In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
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

12:05:23 [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 [3]:
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 [4]:
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[4]:
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 [5]:
shuffling = actions['shuffling']
quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))
quantiles_95.head()
Out[5]:
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 [6]:
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[6]:
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 [7]:
data.groupby('stimulated').count()['action']
Out[7]:
stimulated
False    624
True     660
Name: action, dtype: int64
In [8]:
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 [9]:
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 [10]:
gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]
In [11]:
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 [12]:
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 [13]:
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 [14]:
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 [15]:
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 [16]:
gridcell_sessions.groupby('stimulated')[columns].mean()
Out[16]:
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 [17]:
gridcell_sessions.query('baseline')[columns].describe()
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
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 [18]:
gridcell_sessions.query("stimulated")[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 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')

result = pd.DataFrame()

result['Baseline'] = _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 Stimulated MWU PRS
Average rate 8.90 ± 0.67 (129) 8.39 ± 0.60 (102) 6514.00, 0.898 0.38, 0.786
Gridness 0.52 ± 0.03 (129) 0.44 ± 0.04 (102) 5681.00, 0.075 0.13, 0.065
Sparsity 0.62 ± 0.02 (129) 0.66 ± 0.02 (102) 7486.00, 0.072 0.06, 0.124
Selectivity 5.93 ± 0.28 (129) 5.98 ± 0.37 (102) 6254.00, 0.520 0.10, 0.803
Information specificity 0.23 ± 0.02 (129) 0.22 ± 0.02 (102) 5573.00, 0.046 0.05, 0.031
Max rate 37.44 ± 1.44 (129) 33.72 ± 1.31 (102) 5851.00, 0.149 3.66, 0.072
Information rate 1.25 ± 0.05 (129) 0.96 ± 0.06 (102) 4646.00, 0.000 0.29, 0.001
Interspike interval cv 2.40 ± 0.07 (129) 2.22 ± 0.08 (102) 5516.00, 0.035 0.14, 0.270
In-field mean rate 14.72 ± 0.82 (129) 12.94 ± 0.71 (102) 6026.00, 0.273 0.76, 0.414
Out-field mean rate 6.35 ± 0.60 (129) 6.12 ± 0.53 (102) 6535.00, 0.931 0.08, 0.921
Burst event ratio 0.21 ± 0.01 (129) 0.20 ± 0.01 (102) 5792.00, 0.119 0.02, 0.071
Specificity 0.48 ± 0.02 (129) 0.46 ± 0.02 (102) 5962.00, 0.222 0.06, 0.181
Speed score 0.14 ± 0.01 (129) 0.10 ± 0.01 (102) 5128.00, 0.004 0.02, 0.008
In [21]:
_stim_data = stimulated_11
_base_data = baseline_i

result = pd.DataFrame()

