diff --git a/actions/calculate-statistics/data/10_calculate_spatial_statistics.html b/actions/calculate-statistics/data/10_calculate_spatial_statistics.html index e6e8f1412..aa4adaf00 100644 --- a/actions/calculate-statistics/data/10_calculate_spatial_statistics.html +++ b/actions/calculate-statistics/data/10_calculate_spatial_statistics.html @@ -13173,7 +13173,7 @@ div#notebook {
@@ -13196,6 +13196,9 @@ div#notebook { bin_size = 0.02 smoothing_low = 0.03 smoothing_high = 0.06 + +stim_mask = True +baseline_duration = 600 @@ -13225,8 +13228,7 @@ div#notebook {identify_neurons = actions['identify-neurons']
-units = pd.read_csv(identify_neurons.data_path('all_non_identified_units'))
-# units = pd.read_csv(identify_neurons.data_path('units'))
+units = pd.read_csv(identify_neurons.data_path('units'))
units.head()
data_loader = dp.Data(
position_sampling_rate=position_sampling_rate,
position_low_pass_frequency=position_low_pass_frequency,
- box_size=box_size, bin_size=bin_size
+ box_size=box_size, bin_size=bin_size, stim_mask=stim_mask, baseline_duration=baseline_duration
)
data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)
@@ -13804,12 +13804,17 @@ Name: action, dtype: int64
query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'
+query = (
+ 'gridness > gridness_threshold and '
+ 'information_rate > information_rate_threshold and '
+ 'gridness > .2 and '
+ 'average_rate < 25'
+)
sessions_above_threshold = data.query(query)
-print("Number of gridcells", len(sessions_above_threshold))
+print("Number of sessions above threshold", len(sessions_above_threshold))
print("Number of animals", len(sessions_above_threshold.groupby(['entity'])))
@@ -13827,7 +13832,7 @@ Name: action, dtype: int64
@@ -13839,7 +13844,7 @@ Number of animals 4
gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]
@@ -13849,6 +13854,41 @@ Number of animals 4
print("Number of gridcells", gridcell_sessions.unit_idnum.nunique())
+print("Number of gridcell recordings", len(gridcell_sessions))
+print("Number of animals", len(gridcell_sessions.groupby(['entity'])))
+
_stim_data = gridcell_sessions.query('stimulated')
@@ -14570,7 +14610,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
- Out[49]:
+ Out[20]:
@@ -14602,94 +14642,94 @@ Number of gridcells in stimulated 30Hz ms sessions 49
Average rate
- 10.05 ± 0.65 (147)
- 9.81 ± 0.69 (124)
- 9040.00, 0.909
- 0.56, 0.717
+ 8.90 ± 0.67 (129)
+ 8.39 ± 0.60 (102)
+ 6514.00, 0.898
+ 0.38, 0.786
Gridness
- 0.54 ± 0.03 (147)
- 0.43 ± 0.03 (124)
- 7516.00, 0.013
- 0.17, 0.004
+ 0.52 ± 0.03 (129)
+ 0.44 ± 0.04 (102)
+ 5681.00, 0.075
+ 0.13, 0.065
Sparsity
- 0.66 ± 0.02 (147)
- 0.69 ± 0.02 (124)
- 10275.00, 0.071
- 0.04, 0.161
+ 0.62 ± 0.02 (129)
+ 0.66 ± 0.02 (102)
+ 7486.00, 0.072
+ 0.06, 0.124
Selectivity
- 5.35 ± 0.24 (147)
- 5.28 ± 0.32 (124)
- 8488.00, 0.330
- 0.23, 0.450
+ 5.93 ± 0.28 (129)
+ 5.98 ± 0.37 (102)
+ 6254.00, 0.520
+ 0.10, 0.803
Information specificity
- 0.21 ± 0.02 (147)
- 0.18 ± 0.02 (124)
- 7883.00, 0.056
- 0.03, 0.103
+ 0.23 ± 0.02 (129)
+ 0.22 ± 0.02 (102)
+ 5573.00, 0.046
+ 0.05, 0.031
Max rate
- 37.74 ± 1.40 (147)
- 34.65 ± 1.30 (124)
- 8165.00, 0.140
- 2.31, 0.108
+ 37.44 ± 1.44 (129)
+ 33.72 ± 1.31 (102)
+ 5851.00, 0.149
+ 3.66, 0.072
Information rate
- 1.18 ± 0.05 (147)
- 0.93 ± 0.04 (124)
- 6772.00, 0.000
- 0.18, 0.008
+ 1.25 ± 0.05 (129)
+ 0.96 ± 0.06 (102)
+ 4646.00, 0.000
+ 0.29, 0.001
Interspike interval cv
- 2.34 ± 0.06 (147)
- 2.25 ± 0.07 (124)
- 8361.00, 0.242
- 0.07, 0.500
+ 2.40 ± 0.07 (129)
+ 2.22 ± 0.08 (102)
+ 5516.00, 0.035
+ 0.14, 0.270
In-field mean rate
- 15.79 ± 0.82 (147)
- 14.46 ± 0.79 (124)
- 8526.00, 0.361
- 0.67, 0.638
+ 14.72 ± 0.82 (129)
+ 12.94 ± 0.71 (102)
+ 6026.00, 0.273
+ 0.76, 0.414
Out-field mean rate
- 7.41 ± 0.58 (147)
- 7.43 ± 0.62 (124)
- 9193.00, 0.903
- 0.88, 0.456
+ 6.35 ± 0.60 (129)
+ 6.12 ± 0.53 (102)
+ 6535.00, 0.931
+ 0.08, 0.921
Burst event ratio
- 0.22 ± 0.01 (147)
- 0.21 ± 0.01 (124)
- 8548.00, 0.379
- 0.01, 0.370
+ 0.21 ± 0.01 (129)
+ 0.20 ± 0.01 (102)
+ 5792.00, 0.119
+ 0.02, 0.071
Specificity
- 0.45 ± 0.02 (147)
- 0.42 ± 0.02 (124)
- 8221.00, 0.165
- 0.03, 0.167
+ 0.48 ± 0.02 (129)
+ 0.46 ± 0.02 (102)
+ 5962.00, 0.222
+ 0.06, 0.181
Speed score
- 0.13 ± 0.01 (147)
- 0.11 ± 0.01 (124)
- 7793.00, 0.040
- 0.02, 0.046
+ 0.14 ± 0.01 (129)
+ 0.10 ± 0.01 (102)
+ 5128.00, 0.004
+ 0.02, 0.008
@@ -14704,7 +14744,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
-In [47]:
+In [21]:
_stim_data = stimulated_11
@@ -14737,7 +14777,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
- Out[47]:
+ Out[21]:
@@ -14769,94 +14809,94 @@ Number of gridcells in stimulated 30Hz ms sessions 49
Average rate
- 9.82 ± 0.91 (70)
- 9.28 ± 0.90 (65)
- 2175.00, 0.661
- 0.18, 0.933
+ 8.96 ± 0.80 (63)
+ 8.80 ± 0.85 (58)
+ 1781.00, 0.813
+ 0.04, 0.969
Gridness
- 0.54 ± 0.05 (70)
- 0.42 ± 0.05 (65)
- 1822.00, 0.046
- 0.17, 0.052
+ 0.53 ± 0.05 (63)
+ 0.41 ± 0.05 (58)
+ 1459.00, 0.057
+ 0.21, 0.038
Sparsity
- 0.65 ± 0.02 (70)
- 0.69 ± 0.02 (65)
- 2578.00, 0.183
- 0.06, 0.147
+ 0.63 ± 0.02 (63)
+ 0.67 ± 0.03 (58)
+ 2138.00, 0.107
+ 0.07, 0.126
Selectivity
- 5.25 ± 0.35 (70)
- 5.43 ± 0.48 (65)
- 2214.00, 0.790
- 0.05, 0.961
+ 5.76 ± 0.40 (63)
+ 5.69 ± 0.50 (58)
+ 1687.00, 0.469
+ 0.00, 0.981
Information specificity
- 0.22 ± 0.03 (70)
- 0.19 ± 0.03 (65)
- 1888.00, 0.089
- 0.05, 0.020
+ 0.24 ± 0.03 (63)
+ 0.21 ± 0.03 (58)
+ 1452.00, 0.052
+ 0.06, 0.031
Max rate
- 36.77 ± 1.96 (70)
- 33.16 ± 1.79 (65)
- 1971.00, 0.181
- 3.18, 0.250
+ 37.39 ± 1.91 (63)
+ 33.11 ± 1.85 (58)
+ 1538.00, 0.134
+ 4.06, 0.128
Information rate
- 1.22 ± 0.06 (70)
- 0.89 ± 0.06 (65)
- 1431.00, 0.000
- 0.20, 0.006
+ 1.31 ± 0.08 (63)
+ 0.94 ± 0.08 (58)
+ 1143.00, 0.000
+ 0.32, 0.003
Interspike interval cv
- 2.37 ± 0.09 (70)
- 2.24 ± 0.09 (65)
- 2022.00, 0.266
- 0.12, 0.520
+ 2.39 ± 0.10 (63)
+ 2.19 ± 0.12 (58)
+ 1462.00, 0.059
+ 0.18, 0.135
In-field mean rate
- 15.52 ± 1.15 (70)
- 13.80 ± 1.06 (65)
- 2064.00, 0.354
- 0.63, 0.738
+ 14.88 ± 1.05 (63)
+ 13.27 ± 1.04 (58)
+ 1633.00, 0.315
+ 0.77, 0.683
Out-field mean rate
- 7.09 ± 0.77 (70)
- 7.00 ± 0.80 (65)
- 2236.00, 0.865
- 0.01, 0.979
+ 6.37 ± 0.67 (63)
+ 6.57 ± 0.77 (58)
+ 1795.00, 0.870
+ 0.47, 0.719
Burst event ratio
- 0.23 ± 0.01 (70)
- 0.23 ± 0.01 (65)
- 2307.00, 0.890
- 0.01, 0.732
+ 0.22 ± 0.01 (63)
+ 0.22 ± 0.01 (58)
+ 1897.00, 0.718
+ 0.00, 0.824
Specificity
- 0.45 ± 0.03 (70)
- 0.42 ± 0.03 (65)
- 2049.00, 0.321
- 0.01, 0.476
+ 0.47 ± 0.03 (63)
+ 0.44 ± 0.03 (58)
+ 1605.00, 0.250
+ 0.06, 0.398
Speed score
- 0.14 ± 0.01 (70)
- 0.12 ± 0.01 (65)
- 1939.00, 0.140
- 0.03, 0.069
+ 0.14 ± 0.01 (63)
+ 0.11 ± 0.01 (58)
+ 1378.00, 0.020
+ 0.04, 0.023
@@ -14871,7 +14911,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
-In [48]:
+In [22]:
_stim_data = stimulated_30
@@ -14903,7 +14943,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
- Out[48]:
+ Out[22]:
@@ -14935,94 +14975,94 @@ Number of gridcells in stimulated 30Hz ms sessions 49
Average rate
- 10.08 ± 1.05 (61)
- 9.94 ± 1.17 (49)
- 1491.00, 0.986
- 0.24, 0.763
+ 8.29 ± 0.87 (52)
+ 7.61 ± 0.87 (38)
+ 958.00, 0.810
+ 0.27, 0.805
Gridness
- 0.53 ± 0.05 (61)
- 0.46 ± 0.06 (49)
- 1342.00, 0.361
- 0.08, 0.289
+ 0.54 ± 0.04 (52)
+ 0.48 ± 0.06 (38)
+ 914.00, 0.548
+ 0.04, 0.608
Sparsity
- 0.67 ± 0.02 (61)
- 0.69 ± 0.03 (49)
- 1622.00, 0.445
- 0.03, 0.466
+ 0.63 ± 0.03 (52)
+ 0.64 ± 0.03 (38)
+ 1040.00, 0.674
+ 0.06, 0.401
Selectivity
- 5.34 ± 0.38 (61)
- 5.21 ± 0.46 (49)
- 1372.00, 0.463
- 0.37, 0.420
+ 5.96 ± 0.46 (52)
+ 6.42 ± 0.60 (38)
+ 1019.00, 0.803
+ 0.20, 0.850
Information specificity
- 0.19 ± 0.02 (61)
- 0.18 ± 0.03 (49)
- 1380.00, 0.493
- 0.01, 0.725
+ 0.21 ± 0.02 (52)
+ 0.22 ± 0.03 (38)
+ 950.00, 0.759
+ 0.04, 0.505
Max rate
- 37.61 ± 2.31 (61)
- 34.42 ± 1.99 (49)
- 1342.00, 0.361
- 2.37, 0.351
+ 36.27 ± 2.34 (52)
+ 33.49 ± 1.89 (38)
+ 943.00, 0.716
+ 2.90, 0.558
Information rate
- 1.08 ± 0.08 (61)
- 0.95 ± 0.07 (49)
- 1321.00, 0.298
- 0.14, 0.413
+ 1.13 ± 0.08 (52)
+ 0.98 ± 0.09 (38)
+ 827.00, 0.190
+ 0.07, 0.332
Interspike interval cv
- 2.28 ± 0.09 (61)
- 2.24 ± 0.11 (49)
- 1419.00, 0.652
- 0.06, 0.740
+ 2.37 ± 0.09 (52)
+ 2.23 ± 0.11 (38)
+ 869.00, 0.333
+ 0.17, 0.470
In-field mean rate
- 15.61 ± 1.32 (61)
- 14.54 ± 1.29 (49)
- 1418.00, 0.648
- 0.64, 0.675
+ 13.79 ± 1.12 (52)
+ 12.21 ± 0.98 (38)
+ 912.00, 0.537
+ 1.06, 0.452
Out-field mean rate
- 7.65 ± 0.96 (61)
- 7.54 ± 1.06 (49)
- 1487.00, 0.966
- 0.37, 0.789
+ 5.80 ± 0.72 (52)
+ 5.36 ± 0.73 (38)
+ 959.00, 0.816
+ 0.13, 0.916
Burst event ratio
- 0.21 ± 0.01 (61)
- 0.19 ± 0.01 (49)
- 1241.00, 0.128
- 0.04, 0.037
+ 0.20 ± 0.01 (52)
+ 0.16 ± 0.01 (38)
+ 676.00, 0.011
+ 0.05, 0.007
Specificity
- 0.42 ± 0.03 (61)
- 0.42 ± 0.03 (49)
- 1429.00, 0.696
- 0.03, 0.495
+ 0.47 ± 0.03 (52)
+ 0.48 ± 0.04 (38)
+ 976.00, 0.925
+ 0.00, 0.985
Speed score
- 0.12 ± 0.01 (61)
- 0.11 ± 0.01 (49)
- 1335.00, 0.339
- 0.01, 0.545
+ 0.12 ± 0.01 (52)
+ 0.11 ± 0.01 (38)
+ 784.00, 0.096
+ 0.01, 0.241
@@ -15037,7 +15077,173 @@ Number of gridcells in stimulated 30Hz ms sessions 49
-In [45]:
+In [23]:
+
+
+_stim_data = stimulated_30
+_base_data = baseline_i
+
+result = pd.DataFrame()
+
+result['Baseline I'] = _base_data[columns].agg(summarize)
+result['30 Hz'] = _stim_data[columns].agg(summarize)
+
+result.index = map(rename, result.index)
+
+result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))
+result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))
+
+
+result.to_latex(output_path / "statistics" / "statistics_b_i_30.tex")
+result.to_latex(output_path / "statistics" / "statistics_b_i_30.csv")
+result
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+In [ ]:
_stim_data = stimulated_30
@@ -15064,147 +15270,10 @@ Number of gridcells in stimulated 30Hz ms sessions 49
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@@ -15230,143 +15299,6 @@ Number of gridcells in stimulated 30Hz ms sessions 49
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%matplotlib inline
@@ -15396,10 +15328,13 @@ Number of gridcells in stimulated 30Hz ms sessions 49
-In [51]:
+In [ ]:
-stuff = {
+# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']
+# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']
+
+stuff = {
'': {
'base': gridcell_sessions.query('baseline'),
'stim': gridcell_sessions.query('stimulated')
@@ -15419,6 +15354,12 @@ Number of gridcells in stimulated 30Hz ms sessions 49
'_11': ['Baseline I ', ' 11 Hz'],
'_30': ['Baseline II ', ' 30 Hz']
}
+
+colors = {
+ '': None,
+ '_11': ['#1b9e77', '#d95f02'],
+ '_30': ['#7570b3', '#e7298a']
+}
@@ -15435,7 +15376,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
-In [67]:
+In [ ]:
for key, data in stuff.items():
@@ -15443,7 +15384,7 @@ Number of gridcells in stimulated 30Hz ms sessions 49
stimulated = data['stim']['information_specificity'].to_numpy()
print(key)
plt.figure()
- violinplot(baseline, stimulated, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Spatial information specificity")
plt.ylabel("bits/spike")
plt.ylim(-0.2, 1.6)
@@ -15456,78 +15397,10 @@ Number of gridcells in stimulated 30Hz ms sessions 49
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@@ -15535,7 +15408,7 @@ U-test: U value 1609.0 p value 0.49296516393290757
stimulated = data['stim']['information_rate'].to_numpy()
print(key)
plt.figure()
- violinplot(baseline, stimulated, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Spatial information")
plt.ylabel("bits/s")
plt.ylim(-0.2, 4)
@@ -15548,85 +15421,17 @@ U-test: U value 1609.0 p value 0.49296516393290757
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for key, data in stuff.items():
baseline = data['base']['specificity'].to_numpy()
stimulated = data['stim']['specificity'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Spatial specificity")
plt.ylabel("")
plt.ylim(-0.02, 1.25)
@@ -15638,82 +15443,17 @@ U-test: U value 1668.0 p value 0.29814082297055944
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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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Average rate")
plt.ylabel("spikes/s")
plt.ylim(-0.2, 40)
@@ -15726,82 +15466,17 @@ U-test: U value 1560.0 p value 0.6958619307501573
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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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Max rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.2, 45)
@@ -15814,82 +15489,17 @@ U-test: U value 1498.0 p value 0.985605256484472
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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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("ISI CV")
plt.ylabel("Coefficient of variation")
# plt.ylim(0.9, 5)
@@ -15902,82 +15512,17 @@ U-test: U value 1647.0 p value 0.3606465475361048
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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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("In-field rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.1, 18)
@@ -15990,82 +15535,17 @@ U-test: U value 1570.0 p value 0.6519514527439945
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+In [ ]:
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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Out-of-field rate")
plt.ylabel("spikes/s")
# plt.ylim(-0.2, 8)
@@ -16078,82 +15558,17 @@ U-test: U value 1571.0 p value 0.6476229630232442
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-In [59]:
+In [ ]:
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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Bursting ratio")
plt.ylabel("")
# plt.ylim(-0.02, 0.60)
@@ -16166,82 +15581,17 @@ U-test: U value 1502.0 p value 0.9664203618429744
-
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-In [60]:
+In [ ]:
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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Mean rate of max field")
plt.ylabel("(spikes/s)")
# plt.ylim(-0.5,25)
@@ -16254,82 +15604,17 @@ U-test: U value 1748.0 p value 0.12812146204516903
-
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-In [61]:
+In [ ]:
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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("ratio of spikes per burst")
plt.ylabel("")
# plt.ylim(-0.03,0.9)
@@ -16342,82 +15627,17 @@ U-test: U value 1693.0 p value 0.23374014039208268
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-In [62]:
+In [ ]:
for key, data in stuff.items():
baseline = data['base']['gridness'].to_numpy()
stimulated = data['stim']['gridness'].to_numpy()
plt.figure()
- violinplot(baseline, stimulated, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Gridness")
plt.ylabel("Gridness")
plt.ylim(-0.6, 1.5)
@@ -16430,82 +15650,17 @@ U-test: U value 1775.0 p value 0.09219506786209755
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-In [63]:
+In [ ]:
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, xticks=label[key])
+ violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
plt.title("Speed score")
plt.ylabel("Speed score")
# plt.ylim(-0.1, 0.5)
@@ -16518,75 +15673,10 @@ U-test: U value 1647.0 p value 0.3606465475361048
-
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-In [39]:
+In [ ]:
# fig, (ax1, ax2) = plt.subplots(2,1, figsize=(6,6), sharey=True)
@@ -16618,7 +15708,7 @@ U-test: U value 1654.0 p value 0.338955005854493
-In [40]:
+In [ ]:
action = project.require_action("comparisons-gridcells")
@@ -16631,7 +15721,7 @@ U-test: U value 1654.0 p value 0.338955005854493
-In [41]:
+In [ ]:
copy_tree(output_path, str(action.data_path()))
@@ -16641,111 +15731,10 @@ U-test: U value 1654.0 p value 0.338955005854493
-
-
-
-
-
-
- Out[41]:
-
-
-
-
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-In [42]:
+In [ ]:
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 1f0f48309..5268c410b 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": [
- "19:21:26 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+ "12:05:23 [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",
@@ -135,16 +135,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.397921 \n",
- " 0.676486 \n",
- " -0.459487 \n",
- " 0.078474 \n",
- " 0.965845 \n",
- " 0.309723 \n",
- " 5.788704 \n",
- " 0.043321 \n",
- " 0.624971 \n",
- " 22.067900 \n",
+ " 0.398230 \n",
+ " 0.678064 \n",
+ " -0.466923 \n",
+ " 0.029328 \n",
+ " 1.009215 \n",
+ " 0.317256 \n",
+ " 5.438033 \n",
+ " 0.040874 \n",
+ " 0.628784 \n",
+ " 20.224859 \n",
" \n",
" \n",
" 1 \n",
@@ -159,16 +159,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.146481 \n",
- " 0.277121 \n",
- " -0.615405 \n",
- " 0.311180 \n",
- " 0.191375 \n",
- " 0.032266 \n",
- " 1.821598 \n",
- " 0.014624 \n",
- " 0.753333 \n",
- " 0.000000 \n",
+ " 0.138014 \n",
+ " 0.263173 \n",
+ " -0.666792 \n",
+ " 0.308146 \n",
+ " 0.192524 \n",
+ " 0.033447 \n",
+ " 1.951740 \n",
+ " 0.017289 \n",
+ " 0.789388 \n",
+ " 27.897271 \n",
" \n",
" \n",
" 2 \n",
@@ -183,16 +183,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.373466 \n",
- " 0.658748 \n",
- " -0.527711 \n",
- " 0.131660 \n",
- " 3.833587 \n",
- " 0.336590 \n",
- " 4.407614 \n",
- " 0.121115 \n",
- " 0.542877 \n",
- " 27.758541 \n",
+ " 0.373986 \n",
+ " 0.659259 \n",
+ " -0.572566 \n",
+ " 0.143252 \n",
+ " 4.745836 \n",
+ " 0.393704 \n",
+ " 4.439721 \n",
+ " 0.124731 \n",
+ " 0.555402 \n",
+ " 28.810794 \n",
" \n",
" \n",
" 3 \n",
@@ -207,16 +207,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.097464 \n",
- " 0.196189 \n",
- " -0.641543 \n",
- " 0.274989 \n",
- " 0.153740 \n",
- " 0.068626 \n",
- " 6.128601 \n",
- " 0.099223 \n",
- " 0.484916 \n",
- " 11.309932 \n",
+ " 0.087413 \n",
+ " 0.179245 \n",
+ " -0.437492 \n",
+ " 0.268948 \n",
+ " 0.157394 \n",
+ " 0.073553 \n",
+ " 6.215195 \n",
+ " 0.101911 \n",
+ " 0.492250 \n",
+ " 9.462322 \n",
" \n",
" \n",
" 4 \n",
@@ -231,15 +231,15 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.248036 \n",
- " 0.461250 \n",
- " -0.085292 \n",
- " 0.198676 \n",
- " 0.526720 \n",
- " 0.033667 \n",
- " 1.602362 \n",
- " 0.051825 \n",
- " 0.646571 \n",
+ " 0.248771 \n",
+ " 0.463596 \n",
+ " -0.085938 \n",
+ " 0.218744 \n",
+ " 0.519153 \n",
+ " 0.032683 \n",
+ " 1.531481 \n",
+ " 0.053810 \n",
+ " 0.559905 \n",
" 0.000000 \n",
" \n",
" \n",
@@ -256,31 +256,31 @@
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... burst_event_ratio \\\n",
- "0 NaN False baseline ii ... 0.397921 \n",
- "1 NaN False baseline ii ... 0.146481 \n",
- "2 NaN False baseline ii ... 0.373466 \n",
- "3 NaN False baseline ii ... 0.097464 \n",
- "4 NaN False baseline ii ... 0.248036 \n",
+ "0 NaN False baseline ii ... 0.398230 \n",
+ "1 NaN False baseline ii ... 0.138014 \n",
+ "2 NaN False baseline ii ... 0.373986 \n",
+ "3 NaN False baseline ii ... 0.087413 \n",
+ "4 NaN False baseline ii ... 0.248771 \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",
+ "0 0.678064 -0.466923 0.029328 1.009215 \n",
+ "1 0.263173 -0.666792 0.308146 0.192524 \n",
+ "2 0.659259 -0.572566 0.143252 4.745836 \n",
+ "3 0.179245 -0.437492 0.268948 0.157394 \n",
+ "4 0.463596 -0.085938 0.218744 0.519153 \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",
+ "0 0.317256 5.438033 0.040874 0.628784 \n",
+ "1 0.033447 1.951740 0.017289 0.789388 \n",
+ "2 0.393704 4.439721 0.124731 0.555402 \n",
+ "3 0.073553 6.215195 0.101911 0.492250 \n",
+ "4 0.032683 1.531481 0.053810 0.559905 \n",
"\n",
" orientation \n",
- "0 22.067900 \n",
- "1 0.000000 \n",
- "2 27.758541 \n",
- "3 11.309932 \n",
+ "0 20.224859 \n",
+ "1 27.897271 \n",
+ "2 28.810794 \n",
+ "3 9.462322 \n",
"4 0.000000 \n",
"\n",
"[5 rows x 39 columns]"
@@ -494,16 +494,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.043321 \n",
- " 0.624971 \n",
- " 22.067900 \n",
+ " 0.040874 \n",
+ " 0.628784 \n",
+ " 20.224859 \n",
" 0.332548 \n",
" 0.229073 \n",
" 6.029431 \n",
" 0.205362 \n",
" 1.115825 \n",
" 0.066736 \n",
- " 0.445206 \n",
+ " 0.451741 \n",
" \n",
" \n",
" 1 \n",
@@ -518,16 +518,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.014624 \n",
- " 0.753333 \n",
- " 0.000000 \n",
+ " 0.017289 \n",
+ " 0.789388 \n",
+ " 27.897271 \n",
" 0.354830 \n",
" 0.089333 \n",
" 6.120055 \n",
" 0.073566 \n",
" 0.223237 \n",
" 0.052594 \n",
- " 0.097485 \n",
+ " 0.098517 \n",
" \n",
" \n",
" 2 \n",
@@ -542,16 +542,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.121115 \n",
- " 0.542877 \n",
- " 27.758541 \n",
+ " 0.124731 \n",
+ " 0.555402 \n",
+ " 28.810794 \n",
" 0.264610 \n",
" -0.121081 \n",
" 5.759406 \n",
" 0.150827 \n",
" 4.964984 \n",
" 0.027120 \n",
- " 0.393687 \n",
+ " 0.400770 \n",
" \n",
" \n",
" 3 \n",
@@ -566,16 +566,16 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.099223 \n",
- " 0.484916 \n",
- " 11.309932 \n",
+ " 0.101911 \n",
+ " 0.492250 \n",
+ " 9.462322 \n",
" 0.344280 \n",
" 0.215829 \n",
" 6.033364 \n",
" 0.110495 \n",
" 0.239996 \n",
" 0.054074 \n",
- " 0.262612 \n",
+ " 0.269461 \n",
" \n",
" \n",
" 4 \n",
@@ -590,8 +590,8 @@
" False \n",
" baseline ii \n",
" ... \n",
- " 0.051825 \n",
- " 0.646571 \n",
+ " 0.053810 \n",
+ " 0.559905 \n",
" 0.000000 \n",
" 0.342799 \n",
" 0.218967 \n",
@@ -599,7 +599,7 @@
" 0.054762 \n",
" 0.524990 \n",
" 0.144702 \n",
- " 0.133677 \n",
+ " 0.133410 \n",
" \n",
" \n",
"\n",
@@ -615,17 +615,17 @@
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... head_mean_vec_len spacing \\\n",
- "0 NaN False baseline ii ... 0.043321 0.624971 \n",
- "1 NaN False baseline ii ... 0.014624 0.753333 \n",
- "2 NaN False baseline ii ... 0.121115 0.542877 \n",
- "3 NaN False baseline ii ... 0.099223 0.484916 \n",
- "4 NaN False baseline ii ... 0.051825 0.646571 \n",
+ "0 NaN False baseline ii ... 0.040874 0.628784 \n",
+ "1 NaN False baseline ii ... 0.017289 0.789388 \n",
+ "2 NaN False baseline ii ... 0.124731 0.555402 \n",
+ "3 NaN False baseline ii ... 0.101911 0.492250 \n",
+ "4 NaN False baseline ii ... 0.053810 0.559905 \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",
+ "0 20.224859 0.332548 0.229073 \n",
+ "1 27.897271 0.354830 0.089333 \n",
+ "2 28.810794 0.264610 -0.121081 \n",
+ "3 9.462322 0.344280 0.215829 \n",
"4 0.000000 0.342799 0.218967 \n",
"\n",
" head_mean_ang_threshold head_mean_vec_len_threshold \\\n",
@@ -636,11 +636,11 @@
"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",
+ "0 1.115825 0.066736 0.451741 \n",
+ "1 0.223237 0.052594 0.098517 \n",
+ "2 4.964984 0.027120 0.400770 \n",
+ "3 0.239996 0.054074 0.269461 \n",
+ "4 0.524990 0.144702 0.133410 \n",
"\n",
"[5 rows x 46 columns]"
]
@@ -691,7 +691,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -707,32 +707,58 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of gridcells 225\n",
+ "Number of sessions above threshold 194\n",
"Number of animals 4\n"
]
}
],
"source": [
- "query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
+ "query = (\n",
+ " 'gridness > gridness_threshold and '\n",
+ " 'information_rate > information_rate_threshold and '\n",
+ " 'gridness > .2 and '\n",
+ " 'average_rate < 25'\n",
+ ")\n",
"sessions_above_threshold = data.query(query)\n",
- "print(\"Number of gridcells\", len(sessions_above_threshold))\n",
+ "print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of gridcells 139\n",
+ "Number of gridcell recordings 231\n",
+ "Number of animals 4\n"
+ ]
+ }
+ ],
"source": [
- "gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
+ "print(\"Number of gridcells\", gridcell_sessions.unit_idnum.nunique())\n",
+ "print(\"Number of gridcell recordings\", len(gridcell_sessions))\n",
+ "print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
]
},
{
@@ -744,10 +770,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "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"
+ "Number of gridcells in baseline i sessions 66\n",
+ "Number of gridcells in stimulated 11Hz ms sessions 61\n",
+ "Number of gridcells in baseline ii sessions 56\n",
+ "Number of gridcells in stimulated 30Hz ms sessions 40\n"
]
}
],
@@ -793,10 +819,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "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"
+ "Number of gridcells in baseline i sessions 63\n",
+ "Number of gridcells in stimulated 11Hz ms sessions 58\n",
+ "Number of gridcells in baseline ii sessions 52\n",
+ "Number of gridcells in stimulated 30Hz ms sessions 38\n"
]
}
],
@@ -889,35 +915,35 @@
" \n",
" \n",
" False \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",
+ " 8.904501 \n",
+ " 0.521371 \n",
+ " 0.618384 \n",
+ " 5.934539 \n",
+ " 0.234632 \n",
+ " 37.437808 \n",
+ " 1.246546 \n",
+ " 2.404647 \n",
+ " 14.717635 \n",
+ " 6.346875 \n",
+ " 0.211840 \n",
+ " 0.478775 \n",
+ " 0.135495 \n",
" \n",
" \n",
" True \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",
+ " 8.392252 \n",
+ " 0.440296 \n",
+ " 0.655698 \n",
+ " 5.977408 \n",
+ " 0.215736 \n",
+ " 33.716478 \n",
+ " 0.964787 \n",
+ " 2.223636 \n",
+ " 12.936021 \n",
+ " 6.122228 \n",
+ " 0.197264 \n",
+ " 0.455878 \n",
+ " 0.104697 \n",
" \n",
" \n",
"\n",
@@ -926,23 +952,23 @@
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
- "False 10.046219 0.537204 0.656641 5.347833 \n",
- "True 9.814609 0.433530 0.692547 5.280295 \n",
+ "False 8.904501 0.521371 0.618384 5.934539 \n",
+ "True 8.392252 0.440296 0.655698 5.977408 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
- "False 0.205817 37.735779 1.175931 \n",
- "True 0.182564 34.650917 0.933478 \n",
+ "False 0.234632 37.437808 1.246546 \n",
+ "True 0.215736 33.716478 0.964787 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
- "False 2.344483 15.790391 7.405761 \n",
- "True 2.247505 14.455320 7.429762 \n",
+ "False 2.404647 14.717635 6.346875 \n",
+ "True 2.223636 12.936021 6.122228 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
- "False 0.219892 0.445701 0.132422 \n",
- "True 0.213281 0.419822 0.111848 "
+ "False 0.211840 0.478775 0.135495 \n",
+ "True 0.197264 0.455878 0.104697 "
]
},
"execution_count": 16,
@@ -998,131 +1024,131 @@
" \n",
" \n",
" count \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",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
+ " 129.000000 \n",
" \n",
" \n",
" mean \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",
+ " 8.904501 \n",
+ " 0.521371 \n",
+ " 0.618384 \n",
+ " 5.934539 \n",
+ " 0.234632 \n",
+ " 37.437808 \n",
+ " 1.246546 \n",
+ " 2.404647 \n",
+ " 14.717635 \n",
+ " 6.346875 \n",
+ " 0.211840 \n",
+ " 0.478775 \n",
+ " 0.135495 \n",
" \n",
" \n",
" std \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",
+ " 7.605598 \n",
+ " 0.337607 \n",
+ " 0.187934 \n",
+ " 3.217366 \n",
+ " 0.200726 \n",
+ " 16.300117 \n",
+ " 0.605971 \n",
+ " 0.756407 \n",
+ " 9.267522 \n",
+ " 6.805499 \n",
+ " 0.080143 \n",
+ " 0.209531 \n",
+ " 0.072831 \n",
" \n",
" \n",
" min \n",
- " 0.516375 \n",
- " -0.599569 \n",
- " 0.220235 \n",
- " 1.762785 \n",
- " 0.005947 \n",
- " 3.013150 \n",
- " 0.102101 \n",
- " 1.067244 \n",
- " 0.993877 \n",
- " 0.185332 \n",
- " 0.027228 \n",
- " 0.072063 \n",
- " -0.023795 \n",
+ " 0.478349 \n",
+ " -0.684924 \n",
+ " 0.200066 \n",
+ " 1.533216 \n",
+ " 0.007807 \n",
+ " 3.346027 \n",
+ " 0.117638 \n",
+ " 1.304387 \n",
+ " 0.924066 \n",
+ " 0.159076 \n",
+ " 0.025000 \n",
+ " 0.071681 \n",
+ " -0.025629 \n",
" \n",
" \n",
" 25% \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",
+ " 3.518392 \n",
+ " 0.316326 \n",
+ " 0.437499 \n",
+ " 3.729863 \n",
+ " 0.093252 \n",
+ " 26.948843 \n",
+ " 0.786753 \n",
+ " 1.872991 \n",
+ " 7.701156 \n",
+ " 1.669844 \n",
+ " 0.160795 \n",
+ " 0.310822 \n",
+ " 0.084280 \n",
" \n",
" \n",
" 50% \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",
+ " 6.456882 \n",
+ " 0.529243 \n",
+ " 0.642167 \n",
+ " 4.794970 \n",
+ " 0.180286 \n",
+ " 35.064991 \n",
+ " 1.156087 \n",
+ " 2.221185 \n",
+ " 12.212289 \n",
+ " 4.314913 \n",
+ " 0.210240 \n",
+ " 0.436340 \n",
+ " 0.128603 \n",
" \n",
" \n",
" 75% \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",
+ " 12.721755 \n",
+ " 0.783682 \n",
+ " 0.758097 \n",
+ " 7.439464 \n",
+ " 0.312487 \n",
+ " 44.324873 \n",
+ " 1.592948 \n",
+ " 2.770624 \n",
+ " 20.974026 \n",
+ " 9.121505 \n",
+ " 0.267568 \n",
+ " 0.624834 \n",
+ " 0.188948 \n",
" \n",
" \n",
" max \n",
- " 35.560173 \n",
- " 1.174288 \n",
- " 0.980148 \n",
- " 17.011330 \n",
- " 1.202862 \n",
- " 90.839266 \n",
- " 3.540663 \n",
- " 5.240845 \n",
- " 45.349506 \n",
- " 32.997789 \n",
- " 0.400014 \n",
- " 0.975050 \n",
- " 0.333463 \n",
+ " 59.365312 \n",
+ " 1.148979 \n",
+ " 0.976157 \n",
+ " 18.975875 \n",
+ " 1.243307 \n",
+ " 90.160158 \n",
+ " 3.456796 \n",
+ " 5.671362 \n",
+ " 66.350754 \n",
+ " 56.255544 \n",
+ " 0.393306 \n",
+ " 1.066391 \n",
+ " 0.297548 \n",
" \n",
" \n",
"\n",
@@ -1130,44 +1156,44 @@
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\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",
+ "count 129.000000 129.000000 129.000000 129.000000 \n",
+ "mean 8.904501 0.521371 0.618384 5.934539 \n",
+ "std 7.605598 0.337607 0.187934 3.217366 \n",
+ "min 0.478349 -0.684924 0.200066 1.533216 \n",
+ "25% 3.518392 0.316326 0.437499 3.729863 \n",
+ "50% 6.456882 0.529243 0.642167 4.794970 \n",
+ "75% 12.721755 0.783682 0.758097 7.439464 \n",
+ "max 59.365312 1.148979 0.976157 18.975875 \n",
"\n",
" information_specificity max_rate information_rate \\\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",
+ "count 129.000000 129.000000 129.000000 \n",
+ "mean 0.234632 37.437808 1.246546 \n",
+ "std 0.200726 16.300117 0.605971 \n",
+ "min 0.007807 3.346027 0.117638 \n",
+ "25% 0.093252 26.948843 0.786753 \n",
+ "50% 0.180286 35.064991 1.156087 \n",
+ "75% 0.312487 44.324873 1.592948 \n",
+ "max 1.243307 90.160158 3.456796 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\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",
+ "count 129.000000 129.000000 129.000000 \n",
+ "mean 2.404647 14.717635 6.346875 \n",
+ "std 0.756407 9.267522 6.805499 \n",
+ "min 1.304387 0.924066 0.159076 \n",
+ "25% 1.872991 7.701156 1.669844 \n",
+ "50% 2.221185 12.212289 4.314913 \n",
+ "75% 2.770624 20.974026 9.121505 \n",
+ "max 5.671362 66.350754 56.255544 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
- "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 "
+ "count 129.000000 129.000000 129.000000 \n",
+ "mean 0.211840 0.478775 0.135495 \n",
+ "std 0.080143 0.209531 0.072831 \n",
+ "min 0.025000 0.071681 -0.025629 \n",
+ "25% 0.160795 0.310822 0.084280 \n",
+ "50% 0.210240 0.436340 0.128603 \n",
+ "75% 0.267568 0.624834 0.188948 \n",
+ "max 0.393306 1.066391 0.297548 "
]
},
"execution_count": 17,
@@ -1223,131 +1249,131 @@
" \n",
" \n",
" count \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",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
+ " 102.000000 \n",
" \n",
" \n",
" mean \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",
+ " 8.392252 \n",
+ " 0.440296 \n",
+ " 0.655698 \n",
+ " 5.977408 \n",
+ " 0.215736 \n",
+ " 33.716478 \n",
+ " 0.964787 \n",
+ " 2.223636 \n",
+ " 12.936021 \n",
+ " 6.122228 \n",
+ " 0.197264 \n",
+ " 0.455878 \n",
+ " 0.104697 \n",
" \n",
" \n",
" std \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",
+ " 6.057001 \n",
+ " 0.357038 \n",
+ " 0.211704 \n",
+ " 3.702400 \n",
+ " 0.235916 \n",
+ " 13.249312 \n",
+ " 0.572972 \n",
+ " 0.819734 \n",
+ " 7.211895 \n",
+ " 5.366332 \n",
+ " 0.082164 \n",
+ " 0.236777 \n",
+ " 0.081989 \n",
" \n",
" \n",
" min \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",
+ " 0.198337 \n",
+ " -0.516914 \n",
+ " 0.172684 \n",
+ " 1.930026 \n",
+ " 0.013088 \n",
+ " 2.846281 \n",
+ " 0.063173 \n",
+ " 1.110672 \n",
+ " 0.524639 \n",
+ " 0.099060 \n",
+ " 0.008475 \n",
+ " 0.097718 \n",
+ " -0.138128 \n",
" \n",
" \n",
" 25% \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",
+ " 3.579184 \n",
+ " 0.265949 \n",
+ " 0.458493 \n",
+ " 3.044303 \n",
+ " 0.066656 \n",
+ " 25.555110 \n",
+ " 0.564279 \n",
+ " 1.620472 \n",
+ " 7.555760 \n",
+ " 1.733624 \n",
+ " 0.146755 \n",
+ " 0.248057 \n",
+ " 0.056903 \n",
" \n",
" \n",
" 50% \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",
+ " 6.838561 \n",
+ " 0.399053 \n",
+ " 0.699561 \n",
+ " 4.891855 \n",
+ " 0.128562 \n",
+ " 31.402558 \n",
+ " 0.862413 \n",
+ " 2.084020 \n",
+ " 11.451560 \n",
+ " 4.234871 \n",
+ " 0.192948 \n",
+ " 0.376143 \n",
+ " 0.106314 \n",
" \n",
" \n",
" 75% \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",
+ " 11.934599 \n",
+ " 0.749561 \n",
+ " 0.842332 \n",
+ " 8.001587 \n",
+ " 0.300713 \n",
+ " 42.334786 \n",
+ " 1.190324 \n",
+ " 2.673991 \n",
+ " 17.335356 \n",
+ " 8.583415 \n",
+ " 0.247405 \n",
+ " 0.684623 \n",
+ " 0.149313 \n",
" \n",
" \n",
" max \n",
- " 34.844930 \n",
- " 1.230658 \n",
- " 0.983263 \n",
- " 25.599598 \n",
- " 1.296616 \n",
- " 76.146357 \n",
- " 2.918984 \n",
- " 5.324055 \n",
- " 42.803943 \n",
- " 31.519482 \n",
- " 0.406678 \n",
- " 1.077313 \n",
- " 0.349283 \n",
+ " 24.858738 \n",
+ " 1.155123 \n",
+ " 0.967003 \n",
+ " 19.911477 \n",
+ " 1.359164 \n",
+ " 65.990793 \n",
+ " 3.182285 \n",
+ " 6.526960 \n",
+ " 34.489913 \n",
+ " 21.696265 \n",
+ " 0.393037 \n",
+ " 1.091064 \n",
+ " 0.390079 \n",
" \n",
" \n",
"\n",
@@ -1355,44 +1381,44 @@
],
"text/plain": [
" 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",
+ "count 102.000000 102.000000 102.000000 102.000000 \n",
+ "mean 8.392252 0.440296 0.655698 5.977408 \n",
+ "std 6.057001 0.357038 0.211704 3.702400 \n",
+ "min 0.198337 -0.516914 0.172684 1.930026 \n",
+ "25% 3.579184 0.265949 0.458493 3.044303 \n",
+ "50% 6.838561 0.399053 0.699561 4.891855 \n",
+ "75% 11.934599 0.749561 0.842332 8.001587 \n",
+ "max 24.858738 1.155123 0.967003 19.911477 \n",
"\n",
" information_specificity max_rate information_rate \\\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",
+ "count 102.000000 102.000000 102.000000 \n",
+ "mean 0.215736 33.716478 0.964787 \n",
+ "std 0.235916 13.249312 0.572972 \n",
+ "min 0.013088 2.846281 0.063173 \n",
+ "25% 0.066656 25.555110 0.564279 \n",
+ "50% 0.128562 31.402558 0.862413 \n",
+ "75% 0.300713 42.334786 1.190324 \n",
+ "max 1.359164 65.990793 3.182285 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\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",
+ "count 102.000000 102.000000 102.000000 \n",
+ "mean 2.223636 12.936021 6.122228 \n",
+ "std 0.819734 7.211895 5.366332 \n",
+ "min 1.110672 0.524639 0.099060 \n",
+ "25% 1.620472 7.555760 1.733624 \n",
+ "50% 2.084020 11.451560 4.234871 \n",
+ "75% 2.673991 17.335356 8.583415 \n",
+ "max 6.526960 34.489913 21.696265 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
- "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 "
+ "count 102.000000 102.000000 102.000000 \n",
+ "mean 0.197264 0.455878 0.104697 \n",
+ "std 0.082164 0.236777 0.081989 \n",
+ "min 0.008475 0.097718 -0.138128 \n",
+ "25% 0.146755 0.248057 0.056903 \n",
+ "50% 0.192948 0.376143 0.106314 \n",
+ "75% 0.247405 0.684623 0.149313 \n",
+ "max 0.393037 1.091064 0.390079 "
]
},
"execution_count": 18,
@@ -1450,7 +1476,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -1483,94 +1509,94 @@
" \n",
" \n",
" Average rate \n",
- " 10.05 ± 0.65 (147) \n",
- " 9.81 ± 0.69 (124) \n",
- " 9040.00, 0.909 \n",
- " 0.56, 0.717 \n",
+ " 8.90 ± 0.67 (129) \n",
+ " 8.39 ± 0.60 (102) \n",
+ " 6514.00, 0.898 \n",
+ " 0.38, 0.786 \n",
" \n",
" \n",
" Gridness \n",
- " 0.54 ± 0.03 (147) \n",
- " 0.43 ± 0.03 (124) \n",
- " 7516.00, 0.013 \n",
- " 0.17, 0.004 \n",
+ " 0.52 ± 0.03 (129) \n",
+ " 0.44 ± 0.04 (102) \n",
+ " 5681.00, 0.075 \n",
+ " 0.13, 0.065 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.66 ± 0.02 (147) \n",
- " 0.69 ± 0.02 (124) \n",
- " 10275.00, 0.071 \n",
- " 0.04, 0.161 \n",
+ " 0.62 ± 0.02 (129) \n",
+ " 0.66 ± 0.02 (102) \n",
+ " 7486.00, 0.072 \n",
+ " 0.06, 0.124 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.35 ± 0.24 (147) \n",
- " 5.28 ± 0.32 (124) \n",
- " 8488.00, 0.330 \n",
- " 0.23, 0.450 \n",
+ " 5.93 ± 0.28 (129) \n",
+ " 5.98 ± 0.37 (102) \n",
+ " 6254.00, 0.520 \n",
+ " 0.10, 0.803 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.21 ± 0.02 (147) \n",
- " 0.18 ± 0.02 (124) \n",
- " 7883.00, 0.056 \n",
- " 0.03, 0.103 \n",
+ " 0.23 ± 0.02 (129) \n",
+ " 0.22 ± 0.02 (102) \n",
+ " 5573.00, 0.046 \n",
+ " 0.05, 0.031 \n",
" \n",
" \n",
" Max rate \n",
- " 37.74 ± 1.40 (147) \n",
- " 34.65 ± 1.30 (124) \n",
- " 8165.00, 0.140 \n",
- " 2.31, 0.108 \n",
+ " 37.44 ± 1.44 (129) \n",
+ " 33.72 ± 1.31 (102) \n",
+ " 5851.00, 0.149 \n",
+ " 3.66, 0.072 \n",
" \n",
" \n",
" Information rate \n",
- " 1.18 ± 0.05 (147) \n",
- " 0.93 ± 0.04 (124) \n",
- " 6772.00, 0.000 \n",
- " 0.18, 0.008 \n",
+ " 1.25 ± 0.05 (129) \n",
+ " 0.96 ± 0.06 (102) \n",
+ " 4646.00, 0.000 \n",
+ " 0.29, 0.001 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.34 ± 0.06 (147) \n",
- " 2.25 ± 0.07 (124) \n",
- " 8361.00, 0.242 \n",
- " 0.07, 0.500 \n",
+ " 2.40 ± 0.07 (129) \n",
+ " 2.22 ± 0.08 (102) \n",
+ " 5516.00, 0.035 \n",
+ " 0.14, 0.270 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 15.79 ± 0.82 (147) \n",
- " 14.46 ± 0.79 (124) \n",
- " 8526.00, 0.361 \n",
- " 0.67, 0.638 \n",
+ " 14.72 ± 0.82 (129) \n",
+ " 12.94 ± 0.71 (102) \n",
+ " 6026.00, 0.273 \n",
+ " 0.76, 0.414 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.41 ± 0.58 (147) \n",
- " 7.43 ± 0.62 (124) \n",
- " 9193.00, 0.903 \n",
- " 0.88, 0.456 \n",
+ " 6.35 ± 0.60 (129) \n",
+ " 6.12 ± 0.53 (102) \n",
+ " 6535.00, 0.931 \n",
+ " 0.08, 0.921 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.22 ± 0.01 (147) \n",
- " 0.21 ± 0.01 (124) \n",
- " 8548.00, 0.379 \n",
- " 0.01, 0.370 \n",
+ " 0.21 ± 0.01 (129) \n",
+ " 0.20 ± 0.01 (102) \n",
+ " 5792.00, 0.119 \n",
+ " 0.02, 0.071 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.02 (147) \n",
- " 0.42 ± 0.02 (124) \n",
- " 8221.00, 0.165 \n",
- " 0.03, 0.167 \n",
+ " 0.48 ± 0.02 (129) \n",
+ " 0.46 ± 0.02 (102) \n",
+ " 5962.00, 0.222 \n",
+ " 0.06, 0.181 \n",
" \n",
" \n",
" Speed score \n",
- " 0.13 ± 0.01 (147) \n",
- " 0.11 ± 0.01 (124) \n",
- " 7793.00, 0.040 \n",
- " 0.02, 0.046 \n",
+ " 0.14 ± 0.01 (129) \n",
+ " 0.10 ± 0.01 (102) \n",
+ " 5128.00, 0.004 \n",
+ " 0.02, 0.008 \n",
" \n",
" \n",
"\n",
@@ -1578,37 +1604,37 @@
],
"text/plain": [
" 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",
+ "Average rate 8.90 ± 0.67 (129) 8.39 ± 0.60 (102) \n",
+ "Gridness 0.52 ± 0.03 (129) 0.44 ± 0.04 (102) \n",
+ "Sparsity 0.62 ± 0.02 (129) 0.66 ± 0.02 (102) \n",
+ "Selectivity 5.93 ± 0.28 (129) 5.98 ± 0.37 (102) \n",
+ "Information specificity 0.23 ± 0.02 (129) 0.22 ± 0.02 (102) \n",
+ "Max rate 37.44 ± 1.44 (129) 33.72 ± 1.31 (102) \n",
+ "Information rate 1.25 ± 0.05 (129) 0.96 ± 0.06 (102) \n",
+ "Interspike interval cv 2.40 ± 0.07 (129) 2.22 ± 0.08 (102) \n",
+ "In-field mean rate 14.72 ± 0.82 (129) 12.94 ± 0.71 (102) \n",
+ "Out-field mean rate 6.35 ± 0.60 (129) 6.12 ± 0.53 (102) \n",
+ "Burst event ratio 0.21 ± 0.01 (129) 0.20 ± 0.01 (102) \n",
+ "Specificity 0.48 ± 0.02 (129) 0.46 ± 0.02 (102) \n",
+ "Speed score 0.14 ± 0.01 (129) 0.10 ± 0.01 (102) \n",
"\n",
- " 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 "
+ " MWU PRS \n",
+ "Average rate 6514.00, 0.898 0.38, 0.786 \n",
+ "Gridness 5681.00, 0.075 0.13, 0.065 \n",
+ "Sparsity 7486.00, 0.072 0.06, 0.124 \n",
+ "Selectivity 6254.00, 0.520 0.10, 0.803 \n",
+ "Information specificity 5573.00, 0.046 0.05, 0.031 \n",
+ "Max rate 5851.00, 0.149 3.66, 0.072 \n",
+ "Information rate 4646.00, 0.000 0.29, 0.001 \n",
+ "Interspike interval cv 5516.00, 0.035 0.14, 0.270 \n",
+ "In-field mean rate 6026.00, 0.273 0.76, 0.414 \n",
+ "Out-field mean rate 6535.00, 0.931 0.08, 0.921 \n",
+ "Burst event ratio 5792.00, 0.119 0.02, 0.071 \n",
+ "Specificity 5962.00, 0.222 0.06, 0.181 \n",
+ "Speed score 5128.00, 0.004 0.02, 0.008 "
]
},
- "execution_count": 49,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1635,7 +1661,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -1668,94 +1694,94 @@
" \n",
" \n",
" Average rate \n",
- " 9.82 ± 0.91 (70) \n",
- " 9.28 ± 0.90 (65) \n",
- " 2175.00, 0.661 \n",
- " 0.18, 0.933 \n",
+ " 8.96 ± 0.80 (63) \n",
+ " 8.80 ± 0.85 (58) \n",
+ " 1781.00, 0.813 \n",
+ " 0.04, 0.969 \n",
" \n",
" \n",
" Gridness \n",
- " 0.54 ± 0.05 (70) \n",
- " 0.42 ± 0.05 (65) \n",
- " 1822.00, 0.046 \n",
- " 0.17, 0.052 \n",
+ " 0.53 ± 0.05 (63) \n",
+ " 0.41 ± 0.05 (58) \n",
+ " 1459.00, 0.057 \n",
+ " 0.21, 0.038 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.65 ± 0.02 (70) \n",
- " 0.69 ± 0.02 (65) \n",
- " 2578.00, 0.183 \n",
- " 0.06, 0.147 \n",
+ " 0.63 ± 0.02 (63) \n",
+ " 0.67 ± 0.03 (58) \n",
+ " 2138.00, 0.107 \n",
+ " 0.07, 0.126 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.25 ± 0.35 (70) \n",
- " 5.43 ± 0.48 (65) \n",
- " 2214.00, 0.790 \n",
- " 0.05, 0.961 \n",
+ " 5.76 ± 0.40 (63) \n",
+ " 5.69 ± 0.50 (58) \n",
+ " 1687.00, 0.469 \n",
+ " 0.00, 0.981 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.22 ± 0.03 (70) \n",
- " 0.19 ± 0.03 (65) \n",
- " 1888.00, 0.089 \n",
- " 0.05, 0.020 \n",
+ " 0.24 ± 0.03 (63) \n",
+ " 0.21 ± 0.03 (58) \n",
+ " 1452.00, 0.052 \n",
+ " 0.06, 0.031 \n",
" \n",
" \n",
" Max rate \n",
- " 36.77 ± 1.96 (70) \n",
- " 33.16 ± 1.79 (65) \n",
- " 1971.00, 0.181 \n",
- " 3.18, 0.250 \n",
+ " 37.39 ± 1.91 (63) \n",
+ " 33.11 ± 1.85 (58) \n",
+ " 1538.00, 0.134 \n",
+ " 4.06, 0.128 \n",
" \n",
" \n",
" Information rate \n",
- " 1.22 ± 0.06 (70) \n",
- " 0.89 ± 0.06 (65) \n",
- " 1431.00, 0.000 \n",
- " 0.20, 0.006 \n",
+ " 1.31 ± 0.08 (63) \n",
+ " 0.94 ± 0.08 (58) \n",
+ " 1143.00, 0.000 \n",
+ " 0.32, 0.003 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.37 ± 0.09 (70) \n",
- " 2.24 ± 0.09 (65) \n",
- " 2022.00, 0.266 \n",
- " 0.12, 0.520 \n",
+ " 2.39 ± 0.10 (63) \n",
+ " 2.19 ± 0.12 (58) \n",
+ " 1462.00, 0.059 \n",
+ " 0.18, 0.135 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 15.52 ± 1.15 (70) \n",
- " 13.80 ± 1.06 (65) \n",
- " 2064.00, 0.354 \n",
- " 0.63, 0.738 \n",
+ " 14.88 ± 1.05 (63) \n",
+ " 13.27 ± 1.04 (58) \n",
+ " 1633.00, 0.315 \n",
+ " 0.77, 0.683 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.09 ± 0.77 (70) \n",
- " 7.00 ± 0.80 (65) \n",
- " 2236.00, 0.865 \n",
- " 0.01, 0.979 \n",
+ " 6.37 ± 0.67 (63) \n",
+ " 6.57 ± 0.77 (58) \n",
+ " 1795.00, 0.870 \n",
+ " 0.47, 0.719 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.23 ± 0.01 (70) \n",
- " 0.23 ± 0.01 (65) \n",
- " 2307.00, 0.890 \n",
- " 0.01, 0.732 \n",
+ " 0.22 ± 0.01 (63) \n",
+ " 0.22 ± 0.01 (58) \n",
+ " 1897.00, 0.718 \n",
+ " 0.00, 0.824 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.03 (70) \n",
- " 0.42 ± 0.03 (65) \n",
- " 2049.00, 0.321 \n",
- " 0.01, 0.476 \n",
+ " 0.47 ± 0.03 (63) \n",
+ " 0.44 ± 0.03 (58) \n",
+ " 1605.00, 0.250 \n",
+ " 0.06, 0.398 \n",
" \n",
" \n",
" Speed score \n",
- " 0.14 ± 0.01 (70) \n",
- " 0.12 ± 0.01 (65) \n",
- " 1939.00, 0.140 \n",
- " 0.03, 0.069 \n",
+ " 0.14 ± 0.01 (63) \n",
+ " 0.11 ± 0.01 (58) \n",
+ " 1378.00, 0.020 \n",
+ " 0.04, 0.023 \n",
" \n",
" \n",
"\n",
@@ -1763,37 +1789,37 @@
],
"text/plain": [
" 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",
+ "Average rate 8.96 ± 0.80 (63) 8.80 ± 0.85 (58) 1781.00, 0.813 \n",
+ "Gridness 0.53 ± 0.05 (63) 0.41 ± 0.05 (58) 1459.00, 0.057 \n",
+ "Sparsity 0.63 ± 0.02 (63) 0.67 ± 0.03 (58) 2138.00, 0.107 \n",
+ "Selectivity 5.76 ± 0.40 (63) 5.69 ± 0.50 (58) 1687.00, 0.469 \n",
+ "Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.03 (58) 1452.00, 0.052 \n",
+ "Max rate 37.39 ± 1.91 (63) 33.11 ± 1.85 (58) 1538.00, 0.134 \n",
+ "Information rate 1.31 ± 0.08 (63) 0.94 ± 0.08 (58) 1143.00, 0.000 \n",
+ "Interspike interval cv 2.39 ± 0.10 (63) 2.19 ± 0.12 (58) 1462.00, 0.059 \n",
+ "In-field mean rate 14.88 ± 1.05 (63) 13.27 ± 1.04 (58) 1633.00, 0.315 \n",
+ "Out-field mean rate 6.37 ± 0.67 (63) 6.57 ± 0.77 (58) 1795.00, 0.870 \n",
+ "Burst event ratio 0.22 ± 0.01 (63) 0.22 ± 0.01 (58) 1897.00, 0.718 \n",
+ "Specificity 0.47 ± 0.03 (63) 0.44 ± 0.03 (58) 1605.00, 0.250 \n",
+ "Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (58) 1378.00, 0.020 \n",
"\n",
" PRS \n",
- "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 "
+ "Average rate 0.04, 0.969 \n",
+ "Gridness 0.21, 0.038 \n",
+ "Sparsity 0.07, 0.126 \n",
+ "Selectivity 0.00, 0.981 \n",
+ "Information specificity 0.06, 0.031 \n",
+ "Max rate 4.06, 0.128 \n",
+ "Information rate 0.32, 0.003 \n",
+ "Interspike interval cv 0.18, 0.135 \n",
+ "In-field mean rate 0.77, 0.683 \n",
+ "Out-field mean rate 0.47, 0.719 \n",
+ "Burst event ratio 0.00, 0.824 \n",
+ "Specificity 0.06, 0.398 \n",
+ "Speed score 0.04, 0.023 "
]
},
- "execution_count": 47,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -1821,7 +1847,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -1854,94 +1880,94 @@
" \n",
" \n",
" Average rate \n",
- " 10.08 ± 1.05 (61) \n",
- " 9.94 ± 1.17 (49) \n",
- " 1491.00, 0.986 \n",
- " 0.24, 0.763 \n",
+ " 8.29 ± 0.87 (52) \n",
+ " 7.61 ± 0.87 (38) \n",
+ " 958.00, 0.810 \n",
+ " 0.27, 0.805 \n",
" \n",
" \n",
" Gridness \n",
- " 0.53 ± 0.05 (61) \n",
- " 0.46 ± 0.06 (49) \n",
- " 1342.00, 0.361 \n",
- " 0.08, 0.289 \n",
+ " 0.54 ± 0.04 (52) \n",
+ " 0.48 ± 0.06 (38) \n",
+ " 914.00, 0.548 \n",
+ " 0.04, 0.608 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.67 ± 0.02 (61) \n",
- " 0.69 ± 0.03 (49) \n",
- " 1622.00, 0.445 \n",
- " 0.03, 0.466 \n",
+ " 0.63 ± 0.03 (52) \n",
+ " 0.64 ± 0.03 (38) \n",
+ " 1040.00, 0.674 \n",
+ " 0.06, 0.401 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.34 ± 0.38 (61) \n",
- " 5.21 ± 0.46 (49) \n",
- " 1372.00, 0.463 \n",
- " 0.37, 0.420 \n",
+ " 5.96 ± 0.46 (52) \n",
+ " 6.42 ± 0.60 (38) \n",
+ " 1019.00, 0.803 \n",
+ " 0.20, 0.850 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.19 ± 0.02 (61) \n",
- " 0.18 ± 0.03 (49) \n",
- " 1380.00, 0.493 \n",
- " 0.01, 0.725 \n",
+ " 0.21 ± 0.02 (52) \n",
+ " 0.22 ± 0.03 (38) \n",
+ " 950.00, 0.759 \n",
+ " 0.04, 0.505 \n",
" \n",
" \n",
" Max rate \n",
- " 37.61 ± 2.31 (61) \n",
- " 34.42 ± 1.99 (49) \n",
- " 1342.00, 0.361 \n",
- " 2.37, 0.351 \n",
+ " 36.27 ± 2.34 (52) \n",
+ " 33.49 ± 1.89 (38) \n",
+ " 943.00, 0.716 \n",
+ " 2.90, 0.558 \n",
" \n",
" \n",
" Information rate \n",
- " 1.08 ± 0.08 (61) \n",
- " 0.95 ± 0.07 (49) \n",
- " 1321.00, 0.298 \n",
- " 0.14, 0.413 \n",
+ " 1.13 ± 0.08 (52) \n",
+ " 0.98 ± 0.09 (38) \n",
+ " 827.00, 0.190 \n",
+ " 0.07, 0.332 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.28 ± 0.09 (61) \n",
- " 2.24 ± 0.11 (49) \n",
- " 1419.00, 0.652 \n",
- " 0.06, 0.740 \n",
+ " 2.37 ± 0.09 (52) \n",
+ " 2.23 ± 0.11 (38) \n",
+ " 869.00, 0.333 \n",
+ " 0.17, 0.470 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 15.61 ± 1.32 (61) \n",
- " 14.54 ± 1.29 (49) \n",
- " 1418.00, 0.648 \n",
- " 0.64, 0.675 \n",
+ " 13.79 ± 1.12 (52) \n",
+ " 12.21 ± 0.98 (38) \n",
+ " 912.00, 0.537 \n",
+ " 1.06, 0.452 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.65 ± 0.96 (61) \n",
- " 7.54 ± 1.06 (49) \n",
- " 1487.00, 0.966 \n",
- " 0.37, 0.789 \n",
+ " 5.80 ± 0.72 (52) \n",
+ " 5.36 ± 0.73 (38) \n",
+ " 959.00, 0.816 \n",
+ " 0.13, 0.916 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.21 ± 0.01 (61) \n",
- " 0.19 ± 0.01 (49) \n",
- " 1241.00, 0.128 \n",
- " 0.04, 0.037 \n",
+ " 0.20 ± 0.01 (52) \n",
+ " 0.16 ± 0.01 (38) \n",
+ " 676.00, 0.011 \n",
+ " 0.05, 0.007 \n",
" \n",
" \n",
" Specificity \n",
- " 0.42 ± 0.03 (61) \n",
- " 0.42 ± 0.03 (49) \n",
- " 1429.00, 0.696 \n",
- " 0.03, 0.495 \n",
+ " 0.47 ± 0.03 (52) \n",
+ " 0.48 ± 0.04 (38) \n",
+ " 976.00, 0.925 \n",
+ " 0.00, 0.985 \n",
" \n",
" \n",
" Speed score \n",
- " 0.12 ± 0.01 (61) \n",
- " 0.11 ± 0.01 (49) \n",
- " 1335.00, 0.339 \n",
- " 0.01, 0.545 \n",
+ " 0.12 ± 0.01 (52) \n",
+ " 0.11 ± 0.01 (38) \n",
+ " 784.00, 0.096 \n",
+ " 0.01, 0.241 \n",
" \n",
" \n",
"\n",
@@ -1949,37 +1975,37 @@
],
"text/plain": [
" 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",
+ "Average rate 8.29 ± 0.87 (52) 7.61 ± 0.87 (38) 958.00, 0.810 \n",
+ "Gridness 0.54 ± 0.04 (52) 0.48 ± 0.06 (38) 914.00, 0.548 \n",
+ "Sparsity 0.63 ± 0.03 (52) 0.64 ± 0.03 (38) 1040.00, 0.674 \n",
+ "Selectivity 5.96 ± 0.46 (52) 6.42 ± 0.60 (38) 1019.00, 0.803 \n",
+ "Information specificity 0.21 ± 0.02 (52) 0.22 ± 0.03 (38) 950.00, 0.759 \n",
+ "Max rate 36.27 ± 2.34 (52) 33.49 ± 1.89 (38) 943.00, 0.716 \n",
+ "Information rate 1.13 ± 0.08 (52) 0.98 ± 0.09 (38) 827.00, 0.190 \n",
+ "Interspike interval cv 2.37 ± 0.09 (52) 2.23 ± 0.11 (38) 869.00, 0.333 \n",
+ "In-field mean rate 13.79 ± 1.12 (52) 12.21 ± 0.98 (38) 912.00, 0.537 \n",
+ "Out-field mean rate 5.80 ± 0.72 (52) 5.36 ± 0.73 (38) 959.00, 0.816 \n",
+ "Burst event ratio 0.20 ± 0.01 (52) 0.16 ± 0.01 (38) 676.00, 0.011 \n",
+ "Specificity 0.47 ± 0.03 (52) 0.48 ± 0.04 (38) 976.00, 0.925 \n",
+ "Speed score 0.12 ± 0.01 (52) 0.11 ± 0.01 (38) 784.00, 0.096 \n",
"\n",
" PRS \n",
- "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 "
+ "Average rate 0.27, 0.805 \n",
+ "Gridness 0.04, 0.608 \n",
+ "Sparsity 0.06, 0.401 \n",
+ "Selectivity 0.20, 0.850 \n",
+ "Information specificity 0.04, 0.505 \n",
+ "Max rate 2.90, 0.558 \n",
+ "Information rate 0.07, 0.332 \n",
+ "Interspike interval cv 0.17, 0.470 \n",
+ "In-field mean rate 1.06, 0.452 \n",
+ "Out-field mean rate 0.13, 0.916 \n",
+ "Burst event ratio 0.05, 0.007 \n",
+ "Specificity 0.00, 0.985 \n",
+ "Speed score 0.01, 0.241 "
]
},
- "execution_count": 48,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -2006,193 +2032,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " 11 Hz \n",
- " 30 Hz \n",
- " MWU \n",
- " PRS \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " Average rate \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",
- " 0.42 ± 0.05 (65) \n",
- " 0.46 ± 0.06 (49) \n",
- " 1739.00, 0.403 \n",
- " 0.09, 0.420 \n",
- " \n",
- " \n",
- " Sparsity \n",
- " 0.69 ± 0.02 (65) \n",
- " 0.69 ± 0.03 (49) \n",
- " 1618.00, 0.886 \n",
- " 0.01, 0.660 \n",
- " \n",
- " \n",
- " Selectivity \n",
- " 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",
- " 0.19 ± 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",
- " 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",
- " 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",
- " 2.24 ± 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",
- " 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",
- " 7.00 ± 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",
- " 0.23 ± 0.01 (65) \n",
- " 0.19 ± 0.01 (49) \n",
- " 1093.00, 0.004 \n",
- " 0.05, 0.004 \n",
- " \n",
- " \n",
- " Specificity \n",
- " 0.42 ± 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",
- " 0.12 ± 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": [
- " 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 0.09, 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": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "_stim_data = stimulated_30\n",
- "_base_data = stimulated_11\n",
- "\n",
- "result = pd.DataFrame()\n",
- "\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",
- "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
- "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
- "\n",
- "\n",
- "result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.tex\")\n",
- "result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
- "result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -2217,7 +2057,7 @@
" \n",
" \n",
" Baseline I \n",
- " Baseline II \n",
+ " 30 Hz \n",
" MWU \n",
" PRS \n",
" \n",
@@ -2225,136 +2065,187 @@
" \n",
" \n",
" Average rate \n",
- " 9.82 ± 0.91 (70) \n",
- " 10.08 ± 1.05 (61) \n",
- " 2166.00, 0.888 \n",
- " 0.15, 0.852 \n",
+ " 8.96 ± 0.80 (63) \n",
+ " 7.61 ± 0.87 (38) \n",
+ " 1081.00, 0.418 \n",
+ " 0.27, 0.803 \n",
" \n",
" \n",
" Gridness \n",
- " 0.54 ± 0.05 (70) \n",
- " 0.53 ± 0.05 (61) \n",
- " 2158.00, 0.917 \n",
- " 0.00, 0.983 \n",
+ " 0.53 ± 0.05 (63) \n",
+ " 0.48 ± 0.06 (38) \n",
+ " 1094.00, 0.472 \n",
+ " 0.08, 0.363 \n",
" \n",
" \n",
" Sparsity \n",
- " 0.65 ± 0.02 (70) \n",
- " 0.67 ± 0.02 (61) \n",
- " 2001.00, 0.538 \n",
- " 0.04, 0.361 \n",
+ " 0.63 ± 0.02 (63) \n",
+ " 0.64 ± 0.03 (38) \n",
+ " 1261.00, 0.656 \n",
+ " 0.03, 0.641 \n",
" \n",
" \n",
" Selectivity \n",
- " 5.25 ± 0.35 (70) \n",
- " 5.34 ± 0.38 (61) \n",
- " 2062.00, 0.738 \n",
- " 0.25, 0.594 \n",
+ " 5.76 ± 0.40 (63) \n",
+ " 6.42 ± 0.60 (38) \n",
+ " 1276.00, 0.582 \n",
+ " 0.86, 0.283 \n",
" \n",
" \n",
" Information specificity \n",
- " 0.22 ± 0.03 (70) \n",
- " 0.19 ± 0.02 (61) \n",
- " 2329.00, 0.372 \n",
- " 0.05, 0.143 \n",
+ " 0.24 ± 0.03 (63) \n",
+ " 0.22 ± 0.03 (38) \n",
+ " 1076.00, 0.398 \n",
+ " 0.05, 0.161 \n",
" \n",
" \n",
" Max rate \n",
- " 36.77 ± 1.96 (70) \n",
- " 37.61 ± 2.31 (61) \n",
- " 2088.00, 0.830 \n",
- " 0.58, 0.784 \n",
+ " 37.39 ± 1.91 (63) \n",
+ " 33.49 ± 1.89 (38) \n",
+ " 1027.00, 0.235 \n",
+ " 3.99, 0.182 \n",
" \n",
" \n",
" Information rate \n",
- " 1.22 ± 0.06 (70) \n",
- " 1.08 ± 0.08 (61) \n",
- " 2501.00, 0.092 \n",
- " 0.14, 0.151 \n",
+ " 1.31 ± 0.08 (63) \n",
+ " 0.98 ± 0.09 (38) \n",
+ " 797.00, 0.005 \n",
+ " 0.32, 0.045 \n",
" \n",
" \n",
" Interspike interval cv \n",
- " 2.37 ± 0.09 (70) \n",
- " 2.28 ± 0.09 (61) \n",
- " 2257.00, 0.575 \n",
- " 0.01, 0.928 \n",
+ " 2.39 ± 0.10 (63) \n",
+ " 2.23 ± 0.11 (38) \n",
+ " 1100.00, 0.499 \n",
+ " 0.01, 0.993 \n",
" \n",
" \n",
" In-field mean rate \n",
- " 15.52 ± 1.15 (70) \n",
- " 15.61 ± 1.32 (61) \n",
- " 2162.00, 0.903 \n",
- " 0.87, 0.724 \n",
+ " 14.88 ± 1.05 (63) \n",
+ " 12.21 ± 0.98 (38) \n",
+ " 1018.00, 0.211 \n",
+ " 1.74, 0.272 \n",
" \n",
" \n",
" Out-field mean rate \n",
- " 7.09 ± 0.77 (70) \n",
- " 7.65 ± 0.96 (61) \n",
- " 2115.00, 0.928 \n",
- " 0.02, 0.986 \n",
+ " 6.37 ± 0.67 (63) \n",
+ " 5.36 ± 0.73 (38) \n",
+ " 1079.00, 0.410 \n",
+ " 0.51, 0.644 \n",
" \n",
" \n",
" Burst event ratio \n",
- " 0.23 ± 0.01 (70) \n",
- " 0.21 ± 0.01 (61) \n",
- " 2299.00, 0.451 \n",
- " 0.00, 0.830 \n",
+ " 0.22 ± 0.01 (63) \n",
+ " 0.16 ± 0.01 (38) \n",
+ " 675.00, 0.000 \n",
+ " 0.05, 0.006 \n",
" \n",
" \n",
" Specificity \n",
- " 0.45 ± 0.03 (70) \n",
- " 0.42 ± 0.03 (61) \n",
- " 2245.00, 0.613 \n",
- " 0.01, 0.921 \n",
+ " 0.47 ± 0.03 (63) \n",
+ " 0.48 ± 0.04 (38) \n",
+ " 1206.00, 0.952 \n",
+ " 0.01, 0.869 \n",
" \n",
" \n",
" Speed score \n",
- " 0.14 ± 0.01 (70) \n",
- " 0.12 ± 0.01 (61) \n",
- " 2423.00, 0.185 \n",
- " 0.04, 0.042 \n",
+ " 0.14 ± 0.01 (63) \n",
+ " 0.11 ± 0.01 (38) \n",
+ " 835.00, 0.011 \n",
+ " 0.06, 0.005 \n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
- " 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",
+ " Baseline I 30 Hz MWU \\\n",
+ "Average rate 8.96 ± 0.80 (63) 7.61 ± 0.87 (38) 1081.00, 0.418 \n",
+ "Gridness 0.53 ± 0.05 (63) 0.48 ± 0.06 (38) 1094.00, 0.472 \n",
+ "Sparsity 0.63 ± 0.02 (63) 0.64 ± 0.03 (38) 1261.00, 0.656 \n",
+ "Selectivity 5.76 ± 0.40 (63) 6.42 ± 0.60 (38) 1276.00, 0.582 \n",
+ "Information specificity 0.24 ± 0.03 (63) 0.22 ± 0.03 (38) 1076.00, 0.398 \n",
+ "Max rate 37.39 ± 1.91 (63) 33.49 ± 1.89 (38) 1027.00, 0.235 \n",
+ "Information rate 1.31 ± 0.08 (63) 0.98 ± 0.09 (38) 797.00, 0.005 \n",
+ "Interspike interval cv 2.39 ± 0.10 (63) 2.23 ± 0.11 (38) 1100.00, 0.499 \n",
+ "In-field mean rate 14.88 ± 1.05 (63) 12.21 ± 0.98 (38) 1018.00, 0.211 \n",
+ "Out-field mean rate 6.37 ± 0.67 (63) 5.36 ± 0.73 (38) 1079.00, 0.410 \n",
+ "Burst event ratio 0.22 ± 0.01 (63) 0.16 ± 0.01 (38) 675.00, 0.000 \n",
+ "Specificity 0.47 ± 0.03 (63) 0.48 ± 0.04 (38) 1206.00, 0.952 \n",
+ "Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (38) 835.00, 0.011 \n",
"\n",
" PRS \n",
- "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 "
+ "Average rate 0.27, 0.803 \n",
+ "Gridness 0.08, 0.363 \n",
+ "Sparsity 0.03, 0.641 \n",
+ "Selectivity 0.86, 0.283 \n",
+ "Information specificity 0.05, 0.161 \n",
+ "Max rate 3.99, 0.182 \n",
+ "Information rate 0.32, 0.045 \n",
+ "Interspike interval cv 0.01, 0.993 \n",
+ "In-field mean rate 1.74, 0.272 \n",
+ "Out-field mean rate 0.51, 0.644 \n",
+ "Burst event ratio 0.05, 0.006 \n",
+ "Specificity 0.01, 0.869 \n",
+ "Speed score 0.06, 0.005 "
]
},
- "execution_count": 46,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
+ "source": [
+ "_stim_data = stimulated_30\n",
+ "_base_data = baseline_i\n",
+ "\n",
+ "result = pd.DataFrame()\n",
+ "\n",
+ "result['Baseline I'] = _base_data[columns].agg(summarize)\n",
+ "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
+ "\n",
+ "result.index = map(rename, result.index)\n",
+ "\n",
+ "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
+ "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
+ "\n",
+ "\n",
+ "result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.tex\")\n",
+ "result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.csv\")\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_stim_data = stimulated_30\n",
+ "_base_data = stimulated_11\n",
+ "\n",
+ "result = pd.DataFrame()\n",
+ "\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",
+ "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
+ "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
+ "\n",
+ "\n",
+ "result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.tex\")\n",
+ "result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"_stim_data = baseline_i\n",
"_base_data = baseline_ii\n",
@@ -2384,7 +2275,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2399,10 +2290,13 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
+ "# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
+ "# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
+ "\n",
"stuff = {\n",
" '': {\n",
" 'base': gridcell_sessions.query('baseline'),\n",
@@ -2422,6 +2316,12 @@
" '': ['Baseline ', ' Stimulated'],\n",
" '_11': ['Baseline I ', ' 11 Hz'],\n",
" '_30': ['Baseline II ', ' 30 Hz']\n",
+ "}\n",
+ "\n",
+ "colors = {\n",
+ " '': None,\n",
+ " '_11': ['#1b9e77', '#d95f02'],\n",
+ " '_30': ['#7570b3', '#e7298a']\n",
"}"
]
},
@@ -2434,65 +2334,16 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "U-test: U value 10345.0 p value 0.0555771740141912\n",
- "_11\n",
- "U-test: U value 2662.0 p value 0.08875139162540739\n",
- "_30\n",
- "U-test: U value 1609.0 p value 0.49296516393290757\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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