In [2]:
%load_ext autoreload
%autoreload 2
In [3]:
import os
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import re
import shutil
import pandas as pd
import scipy.stats

import exdir
import expipe
from distutils.dir_util import copy_tree
import septum_mec
import spatial_maps as sp
import head_direction.head as head
import septum_mec.analysis.data_processing as dp
import septum_mec.analysis.registration
from septum_mec.analysis.plotting import violinplot

from spike_statistics.core import permutation_resampling

13:03:48 [I] klustakwik KlustaKwik2 version 0.2.6
/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
  return f(*args, **kwds)
/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
  return f(*args, **kwds)
In [4]:
project_path = dp.project_path()
project = expipe.get_project(project_path)
actions = project.actions

output_path = pathlib.Path("output") / "comparisons-gridcells"
(output_path / "statistics").mkdir(exist_ok=True, parents=True)
(output_path / "figures").mkdir(exist_ok=True, parents=True)

Load cell statistics and shuffling quantiles

In [5]:
statistics_action = actions['calculate-statistics']
identification_action = actions['identify-neurons']
sessions = pd.read_csv(identification_action.data_path('sessions'))
units = pd.read_csv(identification_action.data_path('units'))
session_units = pd.merge(sessions, units, on='action')
statistics_results = pd.read_csv(statistics_action.data_path('results'))
statistics = pd.merge(session_units, statistics_results, how='left')
statistics.head()
Out[5]:
action baseline entity frequency i ii session stim_location stimulated tag ... burst_event_ratio bursty_spike_ratio gridness border_score information_rate information_specificity head_mean_ang head_mean_vec_len spacing orientation
0 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.398230 0.678064 -0.466923 0.029328 1.009215 0.317256 5.438033 0.040874 0.628784 20.224859
1 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.138014 0.263173 -0.666792 0.308146 0.192524 0.033447 1.951740 0.017289 0.789388 27.897271
2 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.373986 0.659259 -0.572566 0.143252 4.745836 0.393704 4.439721 0.124731 0.555402 28.810794
3 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.087413 0.179245 -0.437492 0.268948 0.157394 0.073553 6.215195 0.101911 0.492250 9.462322
4 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.248771 0.463596 -0.085938 0.218744 0.519153 0.032683 1.531481 0.053810 0.559905 0.000000

5 rows × 39 columns

In [6]:
shuffling = actions['shuffling']
quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))
quantiles_95.head()
Out[6]:
border_score gridness head_mean_ang head_mean_vec_len information_rate speed_score action channel_group unit_name
0 0.348023 0.275109 3.012689 0.086792 0.707197 0.149071 1833-010719-1 0.0 127.0
1 0.362380 0.166475 3.133138 0.037271 0.482486 0.132212 1833-010719-1 0.0 161.0
2 0.367498 0.266865 5.586395 0.182843 0.271188 0.062821 1833-010719-1 0.0 191.0
3 0.331942 0.312155 5.955767 0.090786 0.354018 0.052009 1833-010719-1 0.0 223.0
4 0.325842 0.180495 5.262721 0.103584 0.210427 0.094041 1833-010719-1 0.0 225.0
In [7]:
action_columns = ['action', 'channel_group', 'unit_name']
data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=("", "_threshold"))

data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])

data.head()
Out[7]:
action baseline entity frequency i ii session stim_location stimulated tag ... head_mean_vec_len spacing orientation border_score_threshold gridness_threshold head_mean_ang_threshold head_mean_vec_len_threshold information_rate_threshold speed_score_threshold specificity
0 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.040874 0.628784 20.224859 0.332548 0.229073 6.029431 0.205362 1.115825 0.066736 0.451741
1 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.017289 0.789388 27.897271 0.354830 0.089333 6.120055 0.073566 0.223237 0.052594 0.098517
2 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.124731 0.555402 28.810794 0.264610 -0.121081 5.759406 0.150827 4.964984 0.027120 0.400770
3 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.101911 0.492250 9.462322 0.344280 0.215829 6.033364 0.110495 0.239996 0.054074 0.269461
4 1849-060319-3 True 1849 NaN False True 3 NaN False baseline ii ... 0.053810 0.559905 0.000000 0.342799 0.218967 5.768170 0.054762 0.524990 0.144702 0.133410