result['Baseline'] = _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 11 Hz MWU PRS
Average rate 8.96 ± 0.80 (63) 8.80 ± 0.85 (58) 1781.00, 0.813 0.04, 0.969
Gridness 0.53 ± 0.05 (63) 0.41 ± 0.05 (58) 1459.00, 0.057 0.21, 0.038
Sparsity 0.63 ± 0.02 (63) 0.67 ± 0.03 (58) 2138.00, 0.107 0.07, 0.126
Selectivity 5.76 ± 0.40 (63) 5.69 ± 0.50 (58) 1687.00, 0.469 0.00, 0.981
Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.03 (58) 1452.00, 0.052 0.06, 0.031
Max rate 37.39 ± 1.91 (63) 33.11 ± 1.85 (58) 1538.00, 0.134 4.06, 0.128
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, 0.683
Out-field mean rate 6.37 ± 0.67 (63) 6.57 ± 0.77 (58) 1795.00, 0.870 0.47, 0.719
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'] = _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 30 Hz MWU PRS
Average rate 8.29 ± 0.87 (52) 7.61 ± 0.87 (38) 958.00, 0.810 0.27, 0.805
Gridness 0.54 ± 0.04 (52) 0.48 ± 0.06 (38) 914.00, 0.548 0.04, 0.608
Sparsity 0.63 ± 0.03 (52) 0.64 ± 0.03 (38) 1040.00, 0.674 0.06, 0.401
Selectivity 5.96 ± 0.46 (52) 6.42 ± 0.60 (38) 1019.00, 0.803 0.20, 0.850
Information specificity 0.21 ± 0.02 (52) 0.22 ± 0.03 (38) 950.00, 0.759 0.04, 0.505
Max rate 36.27 ± 2.34 (52) 33.49 ± 1.89 (38) 943.00, 0.716 2.90, 0.558
Information rate 1.13 ± 0.08 (52) 0.98 ± 0.09 (38) 827.00, 0.190 0.07, 0.332
Interspike interval cv 2.37 ± 0.09 (52) 2.23 ± 0.11 (38) 869.00, 0.333 0.17, 0.470
In-field mean rate 13.79 ± 1.12 (52) 12.21 ± 0.98 (38) 912.00, 0.537 1.06, 0.452
Out-field mean rate 5.80 ± 0.72 (52) 5.36 ± 0.73 (38) 959.00, 0.816 0.13, 0.916
Burst event ratio 0.20 ± 0.01 (52) 0.16 ± 0.01 (38) 676.00, 0.011 0.05, 0.007
Specificity 0.47 ± 0.03 (52) 0.48 ± 0.04 (38) 976.00, 0.925 0.00, 0.985
Speed score 0.12 ± 0.01 (52) 0.11 ± 0.01 (38) 784.00, 0.096 0.01, 0.241
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, 0.803
Gridness 0.53 ± 0.05 (63) 0.48 ± 0.06 (38) 1094.00, 0.472 0.08, 0.363
Sparsity 0.63 ± 0.02 (63) 0.64 ± 0.03 (38) 1261.00, 0.656 0.03, 0.641
Selectivity 5.76 ± 0.40 (63) 6.42 ± 0.60 (38) 1276.00, 0.582 0.86, 0.283
Information specificity 0.24 ± 0.03 (63) 0.22 ± 0.03 (38) 1076.00, 0.398 0.05, 0.161
Max rate 37.39 ± 1.91 (63) 33.49 ± 1.89 (38) 1027.00, 0.235 3.99, 0.182
Information rate 1.31 ± 0.08 (63) 0.98 ± 0.09 (38) 797.00, 0.005 0.32, 0.045
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, 0.272
Out-field mean rate 6.37 ± 0.67 (63) 5.36 ± 0.73 (38) 1079.00, 0.410 0.51, 0.644
Burst event ratio 0.22 ± 0.01 (63) 0.16 ± 0.01 (38) 675.00, 0.000 0.05, 0.006
Specificity 0.47 ± 0.03 (63) 0.48 ± 0.04 (38) 1206.00, 0.952 0.01, 0.869
Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (38) 835.00, 0.011 0.06, 0.005
In [ ]:
_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
In [ ]:
_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

Violinplot

In [ ]:
%matplotlib inline
plt.rc('axes', titlesize=12)
plt.rcParams.update({
    'font.size': 12, 
    'figure.figsize': (1.7, 3), 
    'figure.dpi': 150
})
In [ ]:
# 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')
    },
    '_11': {
        'base': baseline_i,
        'stim': stimulated_11
    },
    '_30': {
        'base': baseline_ii,
        'stim': stimulated_30
    }
}

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

colors = {
    '': None,
    '_11': ['#1b9e77', '#d95f02'],
    '_30': ['#7570b3', '#e7298a']
}

Information rate

In [ ]:
for key, data in stuff.items():
    baseline = data['base']['information_specificity'].to_numpy()
    stimulated = data['stim']['information_specificity'].to_numpy()
    print(key)
    plt.figure()
    violinplot(baseline, stimulated, xticks=label[key], 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")
In [ ]:
for key, data in stuff.items():
    baseline = data['base']['information_rate'].to_numpy()
    stimulated = data['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")
In [ ]:
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], 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")
In [ ]:
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], 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")
In [ ]:
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], 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")
In [ ]:
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], 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")
In [ ]:
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], 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")
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], 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")
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], 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")
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], 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")
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], 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")
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], 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")
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], 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")
In [ ]:
# fig, (ax1, ax2) = plt.subplots(2,1, figsize=(6,6), sharey=True)
# for key, data in stuff.items():
#     ax1.set_title('Baseline')
#     peak_rate = data['base']['max_rate'].to_numpy()
#     spacing = data['base']['spacing'].to_numpy()
#     ax1.scatter(spacing, peak_rate)
    
#     ax2.set_title('Stim')
#     peak_rate = data['stim']['max_rate'].to_numpy()
#     spacing = data['stim']['spacing'].to_numpy()
#     ax2.scatter(spacing, peak_rate, label=key)
    
# ax2.legend()

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 [ ]: