419822 0.111848 \n",
+ "std 0.077978 0.231655 0.076247 \n",
+ "min 0.041561 0.075519 -0.073931 \n",
+ "25% 0.160874 0.243125 0.065039 \n",
+ "50% 0.204469 0.361016 0.105714 \n",
+ "75% 0.266557 0.561412 0.159393 \n",
+ "max 0.406678 1.077313 0.349283 "
]
},
- "execution_count": 24,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "gridcell_in_baseline.query(\"stimulated\")[columns].describe()"
+ "gridcell_sessions.query(\"stimulated\")[columns].describe()"
]
},
{
@@ -1413,7 +1413,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -1450,7 +1450,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -1474,8 +1474,8 @@
" \n",
" \n",
" \n",
- " Stimulated \n",
" Baseline \n",
+ " Stimulated \n",
" MWU \n",
" PRS \n",
" \n",
@@ -1483,144 +1483,145 @@
" \n",
" \n",
" Average rate \n",
- " 9.55 ± 0.86 (73) \n",
- " 9.93 ± 0.64 (144) \n",
- " 5120.00, 0.757 \n",
- " 0.14, 0.868 \n",
+ " 10.05 ± 0.65 (147) \n",
+ " 9.81 ± 0.69 (124) \n",
+ " 9040.00, 0.909 \n",
+ " 0.56, 0.717 \n",
" \n",
" \n",
" Gridness \n",
- " 0.37 ± 0.05 (73) \n",
- " 0.56 ± 0.03 (144) \n",
- " 3718.00, 0.000 \n",
- " 0.30, 0.000 \n",
+ " 0.54 ± 0.03 (147) \n",
+ " 0.43 ± 0.03 (124) \n",
+ " 7516.00, 0.013 \n",
+ " 0.17, 0.004 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.67 ± 0.02 (73) \n",
- " 0.66 ± 0.02 (144) \n",
- " 5515.00, 0.554 \n",
- " 0.03, 0.413 \n",
+ " 0.66 ± 0.02 (147) \n",
+ " 0.69 ± 0.02 (124) \n",
+ " 10275.00, 0.071 \n",
+ " 0.04, 0.161 \n",
" \n",
" \n",
" Selectivity \n",
- " 6.23 ± 0.67 (73) \n",
- " 5.32 ± 0.24 (144) \n",
- " 5482.00, 0.606 \n",
- " 0.21, 0.718 \n",
+ " 5.35 ± 0.24 (147) \n",
+ " 5.28 ± 0.32 (124) \n",
+ " 8488.00, 0.330 \n",
+ " 0.23, 0.450 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.19 ± 0.02 (73) \n",
- " 0.20 ± 0.01 (144) \n",
- " 5094.00, 0.712 \n",
- " 0.03, 0.501 \n",
+ " 0.21 ± 0.02 (147) \n",
+ " 0.18 ± 0.02 (124) \n",
+ " 7883.00, 0.056 \n",
+ " 0.03, 0.103 \n",
" \n",
" \n",
" Max rate \n",
- " 39.86 ± 2.97 (73) \n",
- " 37.44 ± 1.38 (144) \n",
- " 5256.00, 0.999 \n",
- " 0.97, 0.592 \n",
+ " 37.74 ± 1.40 (147) \n",
+ " 34.65 ± 1.30 (124) \n",
+ " 8165.00, 0.140 \n",
+ " 2.31, 0.108 \n",
" \n",
" \n",
" Information rate \n",
- " 1.06 ± 0.06 (73) \n",
- " 1.18 ± 0.05 (144) \n",
- " 4681.00, 0.189 \n",
- " 0.07, 0.426 \n",
+ " 1.18 ± 0.05 (147) \n",
+ " 0.93 ± 0.04 (124) \n",
+ " 6772.00, 0.000 \n",
+ " 0.18, 0.008 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.33 ± 0.09 (73) \n",
- " 2.35 ± 0.06 (144) \n",
- " 5197.00, 0.894 \n",
- " 0.04, 0.715 \n",
+ " 2.34 ± 0.06 (147) \n",
+ " 2.25 ± 0.07 (124) \n",
+ " 8361.00, 0.242 \n",
+ " 0.07, 0.500 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 14.58 ± 1.00 (73) \n",
- " 15.71 ± 0.82 (144) \n",
- " 5000.00, 0.559 \n",
- " 0.55, 0.751 \n",
+ " 15.79 ± 0.82 (147) \n",
+ " 14.46 ± 0.79 (124) \n",
+ " 8526.00, 0.361 \n",
+ " 0.67, 0.638 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.12 ± 0.76 (73) \n",
- " 7.32 ± 0.56 (144) \n",
- " 5166.00, 0.838 \n",
- " 0.10, 0.934 \n",
+ " 7.41 ± 0.58 (147) \n",
+ " 7.43 ± 0.62 (124) \n",
+ " 9193.00, 0.903 \n",
+ " 0.88, 0.456 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.21 ± 0.01 (73) \n",
- " 0.22 ± 0.01 (144) \n",
- " 4677.00, 0.186 \n",
- " 0.01, 0.212 \n",
+ " 0.22 ± 0.01 (147) \n",
+ " 0.21 ± 0.01 (124) \n",
+ " 8548.00, 0.379 \n",
+ " 0.01, 0.370 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.03 (73) \n",
- " 0.44 ± 0.02 (144) \n",
- " 5076.00, 0.681 \n",
- " 0.02, 0.547 \n",
+ " 0.45 ± 0.02 (147) \n",
+ " 0.42 ± 0.02 (124) \n",
+ " 8221.00, 0.165 \n",
+ " 0.03, 0.167 \n",
" \n",
" \n",
" Speed score \n",
- " 0.10 ± 0.01 (73) \n",
- " 0.14 ± 0.01 (144) \n",
- " 3978.00, 0.003 \n",
- " 0.04, 0.008 \n",
+ " 0.13 ± 0.01 (147) \n",
+ " 0.11 ± 0.01 (124) \n",
+ " 7793.00, 0.040 \n",
+ " 0.02, 0.046 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Stimulated Baseline \\\n",
- "Average rate 9.55 ± 0.86 (73) 9.93 ± 0.64 (144) \n",
- "Gridness 0.37 ± 0.05 (73) 0.56 ± 0.03 (144) \n",
- "Sparsity 0.67 ± 0.02 (73) 0.66 ± 0.02 (144) \n",
- "Selectivity 6.23 ± 0.67 (73) 5.32 ± 0.24 (144) \n",
- "Information specificity 0.19 ± 0.02 (73) 0.20 ± 0.01 (144) \n",
- "Max rate 39.86 ± 2.97 (73) 37.44 ± 1.38 (144) \n",
- "Information rate 1.06 ± 0.06 (73) 1.18 ± 0.05 (144) \n",
- "Interspike interval cv 2.33 ± 0.09 (73) 2.35 ± 0.06 (144) \n",
- "In-field mean rate 14.58 ± 1.00 (73) 15.71 ± 0.82 (144) \n",
- "Out-field mean rate 7.12 ± 0.76 (73) 7.32 ± 0.56 (144) \n",
- "Burst event ratio 0.21 ± 0.01 (73) 0.22 ± 0.01 (144) \n",
- "Specificity 0.45 ± 0.03 (73) 0.44 ± 0.02 (144) \n",
- "Speed score 0.10 ± 0.01 (73) 0.14 ± 0.01 (144) \n",
+ " Baseline Stimulated \\\n",
+ "Average rate 10.05 ± 0.65 (147) 9.81 ± 0.69 (124) \n",
+ "Gridness 0.54 ± 0.03 (147) 0.43 ± 0.03 (124) \n",
+ "Sparsity 0.66 ± 0.02 (147) 0.69 ± 0.02 (124) \n",
+ "Selectivity 5.35 ± 0.24 (147) 5.28 ± 0.32 (124) \n",
+ "Information specificity 0.21 ± 0.02 (147) 0.18 ± 0.02 (124) \n",
+ "Max rate 37.74 ± 1.40 (147) 34.65 ± 1.30 (124) \n",
+ "Information rate 1.18 ± 0.05 (147) 0.93 ± 0.04 (124) \n",
+ "Interspike interval cv 2.34 ± 0.06 (147) 2.25 ± 0.07 (124) \n",
+ "In-field mean rate 15.79 ± 0.82 (147) 14.46 ± 0.79 (124) \n",
+ "Out-field mean rate 7.41 ± 0.58 (147) 7.43 ± 0.62 (124) \n",
+ "Burst event ratio 0.22 ± 0.01 (147) 0.21 ± 0.01 (124) \n",
+ "Specificity 0.45 ± 0.02 (147) 0.42 ± 0.02 (124) \n",
+ "Speed score 0.13 ± 0.01 (147) 0.11 ± 0.01 (124) \n",
"\n",
- " MWU PRS \n",
- "Average rate 5120.00, 0.757 0.14, 0.868 \n",
- "Gridness 3718.00, 0.000 0.30, 0.000 \n",
- "Sparsity 5515.00, 0.554 0.03, 0.413 \n",
- "Selectivity 5482.00, 0.606 0.21, 0.718 \n",
- "Information specificity 5094.00, 0.712 0.03, 0.501 \n",
- "Max rate 5256.00, 0.999 0.97, 0.592 \n",
- "Information rate 4681.00, 0.189 0.07, 0.426 \n",
- "Interspike interval cv 5197.00, 0.894 0.04, 0.715 \n",
- "In-field mean rate 5000.00, 0.559 0.55, 0.751 \n",
- "Out-field mean rate 5166.00, 0.838 0.10, 0.934 \n",
- "Burst event ratio 4677.00, 0.186 0.01, 0.212 \n",
- "Specificity 5076.00, 0.681 0.02, 0.547 \n",
- "Speed score 3978.00, 0.003 0.04, 0.008 "
+ " MWU PRS \n",
+ "Average rate 9040.00, 0.909 0.56, 0.717 \n",
+ "Gridness 7516.00, 0.013 0.17, 0.004 \n",
+ "Sparsity 10275.00, 0.071 0.04, 0.161 \n",
+ "Selectivity 8488.00, 0.330 0.23, 0.450 \n",
+ "Information specificity 7883.00, 0.056 0.03, 0.103 \n",
+ "Max rate 8165.00, 0.140 2.31, 0.108 \n",
+ "Information rate 6772.00, 0.000 0.18, 0.008 \n",
+ "Interspike interval cv 8361.00, 0.242 0.07, 0.500 \n",
+ "In-field mean rate 8526.00, 0.361 0.67, 0.638 \n",
+ "Out-field mean rate 9193.00, 0.903 0.88, 0.456 \n",
+ "Burst event ratio 8548.00, 0.379 0.01, 0.370 \n",
+ "Specificity 8221.00, 0.165 0.03, 0.167 \n",
+ "Speed score 7793.00, 0.040 0.02, 0.046 "
]
},
- "execution_count": 26,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "_stim_data = gridcell_in_baseline.query('stimulated')\n",
- "_base_data = gridcell_in_baseline.query('baseline')\n",
+ "_stim_data = gridcell_sessions.query('stimulated')\n",
+ "_base_data = gridcell_sessions.query('baseline')\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
- "result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
+ "result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
+ "\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
@@ -1634,7 +1635,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -1658,8 +1659,8 @@
" \n",
" \n",
" \n",
- " Stimulated \n",
" Baseline \n",
+ " 11 Hz \n",
" MWU \n",
" PRS \n",
" \n",
@@ -1667,132 +1668,132 @@
" \n",
" \n",
" Average rate \n",
- " 9.06 ± 1.21 (32) \n",
- " 9.65 ± 0.90 (68) \n",
- " 1044.00, 0.748 \n",
- " 0.02, 0.997 \n",
+ " 9.82 ± 0.91 (70) \n",
+ " 9.28 ± 0.90 (65) \n",
+ " 2175.00, 0.661 \n",
+ " 0.18, 0.933 \n",
" \n",
" \n",
" Gridness \n",
- " 0.34 ± 0.06 (32) \n",
- " 0.58 ± 0.04 (68) \n",
- " 676.00, 0.002 \n",
- " 0.27, 0.003 \n",
+ " 0.54 ± 0.05 (70) \n",
+ " 0.42 ± 0.05 (65) \n",
+ " 1822.00, 0.046 \n",
+ " 0.17, 0.052 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.67 ± 0.03 (32) \n",
- " 0.65 ± 0.02 (68) \n",
- " 1154.00, 0.628 \n",
- " 0.06, 0.319 \n",
+ " 0.65 ± 0.02 (70) \n",
+ " 0.69 ± 0.02 (65) \n",
+ " 2578.00, 0.183 \n",
+ " 0.06, 0.147 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.43 ± 0.47 (32) \n",
- " 5.22 ± 0.35 (68) \n",
- " 1140.00, 0.704 \n",
- " 0.29, 0.705 \n",
+ " 5.25 ± 0.35 (70) \n",
+ " 5.43 ± 0.48 (65) \n",
+ " 2214.00, 0.790 \n",
+ " 0.05, 0.961 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.19 ± 0.03 (32) \n",
- " 0.21 ± 0.02 (68) \n",
- " 1005.00, 0.542 \n",
- " 0.05, 0.095 \n",
+ " 0.22 ± 0.03 (70) \n",
+ " 0.19 ± 0.03 (65) \n",
+ " 1888.00, 0.089 \n",
+ " 0.05, 0.020 \n",
" \n",
" \n",
" Max rate \n",
- " 35.53 ± 2.50 (32) \n",
- " 36.19 ± 1.79 (68) \n",
- " 1063.00, 0.856 \n",
- " 0.04, 0.972 \n",
+ " 36.77 ± 1.96 (70) \n",
+ " 33.16 ± 1.79 (65) \n",
+ " 1971.00, 0.181 \n",
+ " 3.18, 0.250 \n",
" \n",
" \n",
" Information rate \n",
- " 1.04 ± 0.10 (32) \n",
- " 1.21 ± 0.06 (68) \n",
- " 867.00, 0.103 \n",
- " 0.12, 0.225 \n",
+ " 1.22 ± 0.06 (70) \n",
+ " 0.89 ± 0.06 (65) \n",
+ " 1431.00, 0.000 \n",
+ " 0.20, 0.006 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.29 ± 0.12 (32) \n",
- " 2.38 ± 0.10 (68) \n",
- " 1053.00, 0.799 \n",
- " 0.04, 0.891 \n",
+ " 2.37 ± 0.09 (70) \n",
+ " 2.24 ± 0.09 (65) \n",
+ " 2022.00, 0.266 \n",
+ " 0.12, 0.520 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 13.87 ± 1.42 (32) \n",
- " 15.27 ± 1.12 (68) \n",
- " 1024.00, 0.639 \n",
- " 0.10, 0.948 \n",
+ " 15.52 ± 1.15 (70) \n",
+ " 13.80 ± 1.06 (65) \n",
+ " 2064.00, 0.354 \n",
+ " 0.63, 0.738 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 6.52 ± 1.04 (32) \n",
- " 6.98 ± 0.76 (68) \n",
- " 1037.00, 0.709 \n",
- " 0.35, 0.905 \n",
+ " 7.09 ± 0.77 (70) \n",
+ " 7.00 ± 0.80 (65) \n",
+ " 2236.00, 0.865 \n",
+ " 0.01, 0.979 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.23 ± 0.01 (32) \n",
- " 0.23 ± 0.01 (68) \n",
- " 1158.00, 0.608 \n",
- " 0.01, 0.478 \n",
+ " 0.23 ± 0.01 (70) \n",
+ " 0.23 ± 0.01 (65) \n",
+ " 2307.00, 0.890 \n",
+ " 0.01, 0.732 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.04 (32) \n",
- " 0.45 ± 0.02 (68) \n",
- " 1060.00, 0.839 \n",
- " 0.01, 0.852 \n",
+ " 0.45 ± 0.03 (70) \n",
+ " 0.42 ± 0.03 (65) \n",
+ " 2049.00, 0.321 \n",
+ " 0.01, 0.476 \n",
" \n",
" \n",
" Speed score \n",
- " 0.09 ± 0.01 (32) \n",
- " 0.14 ± 0.01 (68) \n",
- " 736.00, 0.009 \n",
- " 0.05, 0.011 \n",
+ " 0.14 ± 0.01 (70) \n",
+ " 0.12 ± 0.01 (65) \n",
+ " 1939.00, 0.140 \n",
+ " 0.03, 0.069 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Stimulated Baseline MWU \\\n",
- "Average rate 9.06 ± 1.21 (32) 9.65 ± 0.90 (68) 1044.00, 0.748 \n",
- "Gridness 0.34 ± 0.06 (32) 0.58 ± 0.04 (68) 676.00, 0.002 \n",
- "Sparsity 0.67 ± 0.03 (32) 0.65 ± 0.02 (68) 1154.00, 0.628 \n",
- "Selectivity 5.43 ± 0.47 (32) 5.22 ± 0.35 (68) 1140.00, 0.704 \n",
- "Information specificity 0.19 ± 0.03 (32) 0.21 ± 0.02 (68) 1005.00, 0.542 \n",
- "Max rate 35.53 ± 2.50 (32) 36.19 ± 1.79 (68) 1063.00, 0.856 \n",
- "Information rate 1.04 ± 0.10 (32) 1.21 ± 0.06 (68) 867.00, 0.103 \n",
- "Interspike interval cv 2.29 ± 0.12 (32) 2.38 ± 0.10 (68) 1053.00, 0.799 \n",
- "In-field mean rate 13.87 ± 1.42 (32) 15.27 ± 1.12 (68) 1024.00, 0.639 \n",
- "Out-field mean rate 6.52 ± 1.04 (32) 6.98 ± 0.76 (68) 1037.00, 0.709 \n",
- "Burst event ratio 0.23 ± 0.01 (32) 0.23 ± 0.01 (68) 1158.00, 0.608 \n",
- "Specificity 0.45 ± 0.04 (32) 0.45 ± 0.02 (68) 1060.00, 0.839 \n",
- "Speed score 0.09 ± 0.01 (32) 0.14 ± 0.01 (68) 736.00, 0.009 \n",
+ " Baseline 11 Hz MWU \\\n",
+ "Average rate 9.82 ± 0.91 (70) 9.28 ± 0.90 (65) 2175.00, 0.661 \n",
+ "Gridness 0.54 ± 0.05 (70) 0.42 ± 0.05 (65) 1822.00, 0.046 \n",
+ "Sparsity 0.65 ± 0.02 (70) 0.69 ± 0.02 (65) 2578.00, 0.183 \n",
+ "Selectivity 5.25 ± 0.35 (70) 5.43 ± 0.48 (65) 2214.00, 0.790 \n",
+ "Information specificity 0.22 ± 0.03 (70) 0.19 ± 0.03 (65) 1888.00, 0.089 \n",
+ "Max rate 36.77 ± 1.96 (70) 33.16 ± 1.79 (65) 1971.00, 0.181 \n",
+ "Information rate 1.22 ± 0.06 (70) 0.89 ± 0.06 (65) 1431.00, 0.000 \n",
+ "Interspike interval cv 2.37 ± 0.09 (70) 2.24 ± 0.09 (65) 2022.00, 0.266 \n",
+ "In-field mean rate 15.52 ± 1.15 (70) 13.80 ± 1.06 (65) 2064.00, 0.354 \n",
+ "Out-field mean rate 7.09 ± 0.77 (70) 7.00 ± 0.80 (65) 2236.00, 0.865 \n",
+ "Burst event ratio 0.23 ± 0.01 (70) 0.23 ± 0.01 (65) 2307.00, 0.890 \n",
+ "Specificity 0.45 ± 0.03 (70) 0.42 ± 0.03 (65) 2049.00, 0.321 \n",
+ "Speed score 0.14 ± 0.01 (70) 0.12 ± 0.01 (65) 1939.00, 0.140 \n",
"\n",
" PRS \n",
- "Average rate 0.02, 0.997 \n",
- "Gridness 0.27, 0.003 \n",
- "Sparsity 0.06, 0.319 \n",
- "Selectivity 0.29, 0.705 \n",
- "Information specificity 0.05, 0.095 \n",
- "Max rate 0.04, 0.972 \n",
- "Information rate 0.12, 0.225 \n",
- "Interspike interval cv 0.04, 0.891 \n",
- "In-field mean rate 0.10, 0.948 \n",
- "Out-field mean rate 0.35, 0.905 \n",
- "Burst event ratio 0.01, 0.478 \n",
- "Specificity 0.01, 0.852 \n",
- "Speed score 0.05, 0.011 "
+ "Average rate 0.18, 0.933 \n",
+ "Gridness 0.17, 0.052 \n",
+ "Sparsity 0.06, 0.147 \n",
+ "Selectivity 0.05, 0.961 \n",
+ "Information specificity 0.05, 0.020 \n",
+ "Max rate 3.18, 0.250 \n",
+ "Information rate 0.20, 0.006 \n",
+ "Interspike interval cv 0.12, 0.520 \n",
+ "In-field mean rate 0.63, 0.738 \n",
+ "Out-field mean rate 0.01, 0.979 \n",
+ "Burst event ratio 0.01, 0.732 \n",
+ "Specificity 0.01, 0.476 \n",
+ "Speed score 0.03, 0.069 "
]
},
- "execution_count": 27,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -1803,8 +1804,9 @@
"\n",
"result = pd.DataFrame()\n",
"\n",
- "result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
+ "result['11 Hz'] = _stim_data[columns].agg(summarize)\n",
+ "\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
@@ -1819,7 +1821,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -1843,8 +1845,8 @@
" \n",
" \n",
" \n",
- " Stimulated \n",
" Baseline \n",
+ " 30 Hz \n",
" MWU \n",
" PRS \n",
" \n",
@@ -1852,132 +1854,132 @@
" \n",
" \n",
" Average rate \n",
- " 10.11 ± 1.51 (28) \n",
- " 10.01 ± 1.06 (58) \n",
- " 808.00, 0.974 \n",
- " 0.07, 0.968 \n",
+ " 10.08 ± 1.05 (61) \n",
+ " 9.94 ± 1.17 (49) \n",
+ " 1491.00, 0.986 \n",
+ " 0.24, 0.763 \n",
" \n",
" \n",
" Gridness \n",
- " 0.28 ± 0.08 (28) \n",
- " 0.57 ± 0.05 (58) \n",
- " 493.00, 0.003 \n",
- " 0.46, 0.000 \n",
+ " 0.53 ± 0.05 (61) \n",
+ " 0.46 ± 0.06 (49) \n",
+ " 1342.00, 0.361 \n",
+ " 0.08, 0.289 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.68 ± 0.04 (28) \n",
- " 0.66 ± 0.02 (58) \n",
- " 881.00, 0.528 \n",
- " 0.04, 0.328 \n",
+ " 0.67 ± 0.02 (61) \n",
+ " 0.69 ± 0.03 (49) \n",
+ " 1622.00, 0.445 \n",
+ " 0.03, 0.466 \n",
" \n",
" \n",
" Selectivity \n",
- " 7.47 ± 1.63 (28) \n",
- " 5.53 ± 0.40 (58) \n",
- " 809.00, 0.982 \n",
- " 0.30, 0.638 \n",
+ " 5.34 ± 0.38 (61) \n",
+ " 5.21 ± 0.46 (49) \n",
+ " 1372.00, 0.463 \n",
+ " 0.37, 0.420 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.20 ± 0.03 (28) \n",
- " 0.19 ± 0.02 (58) \n",
- " 812.00, 0.996 \n",
- " 0.01, 0.588 \n",
+ " 0.19 ± 0.02 (61) \n",
+ " 0.18 ± 0.03 (49) \n",
+ " 1380.00, 0.493 \n",
+ " 0.01, 0.725 \n",
" \n",
" \n",
" Max rate \n",
- " 45.33 ± 6.85 (28) \n",
- " 38.95 ± 2.48 (58) \n",
- " 797.00, 0.894 \n",
- " 2.09, 0.451 \n",
+ " 37.61 ± 2.31 (61) \n",
+ " 34.42 ± 1.99 (49) \n",
+ " 1342.00, 0.361 \n",
+ " 2.37, 0.351 \n",
" \n",
" \n",
" Information rate \n",
- " 1.04 ± 0.08 (28) \n",
- " 1.12 ± 0.09 (58) \n",
- " 799.00, 0.908 \n",
- " 0.03, 0.858 \n",
+ " 1.08 ± 0.08 (61) \n",
+ " 0.95 ± 0.07 (49) \n",
+ " 1321.00, 0.298 \n",
+ " 0.14, 0.413 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.28 ± 0.16 (28) \n",
- " 2.32 ± 0.09 (58) \n",
- " 745.00, 0.540 \n",
- " 0.16, 0.463 \n",
+ " 2.28 ± 0.09 (61) \n",
+ " 2.24 ± 0.11 (49) \n",
+ " 1419.00, 0.652 \n",
+ " 0.06, 0.740 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 14.95 ± 1.71 (28) \n",
- " 15.81 ± 1.38 (58) \n",
- " 779.00, 0.765 \n",
- " 0.98, 0.712 \n",
+ " 15.61 ± 1.32 (61) \n",
+ " 14.54 ± 1.29 (49) \n",
+ " 1418.00, 0.648 \n",
+ " 0.64, 0.675 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.80 ± 1.35 (28) \n",
- " 7.58 ± 0.96 (58) \n",
- " 827.00, 0.894 \n",
- " 0.10, 0.927 \n",
+ " 7.65 ± 0.96 (61) \n",
+ " 7.54 ± 1.06 (49) \n",
+ " 1487.00, 0.966 \n",
+ " 0.37, 0.789 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.18 ± 0.01 (28) \n",
- " 0.21 ± 0.01 (58) \n",
- " 641.00, 0.116 \n",
- " 0.03, 0.099 \n",
+ " 0.21 ± 0.01 (61) \n",
+ " 0.19 ± 0.01 (49) \n",
+ " 1241.00, 0.128 \n",
+ " 0.04, 0.037 \n",
" \n",
" \n",
" Specificity \n",
- " 0.43 ± 0.05 (28) \n",
- " 0.43 ± 0.03 (58) \n",
- " 749.00, 0.565 \n",
- " 0.02, 0.657 \n",
+ " 0.42 ± 0.03 (61) \n",
+ " 0.42 ± 0.03 (49) \n",
+ " 1429.00, 0.696 \n",
+ " 0.03, 0.495 \n",
" \n",
" \n",
" Speed score \n",
- " 0.10 ± 0.02 (28) \n",
- " 0.12 ± 0.01 (58) \n",
- " 617.00, 0.073 \n",
- " 0.02, 0.116 \n",
+ " 0.12 ± 0.01 (61) \n",
+ " 0.11 ± 0.01 (49) \n",
+ " 1335.00, 0.339 \n",
+ " 0.01, 0.545 \n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
- " Stimulated Baseline MWU \\\n",
- "Average rate 10.11 ± 1.51 (28) 10.01 ± 1.06 (58) 808.00, 0.974 \n",
- "Gridness 0.28 ± 0.08 (28) 0.57 ± 0.05 (58) 493.00, 0.003 \n",
- "Sparsity 0.68 ± 0.04 (28) 0.66 ± 0.02 (58) 881.00, 0.528 \n",
- "Selectivity 7.47 ± 1.63 (28) 5.53 ± 0.40 (58) 809.00, 0.982 \n",
- "Information specificity 0.20 ± 0.03 (28) 0.19 ± 0.02 (58) 812.00, 0.996 \n",
- "Max rate 45.33 ± 6.85 (28) 38.95 ± 2.48 (58) 797.00, 0.894 \n",
- "Information rate 1.04 ± 0.08 (28) 1.12 ± 0.09 (58) 799.00, 0.908 \n",
- "Interspike interval cv 2.28 ± 0.16 (28) 2.32 ± 0.09 (58) 745.00, 0.540 \n",
- "In-field mean rate 14.95 ± 1.71 (28) 15.81 ± 1.38 (58) 779.00, 0.765 \n",
- "Out-field mean rate 7.80 ± 1.35 (28) 7.58 ± 0.96 (58) 827.00, 0.894 \n",
- "Burst event ratio 0.18 ± 0.01 (28) 0.21 ± 0.01 (58) 641.00, 0.116 \n",
- "Specificity 0.43 ± 0.05 (28) 0.43 ± 0.03 (58) 749.00, 0.565 \n",
- "Speed score 0.10 ± 0.02 (28) 0.12 ± 0.01 (58) 617.00, 0.073 \n",
+ " Baseline 30 Hz MWU \\\n",
+ "Average rate 10.08 ± 1.05 (61) 9.94 ± 1.17 (49) 1491.00, 0.986 \n",
+ "Gridness 0.53 ± 0.05 (61) 0.46 ± 0.06 (49) 1342.00, 0.361 \n",
+ "Sparsity 0.67 ± 0.02 (61) 0.69 ± 0.03 (49) 1622.00, 0.445 \n",
+ "Selectivity 5.34 ± 0.38 (61) 5.21 ± 0.46 (49) 1372.00, 0.463 \n",
+ "Information specificity 0.19 ± 0.02 (61) 0.18 ± 0.03 (49) 1380.00, 0.493 \n",
+ "Max rate 37.61 ± 2.31 (61) 34.42 ± 1.99 (49) 1342.00, 0.361 \n",
+ "Information rate 1.08 ± 0.08 (61) 0.95 ± 0.07 (49) 1321.00, 0.298 \n",
+ "Interspike interval cv 2.28 ± 0.09 (61) 2.24 ± 0.11 (49) 1419.00, 0.652 \n",
+ "In-field mean rate 15.61 ± 1.32 (61) 14.54 ± 1.29 (49) 1418.00, 0.648 \n",
+ "Out-field mean rate 7.65 ± 0.96 (61) 7.54 ± 1.06 (49) 1487.00, 0.966 \n",
+ "Burst event ratio 0.21 ± 0.01 (61) 0.19 ± 0.01 (49) 1241.00, 0.128 \n",
+ "Specificity 0.42 ± 0.03 (61) 0.42 ± 0.03 (49) 1429.00, 0.696 \n",
+ "Speed score 0.12 ± 0.01 (61) 0.11 ± 0.01 (49) 1335.00, 0.339 \n",
"\n",
" PRS \n",
- "Average rate 0.07, 0.968 \n",
- "Gridness 0.46, 0.000 \n",
- "Sparsity 0.04, 0.328 \n",
- "Selectivity 0.30, 0.638 \n",
- "Information specificity 0.01, 0.588 \n",
- "Max rate 2.09, 0.451 \n",
- "Information rate 0.03, 0.858 \n",
- "Interspike interval cv 0.16, 0.463 \n",
- "In-field mean rate 0.98, 0.712 \n",
- "Out-field mean rate 0.10, 0.927 \n",
- "Burst event ratio 0.03, 0.099 \n",
- "Specificity 0.02, 0.657 \n",
- "Speed score 0.02, 0.116 "
+ "Average rate 0.24, 0.763 \n",
+ "Gridness 0.08, 0.289 \n",
+ "Sparsity 0.03, 0.466 \n",
+ "Selectivity 0.37, 0.420 \n",
+ "Information specificity 0.01, 0.725 \n",
+ "Max rate 2.37, 0.351 \n",
+ "Information rate 0.14, 0.413 \n",
+ "Interspike interval cv 0.06, 0.740 \n",
+ "In-field mean rate 0.64, 0.675 \n",
+ "Out-field mean rate 0.37, 0.789 \n",
+ "Burst event ratio 0.04, 0.037 \n",
+ "Specificity 0.03, 0.495 \n",
+ "Speed score 0.01, 0.545 "
]
},
- "execution_count": 28,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -1988,8 +1990,8 @@
"\n",
"result = pd.DataFrame()\n",
"\n",
- "result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
+ "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
@@ -2004,7 +2006,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
@@ -2028,8 +2030,8 @@
" \n",
" \n",
" \n",
- " Stimulated 30Hz \n",
- " Stimulated 11Hz \n",
+ " 11 Hz \n",
+ " 30 Hz \n",
" MWU \n",
" PRS \n",
" \n",
@@ -2037,132 +2039,132 @@
" \n",
" \n",
" Average rate \n",
- " 10.11 ± 1.51 (28) \n",
- " 9.06 ± 1.21 (32) \n",
- " 463.00, 0.830 \n",
- " 0.12, 0.978 \n",
+ " 9.28 ± 0.90 (65) \n",
+ " 9.94 ± 1.17 (49) \n",
+ " 1641.00, 0.784 \n",
+ " 0.09, 0.925 \n",
" \n",
" \n",
" Gridness \n",
- " 0.28 ± 0.08 (28) \n",
- " 0.34 ± 0.06 (32) \n",
- " 402.00, 0.500 \n",
- " 0.15, 0.330 \n",
+ " 0.42 ± 0.05 (65) \n",
+ " 0.46 ± 0.06 (49) \n",
+ " 1739.00, 0.403 \n",
+ " 0.09, 0.420 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.68 ± 0.04 (28) \n",
- " 0.67 ± 0.03 (32) \n",
- " 479.00, 0.651 \n",
- " 0.03, 0.493 \n",
+ " 0.69 ± 0.02 (65) \n",
+ " 0.69 ± 0.03 (49) \n",
+ " 1618.00, 0.886 \n",
+ " 0.01, 0.660 \n",
" \n",
" \n",
" Selectivity \n",
- " 7.47 ± 1.63 (28) \n",
- " 5.43 ± 0.47 (32) \n",
- " 449.00, 0.994 \n",
- " 0.00, 0.999 \n",
+ " 5.43 ± 0.48 (65) \n",
+ " 5.21 ± 0.46 (49) \n",
+ " 1548.00, 0.801 \n",
+ " 0.17, 0.835 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.20 ± 0.03 (28) \n",
- " 0.19 ± 0.03 (32) \n",
- " 440.00, 0.912 \n",
- " 0.01, 0.768 \n",
+ " 0.19 ± 0.03 (65) \n",
+ " 0.18 ± 0.03 (49) \n",
+ " 1569.00, 0.895 \n",
+ " 0.01, 0.783 \n",
" \n",
" \n",
" Max rate \n",
- " 45.33 ± 6.85 (28) \n",
- " 35.53 ± 2.50 (32) \n",
- " 488.00, 0.558 \n",
- " 1.22, 0.682 \n",
+ " 33.16 ± 1.79 (65) \n",
+ " 34.42 ± 1.99 (49) \n",
+ " 1681.00, 0.614 \n",
+ " 1.38, 0.740 \n",
" \n",
" \n",
" Information rate \n",
- " 1.04 ± 0.08 (28) \n",
- " 1.04 ± 0.10 (32) \n",
- " 475.00, 0.695 \n",
- " 0.02, 0.775 \n",
+ " 0.89 ± 0.06 (65) \n",
+ " 0.95 ± 0.07 (49) \n",
+ " 1701.00, 0.536 \n",
+ " 0.07, 0.480 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.28 ± 0.16 (28) \n",
- " 2.29 ± 0.12 (32) \n",
- " 411.00, 0.589 \n",
- " 0.14, 0.659 \n",
+ " 2.24 ± 0.09 (65) \n",
+ " 2.24 ± 0.11 (49) \n",
+ " 1583.00, 0.959 \n",
+ " 0.05, 0.814 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 14.95 ± 1.71 (28) \n",
- " 13.87 ± 1.42 (32) \n",
- " 473.00, 0.717 \n",
- " 1.02, 0.794 \n",
+ " 13.80 ± 1.06 (65) \n",
+ " 14.54 ± 1.29 (49) \n",
+ " 1658.00, 0.710 \n",
+ " 0.88, 0.678 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.80 ± 1.35 (28) \n",
- " 6.52 ± 1.04 (32) \n",
- " 489.00, 0.548 \n",
- " 0.17, 0.940 \n",
+ " 7.00 ± 0.80 (65) \n",
+ " 7.54 ± 1.06 (49) \n",
+ " 1631.00, 0.828 \n",
+ " 0.38, 0.923 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.18 ± 0.01 (28) \n",
- " 0.23 ± 0.01 (32) \n",
- " 273.00, 0.010 \n",
- " 0.05, 0.028 \n",
+ " 0.23 ± 0.01 (65) \n",
+ " 0.19 ± 0.01 (49) \n",
+ " 1093.00, 0.004 \n",
+ " 0.05, 0.004 \n",
" \n",
" \n",
" Specificity \n",
- " 0.43 ± 0.05 (28) \n",
- " 0.45 ± 0.04 (32) \n",
- " 400.00, 0.482 \n",
- " 0.02, 0.570 \n",
+ " 0.42 ± 0.03 (65) \n",
+ " 0.42 ± 0.03 (49) \n",
+ " 1559.00, 0.850 \n",
+ " 0.01, 0.597 \n",
" \n",
" \n",
" Speed score \n",
- " 0.10 ± 0.02 (28) \n",
- " 0.09 ± 0.01 (32) \n",
- " 446.00, 0.982 \n",
- " 0.01, 0.480 \n",
+ " 0.12 ± 0.01 (65) \n",
+ " 0.11 ± 0.01 (49) \n",
+ " 1459.00, 0.446 \n",
+ " 0.01, 0.397 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Stimulated 30Hz Stimulated 11Hz MWU \\\n",
- "Average rate 10.11 ± 1.51 (28) 9.06 ± 1.21 (32) 463.00, 0.830 \n",
- "Gridness 0.28 ± 0.08 (28) 0.34 ± 0.06 (32) 402.00, 0.500 \n",
- "Sparsity 0.68 ± 0.04 (28) 0.67 ± 0.03 (32) 479.00, 0.651 \n",
- "Selectivity 7.47 ± 1.63 (28) 5.43 ± 0.47 (32) 449.00, 0.994 \n",
- "Information specificity 0.20 ± 0.03 (28) 0.19 ± 0.03 (32) 440.00, 0.912 \n",
- "Max rate 45.33 ± 6.85 (28) 35.53 ± 2.50 (32) 488.00, 0.558 \n",
- "Information rate 1.04 ± 0.08 (28) 1.04 ± 0.10 (32) 475.00, 0.695 \n",
- "Interspike interval cv 2.28 ± 0.16 (28) 2.29 ± 0.12 (32) 411.00, 0.589 \n",
- "In-field mean rate 14.95 ± 1.71 (28) 13.87 ± 1.42 (32) 473.00, 0.717 \n",
- "Out-field mean rate 7.80 ± 1.35 (28) 6.52 ± 1.04 (32) 489.00, 0.548 \n",
- "Burst event ratio 0.18 ± 0.01 (28) 0.23 ± 0.01 (32) 273.00, 0.010 \n",
- "Specificity 0.43 ± 0.05 (28) 0.45 ± 0.04 (32) 400.00, 0.482 \n",
- "Speed score 0.10 ± 0.02 (28) 0.09 ± 0.01 (32) 446.00, 0.982 \n",
+ " 11 Hz 30 Hz MWU \\\n",
+ "Average rate 9.28 ± 0.90 (65) 9.94 ± 1.17 (49) 1641.00, 0.784 \n",
+ "Gridness 0.42 ± 0.05 (65) 0.46 ± 0.06 (49) 1739.00, 0.403 \n",
+ "Sparsity 0.69 ± 0.02 (65) 0.69 ± 0.03 (49) 1618.00, 0.886 \n",
+ "Selectivity 5.43 ± 0.48 (65) 5.21 ± 0.46 (49) 1548.00, 0.801 \n",
+ "Information specificity 0.19 ± 0.03 (65) 0.18 ± 0.03 (49) 1569.00, 0.895 \n",
+ "Max rate 33.16 ± 1.79 (65) 34.42 ± 1.99 (49) 1681.00, 0.614 \n",
+ "Information rate 0.89 ± 0.06 (65) 0.95 ± 0.07 (49) 1701.00, 0.536 \n",
+ "Interspike interval cv 2.24 ± 0.09 (65) 2.24 ± 0.11 (49) 1583.00, 0.959 \n",
+ "In-field mean rate 13.80 ± 1.06 (65) 14.54 ± 1.29 (49) 1658.00, 0.710 \n",
+ "Out-field mean rate 7.00 ± 0.80 (65) 7.54 ± 1.06 (49) 1631.00, 0.828 \n",
+ "Burst event ratio 0.23 ± 0.01 (65) 0.19 ± 0.01 (49) 1093.00, 0.004 \n",
+ "Specificity 0.42 ± 0.03 (65) 0.42 ± 0.03 (49) 1559.00, 0.850 \n",
+ "Speed score 0.12 ± 0.01 (65) 0.11 ± 0.01 (49) 1459.00, 0.446 \n",
"\n",
" PRS \n",
- "Average rate 0.12, 0.978 \n",
- "Gridness 0.15, 0.330 \n",
- "Sparsity 0.03, 0.493 \n",
- "Selectivity 0.00, 0.999 \n",
- "Information specificity 0.01, 0.768 \n",
- "Max rate 1.22, 0.682 \n",
- "Information rate 0.02, 0.775 \n",
- "Interspike interval cv 0.14, 0.659 \n",
- "In-field mean rate 1.02, 0.794 \n",
- "Out-field mean rate 0.17, 0.940 \n",
- "Burst event ratio 0.05, 0.028 \n",
- "Specificity 0.02, 0.570 \n",
- "Speed score 0.01, 0.480 "
+ "Average rate 0.09, 0.925 \n",
+ "Gridness 0.09, 0.420 \n",
+ "Sparsity 0.01, 0.660 \n",
+ "Selectivity 0.17, 0.835 \n",
+ "Information specificity 0.01, 0.783 \n",
+ "Max rate 1.38, 0.740 \n",
+ "Information rate 0.07, 0.480 \n",
+ "Interspike interval cv 0.05, 0.814 \n",
+ "In-field mean rate 0.88, 0.678 \n",
+ "Out-field mean rate 0.38, 0.923 \n",
+ "Burst event ratio 0.05, 0.004 \n",
+ "Specificity 0.01, 0.597 \n",
+ "Speed score 0.01, 0.397 "
]
},
- "execution_count": 29,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -2173,8 +2175,9 @@
"\n",
"result = pd.DataFrame()\n",
"\n",
- "result['Stimulated 30Hz'] = _stim_data[columns].agg(summarize)\n",
- "result['Stimulated 11Hz'] = _base_data[columns].agg(summarize)\n",
+ "result['11 Hz'] = _base_data[columns].agg(summarize)\n",
+ "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
+ "\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
@@ -2189,7 +2192,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
@@ -2213,8 +2216,8 @@
" \n",
" \n",
" \n",
- " Baseline i \n",
- " Baseline ii \n",
+ " Baseline I \n",
+ " Baseline II \n",
" MWU \n",
" PRS \n",
" \n",
@@ -2222,132 +2225,132 @@
" \n",
" \n",
" Average rate \n",
- " 9.65 ± 0.90 (68) \n",
- " 10.01 ± 1.06 (58) \n",
- " 1979.00, 0.975 \n",
- " 0.20, 0.935 \n",
+ " 9.82 ± 0.91 (70) \n",
+ " 10.08 ± 1.05 (61) \n",
+ " 2166.00, 0.888 \n",
+ " 0.15, 0.852 \n",
" \n",
" \n",
" Gridness \n",
- " 0.58 ± 0.04 (68) \n",
- " 0.57 ± 0.05 (58) \n",
- " 1946.00, 0.901 \n",
- " 0.04, 0.479 \n",
+ " 0.54 ± 0.05 (70) \n",
+ " 0.53 ± 0.05 (61) \n",
+ " 2158.00, 0.917 \n",
+ " 0.00, 0.983 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.65 ± 0.02 (68) \n",
- " 0.66 ± 0.02 (58) \n",
- " 1870.00, 0.619 \n",
- " 0.05, 0.253 \n",
+ " 0.65 ± 0.02 (70) \n",
+ " 0.67 ± 0.02 (61) \n",
+ " 2001.00, 0.538 \n",
+ " 0.04, 0.361 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.22 ± 0.35 (68) \n",
- " 5.53 ± 0.40 (58) \n",
- " 1833.00, 0.498 \n",
- " 0.01, 0.973 \n",
+ " 5.25 ± 0.35 (70) \n",
+ " 5.34 ± 0.38 (61) \n",
+ " 2062.00, 0.738 \n",
+ " 0.25, 0.594 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.21 ± 0.02 (68) \n",
- " 0.19 ± 0.02 (58) \n",
- " 2135.00, 0.426 \n",
- " 0.05, 0.136 \n",
+ " 0.22 ± 0.03 (70) \n",
+ " 0.19 ± 0.02 (61) \n",
+ " 2329.00, 0.372 \n",
+ " 0.05, 0.143 \n",
" \n",
" \n",
" Max rate \n",
- " 36.19 ± 1.79 (68) \n",
- " 38.95 ± 2.48 (58) \n",
- " 1824.00, 0.470 \n",
- " 0.84, 0.675 \n",
+ " 36.77 ± 1.96 (70) \n",
+ " 37.61 ± 2.31 (61) \n",
+ " 2088.00, 0.830 \n",
+ " 0.58, 0.784 \n",
" \n",
" \n",
" Information rate \n",
- " 1.21 ± 0.06 (68) \n",
- " 1.12 ± 0.09 (58) \n",
- " 2246.00, 0.181 \n",
- " 0.13, 0.169 \n",
+ " 1.22 ± 0.06 (70) \n",
+ " 1.08 ± 0.08 (61) \n",
+ " 2501.00, 0.092 \n",
+ " 0.14, 0.151 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.38 ± 0.10 (68) \n",
- " 2.32 ± 0.09 (58) \n",
- " 2055.00, 0.686 \n",
- " 0.02, 0.805 \n",
+ " 2.37 ± 0.09 (70) \n",
+ " 2.28 ± 0.09 (61) \n",
+ " 2257.00, 0.575 \n",
+ " 0.01, 0.928 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 15.27 ± 1.12 (68) \n",
- " 15.81 ± 1.38 (58) \n",
- " 1926.00, 0.824 \n",
- " 0.15, 0.931 \n",
+ " 15.52 ± 1.15 (70) \n",
+ " 15.61 ± 1.32 (61) \n",
+ " 2162.00, 0.903 \n",
+ " 0.87, 0.724 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 6.98 ± 0.76 (68) \n",
- " 7.58 ± 0.96 (58) \n",
- " 1946.00, 0.901 \n",
- " 0.62, 0.650 \n",
+ " 7.09 ± 0.77 (70) \n",
+ " 7.65 ± 0.96 (61) \n",
+ " 2115.00, 0.928 \n",
+ " 0.02, 0.986 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.23 ± 0.01 (68) \n",
- " 0.21 ± 0.01 (58) \n",
- " 2112.00, 0.495 \n",
- " 0.00, 0.743 \n",
+ " 0.23 ± 0.01 (70) \n",
+ " 0.21 ± 0.01 (61) \n",
+ " 2299.00, 0.451 \n",
+ " 0.00, 0.830 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.02 (68) \n",
- " 0.43 ± 0.03 (58) \n",
- " 2035.00, 0.760 \n",
- " 0.01, 0.834 \n",
+ " 0.45 ± 0.03 (70) \n",
+ " 0.42 ± 0.03 (61) \n",
+ " 2245.00, 0.613 \n",
+ " 0.01, 0.921 \n",
" \n",
" \n",
" Speed score \n",
- " 0.14 ± 0.01 (68) \n",
- " 0.12 ± 0.01 (58) \n",
- " 2267.00, 0.149 \n",
- " 0.05, 0.014 \n",
+ " 0.14 ± 0.01 (70) \n",
+ " 0.12 ± 0.01 (61) \n",
+ " 2423.00, 0.185 \n",
+ " 0.04, 0.042 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Baseline i Baseline ii MWU \\\n",
- "Average rate 9.65 ± 0.90 (68) 10.01 ± 1.06 (58) 1979.00, 0.975 \n",
- "Gridness 0.58 ± 0.04 (68) 0.57 ± 0.05 (58) 1946.00, 0.901 \n",
- "Sparsity 0.65 ± 0.02 (68) 0.66 ± 0.02 (58) 1870.00, 0.619 \n",
- "Selectivity 5.22 ± 0.35 (68) 5.53 ± 0.40 (58) 1833.00, 0.498 \n",
- "Information specificity 0.21 ± 0.02 (68) 0.19 ± 0.02 (58) 2135.00, 0.426 \n",
- "Max rate 36.19 ± 1.79 (68) 38.95 ± 2.48 (58) 1824.00, 0.470 \n",
- "Information rate 1.21 ± 0.06 (68) 1.12 ± 0.09 (58) 2246.00, 0.181 \n",
- "Interspike interval cv 2.38 ± 0.10 (68) 2.32 ± 0.09 (58) 2055.00, 0.686 \n",
- "In-field mean rate 15.27 ± 1.12 (68) 15.81 ± 1.38 (58) 1926.00, 0.824 \n",
- "Out-field mean rate 6.98 ± 0.76 (68) 7.58 ± 0.96 (58) 1946.00, 0.901 \n",
- "Burst event ratio 0.23 ± 0.01 (68) 0.21 ± 0.01 (58) 2112.00, 0.495 \n",
- "Specificity 0.45 ± 0.02 (68) 0.43 ± 0.03 (58) 2035.00, 0.760 \n",
- "Speed score 0.14 ± 0.01 (68) 0.12 ± 0.01 (58) 2267.00, 0.149 \n",
+ " Baseline I Baseline II MWU \\\n",
+ "Average rate 9.82 ± 0.91 (70) 10.08 ± 1.05 (61) 2166.00, 0.888 \n",
+ "Gridness 0.54 ± 0.05 (70) 0.53 ± 0.05 (61) 2158.00, 0.917 \n",
+ "Sparsity 0.65 ± 0.02 (70) 0.67 ± 0.02 (61) 2001.00, 0.538 \n",
+ "Selectivity 5.25 ± 0.35 (70) 5.34 ± 0.38 (61) 2062.00, 0.738 \n",
+ "Information specificity 0.22 ± 0.03 (70) 0.19 ± 0.02 (61) 2329.00, 0.372 \n",
+ "Max rate 36.77 ± 1.96 (70) 37.61 ± 2.31 (61) 2088.00, 0.830 \n",
+ "Information rate 1.22 ± 0.06 (70) 1.08 ± 0.08 (61) 2501.00, 0.092 \n",
+ "Interspike interval cv 2.37 ± 0.09 (70) 2.28 ± 0.09 (61) 2257.00, 0.575 \n",
+ "In-field mean rate 15.52 ± 1.15 (70) 15.61 ± 1.32 (61) 2162.00, 0.903 \n",
+ "Out-field mean rate 7.09 ± 0.77 (70) 7.65 ± 0.96 (61) 2115.00, 0.928 \n",
+ "Burst event ratio 0.23 ± 0.01 (70) 0.21 ± 0.01 (61) 2299.00, 0.451 \n",
+ "Specificity 0.45 ± 0.03 (70) 0.42 ± 0.03 (61) 2245.00, 0.613 \n",
+ "Speed score 0.14 ± 0.01 (70) 0.12 ± 0.01 (61) 2423.00, 0.185 \n",
"\n",
" PRS \n",
- "Average rate 0.20, 0.935 \n",
- "Gridness 0.04, 0.479 \n",
- "Sparsity 0.05, 0.253 \n",
- "Selectivity 0.01, 0.973 \n",
- "Information specificity 0.05, 0.136 \n",
- "Max rate 0.84, 0.675 \n",
- "Information rate 0.13, 0.169 \n",
- "Interspike interval cv 0.02, 0.805 \n",
- "In-field mean rate 0.15, 0.931 \n",
- "Out-field mean rate 0.62, 0.650 \n",
- "Burst event ratio 0.00, 0.743 \n",
- "Specificity 0.01, 0.834 \n",
- "Speed score 0.05, 0.014 "
+ "Average rate 0.15, 0.852 \n",
+ "Gridness 0.00, 0.983 \n",
+ "Sparsity 0.04, 0.361 \n",
+ "Selectivity 0.25, 0.594 \n",
+ "Information specificity 0.05, 0.143 \n",
+ "Max rate 0.58, 0.784 \n",
+ "Information rate 0.14, 0.151 \n",
+ "Interspike interval cv 0.01, 0.928 \n",
+ "In-field mean rate 0.87, 0.724 \n",
+ "Out-field mean rate 0.02, 0.986 \n",
+ "Burst event ratio 0.00, 0.830 \n",
+ "Specificity 0.01, 0.921 \n",
+ "Speed score 0.04, 0.042 "
]
},
- "execution_count": 30,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -2358,8 +2361,8 @@
"\n",
"result = pd.DataFrame()\n",
"\n",
- "result['Baseline i'] = _stim_data[columns].agg(summarize)\n",
- "result['Baseline ii'] = _base_data[columns].agg(summarize)\n",
+ "result['Baseline I'] = _stim_data[columns].agg(summarize)\n",
+ "result['Baseline II'] = _base_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
@@ -2381,7 +2384,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -2396,14 +2399,14 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"stuff = {\n",
" '': {\n",
- " 'base': gridcell_in_baseline.query('baseline'),\n",
- " 'stim': gridcell_in_baseline.query('stimulated')\n",
+ " 'base': gridcell_sessions.query('baseline'),\n",
+ " 'stim': gridcell_sessions.query('stimulated')\n",
" },\n",
" '_11': {\n",
" 'base': baseline_i,\n",
@@ -2413,6 +2416,12 @@
" 'base': baseline_ii,\n",
" 'stim': stimulated_30\n",
" }\n",
+ "}\n",
+ "\n",
+ "label = {\n",
+ " '': ['Baseline ', ' Stimulated'],\n",
+ " '_11': ['Baseline I ', ' 11 Hz'],\n",
+ " '_30': ['Baseline II ', ' 30 Hz']\n",
"}"
]
},
@@ -2425,7 +2434,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
@@ -2433,16 +2442,16 @@
"output_type": "stream",
"text": [
"\n",
- "U-test: U value 5831.0 p value 0.18862797777215656\n",
+ "U-test: U value 10345.0 p value 0.0555771740141912\n",
"_11\n",
- "U-test: U value 1309.0 p value 0.10324315446274247\n",
+ "U-test: U value 2662.0 p value 0.08875139162540739\n",
"_30\n",
- "U-test: U value 825.0 p value 0.9082875409541091\n"
+ "U-test: U value 1609.0 p value 0.49296516393290757\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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