5 rows × 46 columns

Statistics about all cell-sessions

In [8]:
data.groupby('stimulated').count()['action']
Out[8]:
stimulated
False    624
True     660
Name: action, dtype: int64
In [9]:
data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)

Find all cells with gridness above threshold

In [10]:
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 sessions above threshold", len(sessions_above_threshold))
print("Number of animals", len(sessions_above_threshold.groupby(['entity'])))

Number of sessions above threshold 194
Number of animals 4
In [11]:
gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]
In [12]:
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'])))

Number of gridcells 139
Number of gridcell recordings 231
Number of animals 4
In [13]:
baseline_i = gridcell_sessions.query('baseline and Hz11')
stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location=="ms"')

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))

print("Number of gridcells in baseline ii sessions", len(baseline_ii))
print("Number of gridcells in stimulated 30Hz ms sessions", len(stimulated_30))

Number of gridcells in baseline i sessions 66
Number of gridcells in stimulated 11Hz ms sessions 61
Number of gridcells in baseline ii sessions 56
Number of gridcells in stimulated 30Hz ms sessions 40

slice unique units

In [14]:
baseline_i = baseline_i.drop_duplicates('unit_id')
stimulated_11 = stimulated_11.drop_duplicates('unit_id')
baseline_ii = baseline_ii.drop_duplicates('unit_id')
stimulated_30 = stimulated_30.drop_duplicates('unit_id')
In [15]:
print("Number of gridcells in baseline i sessions", len(baseline_i))
print("Number of gridcells in stimulated 11Hz ms sessions", len(stimulated_11))

print("Number of gridcells in baseline ii sessions", len(baseline_ii))
print("Number of gridcells in stimulated 30Hz ms sessions", len(stimulated_30))

Number of gridcells in baseline i sessions 63
Number of gridcells in stimulated 11Hz ms sessions 58
Number of gridcells in baseline ii sessions 52
Number of gridcells in stimulated 30Hz ms sessions 38

Calculate statistics

In [16]:
columns = [
    'average_rate', 'gridness', 'sparsity', 'selectivity', 'information_specificity',
    'max_rate', 'information_rate', 'interspike_interval_cv', 
    'in_field_mean_rate', 'out_field_mean_rate', 
    'burst_event_ratio', 'specificity', 'speed_score'
]
In [17]:
gridcell_sessions.groupby('stimulated')[columns].mean()
Out[17]:
average_rate gridness sparsity selectivity information_specificity max_rate information_rate interspike_interval_cv in_field_mean_rate out_field_mean_rate burst_event_ratio specificity speed_score
stimulated
False 8.904501 0.521371 0.618384 5.934539 0.234632 37.437808 1.246546 2.404647 14.717635 6.346875 0.211840 0.478775 0.135495
True 8.392252 0.440296 0.655698 5.977408 0.215736 33.716478 0.964787 2.223636 12.936021 6.122228 0.197264 0.455878 0.104697
In [18]:
gridcell_sessions.query('baseline')[columns].describe()
Out[18]:
average_rate gridness sparsity selectivity information_specificity max_rate information_rate interspike_interval_cv in_field_mean_rate out_field_mean_rate burst_event_ratio specificity speed_score
count 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000 129.000000
mean 8.904501 0.521371 0.618384 5.934539 0.234632 37.437808 1.246546 2.404647 14.717635 6.346875 0.211840 0.478775 0.135495
std 7.605598 0.337607 0.187934 3.217366 0.200726 16.300117 0.605971 0.756407 9.267522 6.805499 0.080143 0.209531 0.072831
min 0.478349 -0.684924 0.200066 1.533216 0.007807 3.346027 0.117638 1.304387 0.924066 0.159076 0.025000 0.071681 -0.025629
25% 3.518392 0.316326 0.437499 3.729863 0.093252 26.948843 0.786753 1.872991 7.701156 1.669844 0.160795 0.310822 0.084280
50% 6.456882 0.529243 0.642167 4.794970 0.180286 35.064991 1.156087 2.221185 12.212289 4.314913 0.210240 0.436340 0.128603
75% 12.721755 0.783682 0.758097 7.439464 0.312487 44.324873 1.592948 2.770624 20.974026 9.121505 0.267568 0.624834 0.188948
max 59.365312 1.148979 0.976157 18.975875 1.243307 90.160158 3.456796 5.671362 66.350754 56.255544 0.393306 1.066391 0.297548
In [19]:
gridcell_sessions.query("stimulated")[columns].describe()
Out[19]:
average_rate gridness sparsity selectivity information_specificity max_rate information_rate interspike_interval_cv in_field_mean_rate out_field_mean_rate burst_event_ratio specificity speed_score
count 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000 102.000000
mean 8.392252 0.440296 0.655698 5.977408 0.215736 33.716478 0.964787 2.223636 12.936021 6.122228 0.197264 0.455878 0.104697
std 6.057001 0.357038 0.211704 3.702400 0.235916 13.249312 0.572972 0.819734 7.211895 5.366332 0.082164 0.236777 0.081989
min 0.198337 -0.516914 0.172684 1.930026 0.013088 2.846281 0.063173 1.110672 0.524639 0.099060 0.008475 0.097718 -0.138128
25% 3.579184 0.265949 0.458493 3.044303 0.066656 25.555110 0.564279 1.620472 7.555760 1.733624 0.146755 0.248057 0.056903
50% 6.838561 0.399053 0.699561 4.891855 0.128562 31.402558 0.862413 2.084020 11.451560 4.234871 0.192948 0.376143 0.106314
75% 11.934599 0.749561 0.842332 8.001587 0.300713 42.334786 1.190324 2.673991 17.335356 8.583415 0.247405 0.684623 0.149313
max 24.858738 1.155123 0.967003 19.911477 1.359164 65.990793 3.182285 6.526960 34.489913 21.696265 0.393037 1.091064 0.390079

Create nice table

In [19]:
def summarize(data):
    return "{:.2f} ± {:.2f} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))


def MWU(column, stim, base):
    '''
    Mann Whitney U
    '''
    Uvalue, pvalue = scipy.stats.mannwhitneyu(
        stim[column].dropna(), 
        base[column].dropna(),
        alternative='two-sided')

    return "{:.2f}, {:.3f}".format(Uvalue, pvalue)


def PRS(column, stim, base):
    '''
    Permutation ReSampling
    '''
    pvalue, observed_diff, diffs = permutation_resampling(
        stim[column].dropna(), 
        base[column].dropna(), statistic=np.median)

    return "{:.2f}, {:.3f}".format(observed_diff, pvalue)


def rename(name):
    return name.replace("_field", "-field").replace("_", " ").capitalize()
In [20]:
_stim_data = gridcell_sessions.query('stimulated')
_base_data = gridcell_sessions.query('baseline and i')

result = pd.DataFrame()

result['Baseline I'] = _base_data[columns].agg(summarize)
result['Stimulated'] = _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.tex")
result.to_latex(output_path / "statistics" / "statistics.csv")
result
Out[20]:
Baseline I Stimulated MWU PRS
Average rate 8.61 ± 0.75 (71) 8.39 ± 0.60 (102) 3599.00, 0.947 0.55, 0.764
Gridness 0.51 ± 0.04 (71) 0.44 ± 0.04 (102) 3208.00, 0.203 0.13, 0.141
Sparsity 0.61 ± 0.02 (71) 0.66 ± 0.02 (102) 4170.00, 0.091 0.06, 0.179
Selectivity 5.91 ± 0.37 (71) 5.98 ± 0.37 (102) 3460.00, 0.620 0.10, 0.869
Information specificity 0.25 ± 0.03 (71) 0.22 ± 0.02 (102) 2944.00, 0.037 0.05, 0.033
Max rate 36.55 ± 1.78 (71) 33.72 ± 1.31 (102) 3291.00, 0.309 3.19, 0.195
Information rate 1.30 ± 0.07 (71) 0.96 ± 0.06 (102) 2385.00, 0.000 0.32, 0.000
Interspike interval cv 2.42 ± 0.10 (71) 2.22 ± 0.08 (102) 3034.00, 0.070 0.12, 0.392
In-field mean rate 14.43 ± 1.00 (71) 12.94 ± 0.71 (102) 3368.00, 0.436 0.39, 0.812
Out-field mean rate 6.05 ± 0.62 (71) 6.12 ± 0.53 (102) 3600.00, 0.950 0.08, 0.946
Burst event ratio 0.22 ± 0.01 (71) 0.20 ± 0.01 (102) 3090.00, 0.102 0.02, 0.124
Specificity 0.48 ± 0.03 (71) 0.46 ± 0.02 (102) 3268.00, 0.277 0.06, 0.356
Speed score 0.14 ± 0.01 (71) 0.10 ± 0.01 (102) 2546.00, 0.001 0.05, 0.000
In [21]:
_stim_data = stimulated_11
_base_data = baseline_i

result = pd.DataFrame()

result['Baseline I'] = _base_data[columns].agg(summarize)
result['11 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_11.tex")
result.to_latex(output_path / "statistics" / "statistics_11.csv")
result
Out[21]:
Baseline I 11 Hz MWU PRS
Average rate 8.96 ± 0.80 (63) 8.80 ± 0.85 (58) 1781.00, 0.813 0.04, 0.972
Gridness 0.53 ± 0.05 (63) 0.41 ± 0.05 (58) 1459.00, 0.057 0.21, 0.043
Sparsity 0.63 ± 0.02 (63) 0.67 ± 0.03 (58) 2138.00, 0.107 0.07, 0.119
Selectivity 5.76 ± 0.40 (63) 5.69 ± 0.50 (58) 1687.00, 0.469 0.00, 0.982
Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.03 (58) 1452.00, 0.052 0.06, 0.032
Max rate 37.39 ± 1.91 (63) 33.11 ± 1.85 (58) 1538.00, 0.134 4.06, 0.122
Information rate 1.31 ± 0.08 (63) 0.94 ± 0.08 (58) 1143.00, 0.000 0.32, 0.003
Interspike interval cv 2.39 ± 0.10 (63) 2.19 ± 0.12 (58) 1462.00, 0.059 0.18, 0.135
In-field mean rate 14.88 ± 1.05 (63) 13.27 ± 1.04 (58) 1633.00, 0.315 0.77, 0.688
Out-field mean rate 6.37 ± 0.67 (63) 6.57 ± 0.77 (58) 1795.00, 0.870 0.47, 0.724
Burst event ratio 0.22 ± 0.01 (63) 0.22 ± 0.01 (58) 1897.00, 0.718 0.00, 0.824
Specificity 0.47 ± 0.03 (63) 0.44 ± 0.03 (58) 1605.00, 0.250 0.06, 0.398
Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (58) 1378.00, 0.020 0.04, 0.023
In [22]:
_stim_data = stimulated_30
_base_data = baseline_ii

result = pd.DataFrame()

result['Baseline II'] = _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_30.tex")
result.to_latex(output_path / "statistics" / "statistics_30.csv")
result
Out[22]:
Baseline II 30 Hz MWU PRS
Average rate 8.29 ± 0.87 (52) 7.61 ± 0.87 (38) 958.00, 0.810 0.27, 0.800
Gridness 0.54 ± 0.04 (52) 0.48 ± 0.06 (38) 914.00, 0.548 0.04, 0.601
Sparsity 0.63 ± 0.03 (52) 0.64 ± 0.03 (38) 1040.00, 0.674 0.06, 0.393
Selectivity 5.96 ± 0.46 (52) 6.42 ± 0.60 (38) 1019.00, 0.803 0.20, 0.847
Information specificity 0.21 ± 0.02 (52) 0.22 ± 0.03 (38) 950.00, 0.759 0.04, 0.502
Max rate 36.27 ± 2.34 (52) 33.49 ± 1.89 (38) 943.00, 0.716 2.90, 0.555
Information rate 1.13 ± 0.08 (52) 0.98 ± 0.09 (38) 827.00, 0.190 0.07, 0.334
Interspike interval cv 2.37 ± 0.09 (52) 2.23 ± 0.11 (38) 869.00, 0.333 0.17, 0.476
In-field mean rate 13.79 ± 1.12 (52) 12.21 ± 0.98 (38) 912.00, 0.537 1.06, 0.455
Out-field mean rate 5.80 ± 0.72 (52) 5.36 ± 0.73 (38) 959.00, 0.816 0.13, 0.912
Burst event ratio 0.20 ± 0.01 (52) 0.16 ± 0.01 (38) 676.00, 0.011 0.05, 0.006
Specificity 0.47 ± 0.03 (52) 0.48 ± 0.04 (38) 976.00, 0.925 0.00, 0.987
Speed score 0.12 ± 0.01 (52) 0.11 ± 0.01 (38) 784.00, 0.096 0.01, 0.230
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
Out[23]:
Baseline I 30 Hz MWU PRS
Average rate 8.96 ± 0.80 (63) 7.61 ± 0.87 (38) 1081.00, 0.418 0.27, 0.806
Gridness 0.53 ± 0.05 (63) 0.48 ± 0.06 (38) 1094.00, 0.472 0.08, 0.361
Sparsity 0.63 ± 0.02 (63) 0.64 ± 0.03 (38) 1261.00, 0.656 0.03, 0.638
Selectivity 5.76 ± 0.40 (63) 6.42 ± 0.60 (38) 1276.00, 0.582 0.86, 0.293
Information specificity 0.24 ± 0.03 (63) 0.22 ± 0.03 (38) 1076.00, 0.398 0.05, 0.165
Max rate 37.39 ± 1.91 (63) 33.49 ± 1.89 (38) 1027.00, 0.235 3.99, 0.188
Information rate 1.31 ± 0.08 (63) 0.98 ± 0.09 (38) 797.00, 0.005 0.32, 0.047
Interspike interval cv 2.39 ± 0.10 (63) 2.23 ± 0.11 (38) 1100.00, 0.499 0.01, 0.993
In-field mean rate 14.88 ± 1.05 (63) 12.21 ± 0.98 (38) 1018.00, 0.211 1.74, 0.276
Out-field mean rate 6.37 ± 0.67 (63) 5.36 ± 0.73 (38) 1079.00, 0.410 0.51, 0.631
Burst event ratio 0.22 ± 0.01 (63) 0.16 ± 0.01 (38) 675.00, 0.000 0.05, 0.003
Specificity 0.47 ± 0.03 (63) 0.48 ± 0.04 (38) 1206.00, 0.952 0.01, 0.873
Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (38) 835.00, 0.011 0.06, 0.004
In [24]:
_stim_data = stimulated_30
_base_data = stimulated_11

result = pd.DataFrame()

result['11 Hz'] = _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_11_vs_30.tex")
result.to_latex(output_path / "statistics" / "statistics_11_vs_30.csv")
result
Out[24]:
11 Hz 30 Hz MWU PRS
Average rate 8.80 ± 0.85 (58) 7.61 ± 0.87 (38) 1010.00, 0.493 0.23, 0.893
Gridness 0.41 ± 0.05 (58) 0.48 ± 0.06 (38) 1259.00, 0.241 0.13, 0.094
Sparsity 0.67 ± 0.03 (58) 0.64 ± 0.03 (38) 1002.00, 0.456 0.04, 0.561
Selectivity 5.69 ± 0.50 (58) 6.42 ± 0.60 (38) 1260.00, 0.238 0.85, 0.335
Information specificity 0.21 ± 0.03 (58) 0.22 ± 0.03 (38) 1231.00, 0.336 0.01, 0.732
Max rate 33.11 ± 1.85 (58) 33.49 ± 1.89 (38) 1136.00, 0.802 0.07, 0.993
Information rate 0.94 ± 0.08 (58) 0.98 ± 0.09 (38) 1171.00, 0.608 0.01, 0.789
Interspike interval cv 2.19 ± 0.12 (58) 2.23 ± 0.11 (38) 1228.00, 0.347 0.17, 0.328
In-field mean rate 13.27 ± 1.04 (58) 12.21 ± 0.98 (38) 1058.00, 0.744 0.97, 0.637
Out-field mean rate 6.57 ± 0.77 (58) 5.36 ± 0.73 (38) 1019.00, 0.537 0.04, 0.958
Burst event ratio 0.22 ± 0.01 (58) 0.16 ± 0.01 (38) 552.00, 0.000 0.06, 0.000
Specificity 0.44 ± 0.03 (58) 0.48 ± 0.04 (38) 1233.00, 0.328 0.07, 0.380
Speed score 0.11 ± 0.01 (58) 0.11 ± 0.01 (38) 1022.00, 0.551 0.02, 0.144
In [25]:
_stim_data = baseline_i
_base_data = baseline_ii

result = pd.DataFrame()

result['Baseline I'] = _stim_data[columns].agg(summarize)
result['Baseline II'] = _base_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_base_i_vs_base_ii.tex")
result.to_latex(output_path / "statistics" / "statistics_base_i_vs_base_ii.csv")
result
Out[25]:
Baseline I Baseline II MWU PRS
Average rate 8.96 ± 0.80 (63) 8.29 ± 0.87 (52) 1756.00, 0.509 0.55, 0.674
Gridness 0.53 ± 0.05 (63) 0.54 ± 0.04 (52) 1664.00, 0.886 0.04, 0.550
Sparsity 0.63 ± 0.02 (63) 0.63 ± 0.03 (52) 1652.00, 0.940 0.03, 0.648
Selectivity 5.76 ± 0.40 (63) 5.96 ± 0.46 (52) 1542.00, 0.592 0.66, 0.360
Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.02 (52) 1718.00, 0.655 0.01, 0.812
Max rate 37.39 ± 1.91 (63) 36.27 ± 2.34 (52) 1757.00, 0.505 1.09, 0.610
Information rate 1.31 ± 0.08 (63) 1.13 ± 0.08 (52) 1929.00, 0.103 0.25, 0.140
Interspike interval cv 2.39 ± 0.10 (63) 2.37 ± 0.09 (52) 1572.00, 0.713 0.16, 0.479
In-field mean rate 14.88 ± 1.05 (63) 13.79 ± 1.12 (52) 1763.00, 0.484 0.68, 0.690
Out-field mean rate 6.37 ± 0.67 (63) 5.80 ± 0.72 (52) 1737.00, 0.580 0.38, 0.586
Burst event ratio 0.22 ± 0.01 (63) 0.20 ± 0.01 (52) 1834.00, 0.272 0.01, 0.681
Specificity 0.47 ± 0.03 (63) 0.47 ± 0.03 (52) 1588.00, 0.781 0.01, 0.765
Speed score 0.14 ± 0.01 (63) 0.12 ± 0.01 (52) 1943.00, 0.087 0.04, 0.017

Violinplot

In [20]:
%matplotlib inline
plt.rc('axes', titlesize=12)
plt.rcParams.update({
    'font.size': 12, 
    'figure.figsize': (1.7, 3), 
    'figure.dpi': 150
})
In [21]:
# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']
# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']

stuff = {
    '': {
        'base': gridcell_sessions.query('baseline and i'),
        'stim': gridcell_sessions.query('stimulated')
    },
    '_11': {
        'base': baseline_i,
        'stim': stimulated_11
    },
    '_30': {
        'base': baseline_ii,
        'stim': stimulated_30
    }
}

label = {
    '': ['Baseline I   ', '   Stimulated'],
    '_11': ['Baseline I ', '  11 Hz'],
    '_30': ['Baseline II ', '  30 Hz']
}

colors = {
    '': ['#1b9e77', '#b2182b'],
    '_11': ['#1b9e77', '#d95f02'],
    '_30': ['#7570b3', '#e7298a']
}

Information rate

In [22]:
for key, dd in stuff.items():
    baseline = dd['base']['information_specificity'].to_numpy()
    stimulated = dd['stim']['information_specificity'].to_numpy()
    print(key)
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[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")

U-test: U value 4298.0 p value 0.036830785214103115
_11
U-test: U value 2202.0 p value 0.05201320820170774
_30
U-test: U value 1026.0 p value 0.7593436297175663
In [23]:
for key, dd in stuff.items():
    baseline = dd['base']['information_rate'].to_numpy()
    stimulated = dd['stim']['information_rate'].to_numpy()
    print(key)
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Spatial information")
    plt.ylabel("bits/s")
    plt.ylim(-0.2, 4)

    plt.savefig(output_path / "figures" / f"spatial_information{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"spatial_information{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 4857.0 p value 0.00013747228542563089
_11
U-test: U value 2511.0 p value 0.0003908085167909787
_30
U-test: U value 1149.0 p value 0.18980799435841422
In [24]:
for key, dd in stuff.items():
    baseline = dd['base']['specificity'].to_numpy()
    stimulated = dd['stim']['specificity'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Spatial specificity")
    plt.ylabel("")
    plt.ylim(-0.02, 1.25)
    plt.savefig(output_path / "figures" / f"specificity{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"specificity{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3974.0 p value 0.2766869426478231
U-test: U value 2049.0 p value 0.25046855806374924
U-test: U value 1000.0 p value 0.9251527394243083
In [25]:
for key, dd in stuff.items():
    baseline = dd['base']['average_rate'].to_numpy()
    stimulated = dd['stim']['average_rate'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Average rate")
    plt.ylabel("spikes/s")
    plt.ylim(-0.2, 40)

    plt.savefig(output_path / "figures" / f"average_rate{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"average_rate{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3643.0 p value 0.9471010896899097
U-test: U value 1873.0 p value 0.8133796041365939
U-test: U value 1018.0 p value 0.8095631030980726
In [26]:
for key, dd in stuff.items():
    baseline = dd['base']['max_rate'].to_numpy()
    stimulated = dd['stim']['max_rate'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Max rate")
    plt.ylabel("spikes/s")
    # plt.ylim(-0.2, 45)

    plt.savefig(output_path / "figures" / f"max_rate{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"max_rate{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3951.0 p value 0.30924120740340955
U-test: U value 2116.0 p value 0.13443770008325492
U-test: U value 1033.0 p value 0.7162113753904321
In [27]:
for key, dd in stuff.items():
    baseline = dd['base']['interspike_interval_cv'].to_numpy()
    stimulated = dd['stim']['interspike_interval_cv'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("ISI CV")
    plt.ylabel("Coefficient of variation")
    # plt.ylim(0.9, 5)

    plt.savefig(output_path / "figures" / f"isi_cv{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"isi_cv{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 4208.0 p value 0.0703111829950676
U-test: U value 2192.0 p value 0.05860549066385172
U-test: U value 1107.0 p value 0.3330227188913163
In [28]:
for key, dd in stuff.items():
    baseline = dd['base']['in_field_mean_rate'].to_numpy()
    stimulated = dd['stim']['in_field_mean_rate'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("In-field rate")
    plt.ylabel("spikes/s")
    # plt.ylim(-0.1, 18)

    plt.savefig(output_path / "figures" / f"in_field_mean_rate{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"in_field_mean_rate{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3874.0 p value 0.4358627795642821
U-test: U value 2021.0 p value 0.31540792775479376
U-test: U value 1064.0 p value 0.5373862857775564
In [29]:
for key, dd in stuff.items():
    baseline = dd['base']['out_field_mean_rate'].to_numpy()
    stimulated = dd['stim']['out_field_mean_rate'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Out-of-field rate")
    plt.ylabel("spikes/s")
    # plt.ylim(-0.2, 8)

    plt.savefig(output_path / "figures" / f"out_field_mean_rate{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"out_field_mean_rate{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3642.0 p value 0.9495581436162017
U-test: U value 1859.0 p value 0.8701783404716995
U-test: U value 1017.0 p value 0.8159007523250839
In [30]:
for key, dd in stuff.items():
    baseline = dd['base']['burst_event_ratio'].to_numpy()
    stimulated = dd['stim']['burst_event_ratio'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Bursting ratio")
    plt.ylabel("")
    # plt.ylim(-0.02, 0.60)

    plt.savefig(output_path / "figures" / f"burst_event_ratio{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"burst_event_ratio{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 4152.0 p value 0.10161200342185885
U-test: U value 1757.0 p value 0.718407479245363
U-test: U value 1300.0 p value 0.010937547927479946
In [31]:
for key, dd in stuff.items():
    baseline = dd['base']['max_field_mean_rate'].to_numpy()
    stimulated = dd['stim']['max_field_mean_rate'].to_numpy()
    plt.figure()
    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)

    plt.savefig(output_path / "figures" / f"max_field_mean_rate{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"max_field_mean_rate{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 3934.0 p value 0.3348681711667292
U-test: U value 2022.0 p value 0.3129134904118731
U-test: U value 1069.0 p value 0.5107849438878747

/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/linalg/linalg.py:2125: RuntimeWarning: invalid value encountered in det
  r = _umath_linalg.det(a, signature=signature)
In [32]:
for key, dd in stuff.items():
    baseline = dd['base']['bursty_spike_ratio'].to_numpy()
    stimulated = dd['stim']['bursty_spike_ratio'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("ratio of spikes per burst")
    plt.ylabel("")
    # plt.ylim(-0.03,0.9)

    plt.savefig(output_path / "figures" / f"bursty_spike_ratio{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"bursty_spike_ratio{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 4196.0 p value 0.07625033158277
U-test: U value 1789.0 p value 0.8457359656438074
U-test: U value 1312.0 p value 0.008224362184318894
In [33]:
for key, dd in stuff.items():
    baseline = dd['base']['gridness'].to_numpy()
    stimulated = dd['stim']['gridness'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Gridness")
    plt.ylabel("Gridness")
    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")

U-test: U value 4034.0 p value 0.20303646513670592
U-test: U value 2195.0 p value 0.05655864319615998
U-test: U value 1062.0 p value 0.5482183981489739
In [34]:
for key, dd in stuff.items(): #TODO narrow broad spiking
    baseline = dd['base']['speed_score'].to_numpy()
    stimulated = dd['stim']['speed_score'].to_numpy()
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])
    plt.title("Speed score")
    plt.ylabel("Speed score")
    # plt.ylim(-0.1, 0.5)

    plt.savefig(output_path / "figures" / f"speed_score{key}.svg", bbox_inches="tight")
    plt.savefig(output_path / "figures" / f"speed_score{key}.png", dpi=600, bbox_inches="tight")

U-test: U value 4696.0 p value 0.0009136914115846572
U-test: U value 2276.0 p value 0.019967404084342423
U-test: U value 1192.0 p value 0.09642850317783999

inihibitory cells

In [35]:
stim_action = actions['stimulus-response']
stim_results = pd.read_csv(stim_action.data_path('results'))
# stim_results has old unit id's but correct on (action, unit_name, channel_group)
stim_results = stim_results.drop('unit_id', axis=1)

data = data.merge(stim_results, how='left')

waveform_action = actions['waveform-analysis']
waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)

data = data.merge(waveform_results, how='left')

data.bs = data.bs.astype(bool)

data.loc[data.eval('not t_i_peak.isnull() and not bs'), 'ns_inhibited'] = True
data.ns_inhibited.fillna(False, inplace=True)

data.loc[data.eval('t_i_peak.isnull() and not bs'), 'ns_not_inhibited'] = True
data.ns_not_inhibited.fillna(False, inplace=True)

# make baseline for inhibited vs not inhibited
data.loc[data.unit_id.isin(data.query('ns_inhibited').unit_id.values), 'ns_inhibited'] = True
data.loc[data.unit_id.isin(data.query('ns_not_inhibited').unit_id.values), 'ns_not_inhibited'] = True

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-35-d562b46ecc21> in <module>
     13 data.bs = data.bs.astype(bool)
     14 
---> 15 data.loc[data.eval('not t_i_peak.isnull() and not bs'), 'ns_inhibited'] = True
     16 data.ns_inhibited.fillna(False, inplace=True)
     17 

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
   3313             kwargs["target"] = self
   3314         kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
-> 3315         return _eval(expr, inplace=inplace, **kwargs)
   3316 
   3317     def select_dtypes(self, include=None, exclude=None):

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
    325         eng = _engines[engine]
    326         eng_inst = eng(parsed_expr)
--> 327         ret = eng_inst.evaluate()
    328 
    329         if parsed_expr.assigner is None:

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py in evaluate(self)
     68 
     69         # make sure no names in resolvers and locals/globals clash
---> 70         res = self._evaluate()
     71         return _reconstruct_object(
     72             self.result_type, res, self.aligned_axes, self.expr.terms.return_type

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py in _evaluate(self)
    116             scope = env.full_scope
    117             truediv = scope["truediv"]
--> 118             _check_ne_builtin_clash(self.expr)
    119             return ne.evaluate(s, local_dict=scope, truediv=truediv)
    120         except KeyError as e:

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py in _check_ne_builtin_clash(expr)
     25         Terms can contain
     26     """
---> 27     names = expr.names
     28     overlap = names & _ne_builtins
     29 

~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/expr.py in names(self)
    852         if is_term(self.terms):
    853             return frozenset([self.terms.name])
--> 854         return frozenset(term.name for term in com.flatten(self.terms))
    855 
    856 

TypeError: unhashable type: 'numpy.ndarray'
In [ ]:
baseline = data.query('ns_inhibited and baseline and i')['speed_score'].to_numpy()
stimulated = data.query('ns_inhibited and stimulated')['speed_score'].to_numpy()
plt.figure()
violinplot(baseline, stimulated, xticks=label[''], colors=colors[''])
plt.title("Speed score")
plt.ylabel("Speed score")
# plt.ylim(-0.1, 0.5)

plt.savefig(output_path / "figures" / f"speed_score_ns_inhibited.svg", bbox_inches="tight")
plt.savefig(output_path / "figures" / f"speed_score_ns_inhibited.png", dpi=600, bbox_inches="tight")

Register in Expipe

In [ ]:
action = project.require_action("comparisons-gridcells")
In [ ]:
copy_tree(output_path, str(action.data_path()))
In [ ]:
septum_mec.analysis.registration.store_notebook(action, "20_comparisons_gridcells.ipynb")
In [ ]: