In [1]:
import pandas as pd
Clone the forked repository to a local directory
Install miniconda (or anaconda) if it isn't already installed. Type into bash:
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r
Switch to the development branch. Type into bash:
git checkout develop
Go to the local repository (.../Jadhav-2016-Data-Analysis) and install the anaconda environment for the repository. Type into bash:
conda update -q conda
conda info -a
conda env create -f environment.yml
source activate Jadhav-2016-Data-Analysis
python setup.py develop
Make sure the environment is set up correctly by running the tests. Type into bash:
pytest
Copy the data folders HPa_direct, HPb_direct, HPc_direct from the dropbox folder EastWestSideHippos Team Folder/HCPFCdata to Jadhav-2016-Data-Analysis/Raw-Data
The src.parameters module has some convenient constants for accessing the data. Most important are the ANIMALS dictionary, the N_DAYS, and the SAMPLING_FREQUENCY.
The ANIMALS dictionary maps the animal name to the data directory containing the animal's data.
In [2]:
from src.parameters import ANIMALS
ANIMALS
Out[2]:
{'HPa': Animal(short_name='HPa', directory='HPa_direct'),
'HPb': Animal(short_name='HPb', directory='HPb_direct'),
'HPc': Animal(short_name='HPc', directory='HPc_direct')}
N_DAYS corresponds to the number of days of recording and SAMPLING_FREQUENCY corresponds to the sampling rate of the tetrodes recording neural activity
In [3]:
from src.parameters import N_DAYS, SAMPLING_FREQUENCY
print('Days: {0}'.format(N_DAYS))
print('Sampling Frequency: {0}'.format(SAMPLING_FREQUENCY))
Days: 8
Sampling Frequency: 1500
The src.data_processing module has convenient functions for importing the data files into Pandas dataframes. You can use these functions in conjunction with the constants from the src.parameters module. The most useful functions are:
make_epochs_dataframe: returns descriptive information about each epochmake_tetrode_dataframe: returns descriptive information about each tetrodemake_neuron_dataframe: returns descriptive information about each neuron get_spike_indicator_dataframe: returns the spiking data for each neuron for each time pointget_interpolated_position_dataframe: returns data about the position of animal for each time pointLet's take each of these functions in turn.
make_epochs_dataframeAn epoch is a single recording session. There are typically multiple recording sessions (epochs) a day. The epoch dataframe gives information about what task the animal is performing during the epoch (sleeping, running a linear track, running a w-track, resting, etc). Each row of the epoch dataframe corresponds to an epoch.
In [4]:
from src.data_processing import make_epochs_dataframe
days = range(1, N_DAYS + 1)
epoch_info = make_epochs_dataframe(ANIMALS, days)
epoch_info
Out[4]:
animal
day
epoch
environment
type
animal
day
epoch
HPa
1
1
HPa
1
1
presleep
sleep
2
HPa
1
2
lin
run
3
HPa
1
3
NaN
rest
4
HPa
1
4
wtr1
run
5
HPa
1
5
NaN
rest
6
HPa
1
6
wtr1
run
7
HPa
1
7
postsleep
sleep
2
1
HPa
2
1
presleep
sleep
2
HPa
2
2
wtr1
run
3
HPa
2
3
NaN
rest
4
HPa
2
4
wtr1
run
5
HPa
2
5
postsleep
sleep
3
1
HPa
3
1
presleep
sleep
2
HPa
3
2
wtr1
run
3
HPa
3
3
NaN
rest
4
HPa
3
4
wtr1
run
5
HPa
3
5
postsleep
sleep
4
1
HPa
4
1
presleep
sleep
2
HPa
4
2
wtr1
run
3
HPa
4
3
NaN
rest
4
HPa
4
4
wtr1
run
5
HPa
4
5
postsleep
sleep
5
1
HPa
5
1
presleep
sleep
2
HPa
5
2
wtr1
run
3
HPa
5
3
NaN
rest
4
HPa
5
4
wtr1
run
5
HPa
5
5
postsleep
sleep
6
1
HPa
6
1
presleep
sleep
2
HPa
6
2
wtr1
run
3
HPa
6
3
NaN
rest
...
...
...
...
...
...
...
...
HPc
3
1
HPc
3
1
presleep
sleep
2
HPc
3
2
wtr1
run
3
HPc
3
3
NaN
rest
4
HPc
3
4
wtr1
run
5
HPc
3
5
postsleep
sleep
4
1
HPc
4
1
presleep
sleep
2
HPc
4
2
wtr1
run
3
HPc
4
3
NaN
rest
4
HPc
4
4
wtr1
run
5
HPc
4
5
postsleep
sleep
5
1
HPc
5
1
presleep
sleep
2
HPc
5
2
wtr1
run
3
HPc
5
3
NaN
rest
4
HPc
5
4
wtr1
run
5
HPc
5
5
postsleep
sleep
6
1
HPc
6
1
presleep
sleep
2
HPc
6
2
wtr1
run
3
HPc
6
3
NaN
rest
4
HPc
6
4
wtr2
run
5
HPc
6
5
postsleep
sleep
7
1
HPc
7
1
presleep
sleep
2
HPc
7
2
wtr1
run
3
HPc
7
3
NaN
rest
4
HPc
7
4
wtr2
run
5
HPc
7
5
postsleep
sleep
8
1
HPc
8
1
presleep
sleep
2
HPc
8
2
wtr1
run
3
HPc
8
3
NaN
rest
4
HPc
8
4
wtr2
run
5
HPc
8
5
postsleep
sleep
126 rows × 5 columns
Often we use the epoch as a key to access more information about that epoch (information about the tetrodes, neuron, etc). Epoch keys are tuples with the following format: (Animal, Day, Epoch).
The index of the epoch dataframe can be used to produce these keys.
In [5]:
epoch_info.index.tolist()
Out[5]:
[('HPa', 1, 1),
('HPa', 1, 2),
('HPa', 1, 3),
('HPa', 1, 4),
('HPa', 1, 5),
('HPa', 1, 6),
('HPa', 1, 7),
('HPa', 2, 1),
('HPa', 2, 2),
('HPa', 2, 3),
('HPa', 2, 4),
('HPa', 2, 5),
('HPa', 3, 1),
('HPa', 3, 2),
('HPa', 3, 3),
('HPa', 3, 4),
('HPa', 3, 5),
('HPa', 4, 1),
('HPa', 4, 2),
('HPa', 4, 3),
('HPa', 4, 4),
('HPa', 4, 5),
('HPa', 5, 1),
('HPa', 5, 2),
('HPa', 5, 3),
('HPa', 5, 4),
('HPa', 5, 5),
('HPa', 6, 1),
('HPa', 6, 2),
('HPa', 6, 3),
('HPa', 6, 4),
('HPa', 6, 5),
('HPa', 7, 1),
('HPa', 7, 2),
('HPa', 7, 3),
('HPa', 7, 4),
('HPa', 7, 5),
('HPa', 8, 1),
('HPa', 8, 2),
('HPa', 8, 3),
('HPa', 8, 4),
('HPa', 8, 5),
('HPb', 1, 1),
('HPb', 1, 2),
('HPb', 1, 3),
('HPb', 1, 4),
('HPb', 1, 5),
('HPb', 1, 6),
('HPb', 1, 7),
('HPb', 2, 1),
('HPb', 2, 2),
('HPb', 2, 3),
('HPb', 2, 4),
('HPb', 2, 5),
('HPb', 3, 1),
('HPb', 3, 2),
('HPb', 3, 3),
('HPb', 3, 4),
('HPb', 3, 5),
('HPb', 4, 1),
('HPb', 4, 2),
('HPb', 4, 3),
('HPb', 4, 4),
('HPb', 4, 5),
('HPb', 5, 1),
('HPb', 5, 2),
('HPb', 5, 3),
('HPb', 5, 4),
('HPb', 5, 5),
('HPb', 6, 1),
('HPb', 6, 2),
('HPb', 6, 3),
('HPb', 6, 4),
('HPb', 6, 5),
('HPb', 7, 1),
('HPb', 7, 2),
('HPb', 7, 3),
('HPb', 7, 4),
('HPb', 7, 5),
('HPb', 8, 1),
('HPb', 8, 2),
('HPb', 8, 3),
('HPb', 8, 4),
('HPb', 8, 5),
('HPc', 1, 1),
('HPc', 1, 2),
('HPc', 1, 3),
('HPc', 1, 4),
('HPc', 1, 5),
('HPc', 1, 6),
('HPc', 1, 7),
('HPc', 2, 1),
('HPc', 2, 2),
('HPc', 2, 3),
('HPc', 2, 4),
('HPc', 2, 5),
('HPc', 3, 1),
('HPc', 3, 2),
('HPc', 3, 3),
('HPc', 3, 4),
('HPc', 3, 5),
('HPc', 4, 1),
('HPc', 4, 2),
('HPc', 4, 3),
('HPc', 4, 4),
('HPc', 4, 5),
('HPc', 5, 1),
('HPc', 5, 2),
('HPc', 5, 3),
('HPc', 5, 4),
('HPc', 5, 5),
('HPc', 6, 1),
('HPc', 6, 2),
('HPc', 6, 3),
('HPc', 6, 4),
('HPc', 6, 5),
('HPc', 7, 1),
('HPc', 7, 2),
('HPc', 7, 3),
('HPc', 7, 4),
('HPc', 7, 5),
('HPc', 8, 1),
('HPc', 8, 2),
('HPc', 8, 3),
('HPc', 8, 4),
('HPc', 8, 5)]
This is useful if we want to filter the epoch dataframe by a particular attribute (Say we only want sessions where the animal is asleep) and use the keys to access the data for that epoch.
In [6]:
epoch_info.loc[epoch_info.type == 'run']
Out[6]:
animal
day
epoch
environment
type
animal
day
epoch
HPa
1
2
HPa
1
2
lin
run
4
HPa
1
4
wtr1
run
6
HPa
1
6
wtr1
run
2
2
HPa
2
2
wtr1
run
4
HPa
2
4
wtr1
run
3
2
HPa
3
2
wtr1
run
4
HPa
3
4
wtr1
run
4
2
HPa
4
2
wtr1
run
4
HPa
4
4
wtr1
run
5
2
HPa
5
2
wtr1
run
4
HPa
5
4
wtr1
run
6
2
HPa
6
2
wtr1
run
4
HPa
6
4
wtr2
run
7
2
HPa
7
2
wtr1
run
4
HPa
7
4
wtr2
run
8
2
HPa
8
2
wtr1
run
4
HPa
8
4
wtr2
run
HPb
1
2
HPb
1
2
lin
run
4
HPb
1
4
wtr1
run
6
HPb
1
6
wtr1
run
2
2
HPb
2
2
wtr1
run
4
HPb
2
4
wtr1
run
3
2
HPb
3
2
wtr1
run
4
HPb
3
4
wtr1
run
4
2
HPb
4
2
wtr1
run
4
HPb
4
4
wtr1
run
5
2
HPb
5
2
wtr1
run
4
HPb
5
4
wtr1
run
6
2
HPb
6
2
wtr1
run
4
HPb
6
4
wtr2
run
7
2
HPb
7
2
wtr1
run
4
HPb
7
4
wtr2
run
8
2
HPb
8
2
wtr1
run
4
HPb
8
4
wtr2
run
HPc
1
2
HPc
1
2
lin
run
4
HPc
1
4
wtr1
run
6
HPc
1
6
wtr1
run
2
2
HPc
2
2
wtr1
run
4
HPc
2
4
wtr1
run
3
2
HPc
3
2
wtr1
run
4
HPc
3
4
wtr1
run
4
2
HPc
4
2
wtr1
run
4
HPc
4
4
wtr1
run
5
2
HPc
5
2
wtr1
run
4
HPc
5
4
wtr1
run
6
2
HPc
6
2
wtr1
run
4
HPc
6
4
wtr2
run
7
2
HPc
7
2
wtr1
run
4
HPc
7
4
wtr2
run
8
2
HPc
8
2
wtr1
run
4
HPc
8
4
wtr2
run
In [7]:
epoch_info.loc[epoch_info.type == 'run'].index.tolist()
Out[7]:
[('HPa', 1, 2),
('HPa', 1, 4),
('HPa', 1, 6),
('HPa', 2, 2),
('HPa', 2, 4),
('HPa', 3, 2),
('HPa', 3, 4),
('HPa', 4, 2),
('HPa', 4, 4),
('HPa', 5, 2),
('HPa', 5, 4),
('HPa', 6, 2),
('HPa', 6, 4),
('HPa', 7, 2),
('HPa', 7, 4),
('HPa', 8, 2),
('HPa', 8, 4),
('HPb', 1, 2),
('HPb', 1, 4),
('HPb', 1, 6),
('HPb', 2, 2),
('HPb', 2, 4),
('HPb', 3, 2),
('HPb', 3, 4),
('HPb', 4, 2),
('HPb', 4, 4),
('HPb', 5, 2),
('HPb', 5, 4),
('HPb', 6, 2),
('HPb', 6, 4),
('HPb', 7, 2),
('HPb', 7, 4),
('HPb', 8, 2),
('HPb', 8, 4),
('HPc', 1, 2),
('HPc', 1, 4),
('HPc', 1, 6),
('HPc', 2, 2),
('HPc', 2, 4),
('HPc', 3, 2),
('HPc', 3, 4),
('HPc', 4, 2),
('HPc', 4, 4),
('HPc', 5, 2),
('HPc', 5, 4),
('HPc', 6, 2),
('HPc', 6, 4),
('HPc', 7, 2),
('HPc', 7, 4),
('HPc', 8, 2),
('HPc', 8, 4)]
make_tetrode_dataframemake_tetrode_dataframe is a dictionary of pandas dataframes, where the keys corresponds to an epoch and the values are pandas dataframes detailing information about the tetrodes in that epoch (brain area, number of cells recorded, etc.).
The dictionary keys for the epochs are tuples (Animal, Day, Epoch). For example, let's load the tetrode dataframe and display all the keys:
In [8]:
from src.data_processing import make_tetrode_dataframe
tetrode_info = make_tetrode_dataframe(ANIMALS)
list(tetrode_info.keys())
Out[8]:
[('HPa', 2, 1),
('HPa', 8, 2),
('HPa', 7, 5),
('HPb', 6, 4),
('HPb', 6, 2),
('HPa', 2, 2),
('HPa', 8, 3),
('HPa', 7, 4),
('HPc', 2, 4),
('HPb', 1, 2),
('HPa', 2, 3),
('HPb', 3, 4),
('HPc', 2, 5),
('HPa', 2, 4),
('HPa', 8, 1),
('HPa', 5, 5),
('HPa', 2, 5),
('HPb', 3, 2),
('HPa', 5, 4),
('HPc', 4, 4),
('HPb', 7, 2),
('HPb', 3, 3),
('HPc', 4, 5),
('HPa', 8, 4),
('HPb', 7, 5),
('HPa', 3, 5),
('HPa', 8, 5),
('HPb', 5, 4),
('HPa', 5, 1),
('HPa', 3, 4),
('HPb', 7, 4),
('HPa', 3, 3),
('HPc', 3, 1),
('HPb', 8, 4),
('HPa', 5, 3),
('HPc', 4, 1),
('HPa', 7, 2),
('HPb', 5, 1),
('HPb', 5, 3),
('HPa', 5, 2),
('HPa', 3, 1),
('HPb', 6, 1),
('HPb', 4, 2),
('HPc', 3, 3),
('HPc', 4, 3),
('HPc', 3, 2),
('HPb', 8, 3),
('HPc', 3, 5),
('HPb', 5, 5),
('HPb', 8, 2),
('HPb', 2, 5),
('HPc', 3, 4),
('HPb', 8, 1),
('HPb', 2, 4),
('HPc', 6, 4),
('HPc', 8, 2),
('HPb', 2, 2),
('HPc', 6, 5),
('HPb', 4, 5),
('HPa', 6, 4),
('HPc', 6, 2),
('HPb', 4, 4),
('HPa', 6, 5),
('HPc', 5, 3),
('HPb', 8, 5),
('HPc', 6, 3),
('HPb', 4, 3),
('HPc', 5, 2),
('HPb', 3, 5),
('HPb', 2, 3),
('HPb', 1, 4),
('HPc', 5, 1),
('HPc', 6, 1),
('HPb', 4, 1),
('HPb', 7, 1),
('HPa', 1, 1),
('HPa', 6, 1),
('HPa', 3, 2),
('HPb', 6, 3),
('HPb', 3, 1),
('HPa', 6, 2),
('HPb', 1, 3),
('HPa', 1, 3),
('HPa', 6, 3),
('HPc', 5, 5),
('HPa', 1, 2),
('HPb', 5, 2),
('HPc', 5, 4),
('HPb', 7, 3),
('HPa', 1, 5),
('HPb', 1, 5),
('HPa', 1, 4),
('HPc', 7, 5),
('HPa', 1, 7),
('HPa', 4, 4),
('HPc', 7, 4),
('HPc', 1, 7),
('HPa', 1, 6),
('HPa', 4, 5),
('HPb', 1, 6),
('HPc', 1, 6),
('HPa', 4, 2),
('HPb', 1, 7),
('HPc', 1, 5),
('HPa', 7, 3),
('HPa', 4, 3),
('HPc', 7, 1),
('HPc', 1, 4),
('HPc', 8, 1),
('HPc', 2, 2),
('HPb', 1, 1),
('HPc', 1, 3),
('HPa', 7, 1),
('HPc', 2, 3),
('HPa', 4, 1),
('HPc', 7, 3),
('HPc', 1, 2),
('HPc', 8, 3),
('HPc', 4, 2),
('HPc', 7, 2),
('HPc', 1, 1),
('HPb', 2, 1),
('HPc', 8, 4),
('HPc', 2, 1),
('HPc', 8, 5),
('HPb', 6, 5)]
If we want to access a particular epoch we can just pass the corresponding epoch tuple. In this case, we want animal HPa, day 6, epoch 2. This returns a dataframe where each row corresponds to a tetrode for that epoch.
In [9]:
epoch_key = ('HPa', 6, 2)
tetrode_info[epoch_key]
Out[9]:
area
depth
descrip
numcells
animal
day
epoch
tetrode_number
tetrode_id
animal
day
epoch
tetrode_number
HPa
6
2
1
CA1
114
riptet
7
HPa
6
2
1
HPa621
2
CA1
121
NaN
0
HPa
6
2
2
HPa622
3
CA1
90
CA1Ref
0
HPa
6
2
3
HPa623
4
CA1
117
riptet
8
HPa
6
2
4
HPa624
5
CA1
117
riptet
5
HPa
6
2
5
HPa625
6
CA1
110
riptet
0
HPa
6
2
6
HPa626
7
CA1
114
riptet
0
HPa
6
2
7
HPa627
8
iCA1
114
riptet
1
HPa
6
2
8
HPa628
9
iCA1
101
riptet
1
HPa
6
2
9
HPa629
10
iCA1
98
NaN
1
HPa
6
2
10
HPa6210
11
iCA1
108
riptet
1
HPa
6
2
11
HPa6211
12
iCA1
116
riptet
1
HPa
6
2
12
HPa6212
13
iCA1
120
NaN
0
HPa
6
2
13
HPa6213
14
iCA1
105
riptet
1
HPa
6
2
14
HPa6214
15
PFC
120
NaN
0
HPa
6
2
15
HPa6215
16
PFC
123
NaN
0
HPa
6
2
16
HPa6216
17
PFC
123
NaN
1
HPa
6
2
17
HPa6217
18
PFC
112
NaN
1
HPa
6
2
18
HPa6218
19
PFC
130
NaN
0
HPa
6
2
19
HPa6219
20
PFC
109
NaN
0
HPa
6
2
20
HPa6220
Remember that the epoch dataframe index can be used as keys, which can be useful if we want to access a particular epoch.
In [10]:
[tetrode_info[epoch_key]
for epoch_key in epoch_info.loc[
(epoch_info.type == 'sleep') & (epoch_info.day == 8)].index]
Out[10]:
[ area depth descrip numcells animal day \
animal day epoch tetrode_number
HPa 8 1 1 CA1 113 riptet 12 HPa 8
2 CA1 121 NaN 0 HPa 8
3 CA1 90 CA1Ref 0 HPa 8
4 CA1 116 riptet 15 HPa 8
5 CA1 116 riptet 0 HPa 8
6 CA1 110 riptet 0 HPa 8
7 CA1 114 riptet 0 HPa 8
8 iCA1 114 riptet 0 HPa 8
9 iCA1 100 riptet 0 HPa 8
10 iCA1 96 NaN 0 HPa 8
11 iCA1 106 riptet 0 HPa 8
12 iCA1 114 riptet 3 HPa 8
13 iCA1 120 NaN 0 HPa 8
14 iCA1 105 riptet 6 HPa 8
15 PFC 93 NaN 1 HPa 8
16 PFC 90 NaN 0 HPa 8
17 PFC 90 NaN 6 HPa 8
18 PFC 90 NaN 0 HPa 8
19 PFC 130 NaN 0 HPa 8
20 PFC 109 NaN 0 HPa 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPa 8 1 1 1 1 HPa811
2 1 2 HPa812
3 1 3 HPa813
4 1 4 HPa814
5 1 5 HPa815
6 1 6 HPa816
7 1 7 HPa817
8 1 8 HPa818
9 1 9 HPa819
10 1 10 HPa8110
11 1 11 HPa8111
12 1 12 HPa8112
13 1 13 HPa8113
14 1 14 HPa8114
15 1 15 HPa8115
16 1 16 HPa8116
17 1 17 HPa8117
18 1 18 HPa8118
19 1 19 HPa8119
20 1 20 HPa8120 ,
area depth descrip numcells animal day \
animal day epoch tetrode_number
HPa 8 5 1 CA1 113 riptet 12 HPa 8
2 CA1 121 NaN 0 HPa 8
3 CA1 90 CA1Ref 0 HPa 8
4 CA1 116 riptet 15 HPa 8
5 CA1 116 riptet 0 HPa 8
6 CA1 110 riptet 0 HPa 8
7 CA1 114 riptet 0 HPa 8
8 iCA1 114 riptet 0 HPa 8
9 iCA1 100 riptet 0 HPa 8
10 iCA1 96 NaN 0 HPa 8
11 iCA1 106 riptet 0 HPa 8
12 iCA1 114 riptet 3 HPa 8
13 iCA1 120 NaN 0 HPa 8
14 iCA1 105 riptet 6 HPa 8
15 PFC 93 NaN 1 HPa 8
16 PFC 90 NaN 0 HPa 8
17 PFC 90 NaN 6 HPa 8
18 PFC 90 NaN 0 HPa 8
19 PFC 130 NaN 0 HPa 8
20 PFC 109 NaN 0 HPa 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPa 8 5 1 5 1 HPa851
2 5 2 HPa852
3 5 3 HPa853
4 5 4 HPa854
5 5 5 HPa855
6 5 6 HPa856
7 5 7 HPa857
8 5 8 HPa858
9 5 9 HPa859
10 5 10 HPa8510
11 5 11 HPa8511
12 5 12 HPa8512
13 5 13 HPa8513
14 5 14 HPa8514
15 5 15 HPa8515
16 5 16 HPa8516
17 5 17 HPa8517
18 5 18 HPa8518
19 5 19 HPa8519
20 5 20 HPa8520 ,
area depth descrip numcells animal day \
animal day epoch tetrode_number
HPb 8 1 1 CA1 85 riptet 2 HPb 8
2 CA1 61 NaN 0 HPb 8
3 CA1 69 riptet 2 HPb 8
4 CA1 74 riptet 8 HPb 8
5 CA1 102 riptet 0 HPb 8
6 CA1 67 riptet 0 HPb 8
7 CA1 90 CA1Ref 0 HPb 8
8 PFC 131 NaN 2 HPb 8
9 PFC 98 NaN 1 HPb 8
10 PFC 72 NaN 7 HPb 8
11 PFC 66 NaN 0 HPb 8
12 PFC 84 NaN 1 HPb 8
13 PFC 60 PFCRef 0 HPb 8
14 PFC 99 NaN 3 HPb 8
15 iCA1 96 iCA1Ref 0 HPb 8
16 iCA1 113 riptet 0 HPb 8
17 iCA1 121 riptet 0 HPb 8
18 iCA1 113 riptet 3 HPb 8
19 iCA1 123 NaN 0 HPb 8
20 iCA1 133 riptet 1 HPb 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPb 8 1 1 1 1 HPb811
2 1 2 HPb812
3 1 3 HPb813
4 1 4 HPb814
5 1 5 HPb815
6 1 6 HPb816
7 1 7 HPb817
8 1 8 HPb818
9 1 9 HPb819
10 1 10 HPb8110
11 1 11 HPb8111
12 1 12 HPb8112
13 1 13 HPb8113
14 1 14 HPb8114
15 1 15 HPb8115
16 1 16 HPb8116
17 1 17 HPb8117
18 1 18 HPb8118
19 1 19 HPb8119
20 1 20 HPb8120 ,
area depth descrip numcells animal day \
animal day epoch tetrode_number
HPb 8 5 1 CA1 85 riptet 2 HPb 8
2 CA1 61 NaN 0 HPb 8
3 CA1 69 riptet 2 HPb 8
4 CA1 74 riptet 8 HPb 8
5 CA1 102 riptet 0 HPb 8
6 CA1 67 riptet 0 HPb 8
7 CA1 90 CA1Ref 0 HPb 8
8 PFC 131 NaN 2 HPb 8
9 PFC 98 NaN 0 HPb 8
10 PFC 72 NaN 7 HPb 8
11 PFC 66 NaN 0 HPb 8
12 PFC 84 NaN 1 HPb 8
13 PFC 60 PFCRef 0 HPb 8
14 PFC 99 NaN 3 HPb 8
15 iCA1 96 iCA1Ref 0 HPb 8
16 iCA1 113 riptet 0 HPb 8
17 iCA1 121 riptet 0 HPb 8
18 iCA1 113 riptet 3 HPb 8
19 iCA1 123 NaN 0 HPb 8
20 iCA1 133 riptet 0 HPb 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPb 8 5 1 5 1 HPb851
2 5 2 HPb852
3 5 3 HPb853
4 5 4 HPb854
5 5 5 HPb855
6 5 6 HPb856
7 5 7 HPb857
8 5 8 HPb858
9 5 9 HPb859
10 5 10 HPb8510
11 5 11 HPb8511
12 5 12 HPb8512
13 5 13 HPb8513
14 5 14 HPb8514
15 5 15 HPb8515
16 5 16 HPb8516
17 5 17 HPb8517
18 5 18 HPb8518
19 5 19 HPb8519
20 5 20 HPb8520 ,
area depth descrip numcells animal day \
animal day epoch tetrode_number
HPc 8 1 1 CA1 108 riptet 0 HPc 8
2 CA1 100 riptet 1 HPc 8
3 CA1 91 riptet 5 HPc 8
4 CA1 104 riptet 0 HPc 8
5 CA1 96 riptet 0 HPc 8
6 CA1 106 riptet 3 HPc 8
7 CA1 84 CA1Ref 0 HPc 8
8 PFC 65 NaN 0 HPc 8
9 PFC 60 NaN 0 HPc 8
10 PFC 57 NaN 0 HPc 8
11 PFC 98 NaN 0 HPc 8
12 PFC 102 NaN 0 HPc 8
13 PFC 45 PFCRef 0 HPc 8
14 PFC 66 NaN 0 HPc 8
15 PFC 71 NaN 7 HPc 8
16 PFC 104 NaN 1 HPc 8
17 Ctx 282 CtxRef 0 HPc 8
18 Ctx 315 NaN 0 HPc 8
19 Ctx 318 NaN 0 HPc 8
20 Ctx 300 NaN 0 HPc 8
21 Ctx 196 NaN 0 HPc 8
22 Ctx 0 NaN 0 HPc 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPc 8 1 1 1 1 HPc811
2 1 2 HPc812
3 1 3 HPc813
4 1 4 HPc814
5 1 5 HPc815
6 1 6 HPc816
7 1 7 HPc817
8 1 8 HPc818
9 1 9 HPc819
10 1 10 HPc8110
11 1 11 HPc8111
12 1 12 HPc8112
13 1 13 HPc8113
14 1 14 HPc8114
15 1 15 HPc8115
16 1 16 HPc8116
17 1 17 HPc8117
18 1 18 HPc8118
19 1 19 HPc8119
20 1 20 HPc8120
21 1 21 HPc8121
22 1 22 HPc8122 ,
area depth descrip numcells animal day \
animal day epoch tetrode_number
HPc 8 5 1 CA1 108 riptet 0 HPc 8
2 CA1 100 riptet 1 HPc 8
3 CA1 91 riptet 5 HPc 8
4 CA1 104 riptet 0 HPc 8
5 CA1 96 riptet 0 HPc 8
6 CA1 106 riptet 3 HPc 8
7 CA1 84 CA1Ref 0 HPc 8
8 PFC 65 NaN 0 HPc 8
9 PFC 60 NaN 0 HPc 8
10 PFC 57 NaN 0 HPc 8
11 PFC 98 NaN 0 HPc 8
12 PFC 102 NaN 0 HPc 8
13 PFC 45 PFCRef 0 HPc 8
14 PFC 66 NaN 0 HPc 8
15 PFC 71 NaN 7 HPc 8
16 PFC 104 NaN 1 HPc 8
17 Ctx 282 CtxRef 0 HPc 8
18 Ctx 315 NaN 0 HPc 8
19 Ctx 318 NaN 0 HPc 8
20 Ctx 300 NaN 0 HPc 8
21 Ctx 196 NaN 0 HPc 8
22 Ctx 0 NaN 0 HPc 8
epoch tetrode_number tetrode_id
animal day epoch tetrode_number
HPc 8 5 1 5 1 HPc851
2 5 2 HPc852
3 5 3 HPc853
4 5 4 HPc854
5 5 5 HPc855
6 5 6 HPc856
7 5 7 HPc857
8 5 8 HPc858
9 5 9 HPc859
10 5 10 HPc8510
11 5 11 HPc8511
12 5 12 HPc8512
13 5 13 HPc8513
14 5 14 HPc8514
15 5 15 HPc8515
16 5 16 HPc8516
17 5 17 HPc8517
18 5 18 HPc8518
19 5 19 HPc8519
20 5 20 HPc8520
21 5 21 HPc8521
22 5 22 HPc8522 ]
make_neuron_dataframeThe neuron dataframe is set up similarly to the tetrode dataframe. It is a dictionary of pandas dataframes, where the keys are the epoch tuples (Animal, Day, Epoch) and the values are the pandas dataframes. The dataframes give descriptive information about the number of spikes, the average spiking rate, which brain area, etc.
In [11]:
from src.data_processing import make_neuron_dataframe
neuron_info = make_neuron_dataframe(ANIMALS)
list(neuron_info.keys())
Out[11]:
[('HPa', 2, 1),
('HPa', 8, 2),
('HPa', 7, 5),
('HPb', 6, 4),
('HPb', 6, 2),
('HPa', 2, 2),
('HPa', 8, 3),
('HPa', 7, 4),
('HPc', 2, 4),
('HPb', 1, 2),
('HPa', 2, 3),
('HPb', 3, 4),
('HPc', 2, 5),
('HPa', 2, 4),
('HPa', 8, 1),
('HPa', 5, 5),
('HPa', 2, 5),
('HPb', 3, 2),
('HPa', 5, 4),
('HPc', 4, 4),
('HPa', 2, 6),
('HPb', 3, 3),
('HPc', 4, 5),
('HPa', 2, 7),
('HPa', 8, 4),
('HPb', 7, 5),
('HPb', 7, 2),
('HPa', 3, 5),
('HPa', 8, 5),
('HPb', 5, 4),
('HPa', 5, 1),
('HPa', 3, 4),
('HPb', 7, 4),
('HPa', 3, 3),
('HPc', 3, 1),
('HPb', 8, 4),
('HPa', 5, 3),
('HPc', 4, 1),
('HPa', 7, 2),
('HPb', 5, 1),
('HPb', 5, 3),
('HPa', 5, 2),
('HPa', 3, 1),
('HPb', 6, 1),
('HPb', 4, 2),
('HPc', 3, 3),
('HPc', 4, 3),
('HPc', 3, 2),
('HPb', 8, 3),
('HPc', 3, 5),
('HPb', 5, 5),
('HPb', 8, 2),
('HPb', 2, 5),
('HPc', 3, 4),
('HPb', 8, 1),
('HPb', 2, 4),
('HPc', 6, 4),
('HPc', 8, 2),
('HPb', 2, 2),
('HPc', 6, 5),
('HPb', 4, 5),
('HPa', 6, 4),
('HPc', 6, 2),
('HPb', 4, 4),
('HPa', 6, 5),
('HPc', 5, 3),
('HPb', 8, 5),
('HPc', 6, 3),
('HPb', 4, 3),
('HPc', 5, 2),
('HPb', 3, 5),
('HPb', 2, 3),
('HPb', 1, 4),
('HPc', 5, 1),
('HPc', 6, 1),
('HPb', 4, 1),
('HPb', 7, 1),
('HPa', 1, 1),
('HPa', 6, 1),
('HPa', 3, 2),
('HPb', 6, 3),
('HPb', 3, 1),
('HPa', 6, 2),
('HPb', 1, 3),
('HPa', 1, 3),
('HPa', 6, 3),
('HPc', 5, 5),
('HPa', 1, 2),
('HPb', 5, 2),
('HPc', 5, 4),
('HPb', 7, 3),
('HPa', 1, 5),
('HPb', 1, 5),
('HPa', 1, 4),
('HPc', 7, 5),
('HPa', 1, 7),
('HPa', 4, 4),
('HPc', 7, 4),
('HPc', 1, 7),
('HPa', 1, 6),
('HPa', 4, 5),
('HPb', 1, 6),
('HPc', 1, 6),
('HPa', 4, 2),
('HPb', 1, 7),
('HPc', 1, 5),
('HPa', 7, 3),
('HPa', 4, 3),
('HPc', 7, 1),
('HPc', 1, 4),
('HPc', 8, 1),
('HPc', 2, 2),
('HPb', 1, 1),
('HPc', 1, 3),
('HPa', 7, 1),
('HPc', 2, 3),
('HPa', 4, 1),
('HPc', 7, 3),
('HPc', 1, 2),
('HPc', 8, 3),
('HPc', 4, 2),
('HPc', 7, 2),
('HPc', 1, 1),
('HPb', 2, 1),
('HPc', 8, 4),
('HPc', 2, 1),
('HPc', 8, 5),
('HPb', 6, 5)]
We can access the neuron dataframe for a particular epoch in the same way as the tetrodes
In [12]:
epoch_key = ('HPa', 6, 2)
neuron_info[epoch_key]
Out[12]:
area
csi
meanrate
neuron_number
numspikes
propbursts
spikewidth
tetrode_number
animal
day
epoch
neuron_id
animal
day
epoch
tetrode_number
neuron_number
HPa
6
2
1
1
CA1
0.004048582995951417
0.4099585062240664
1
494
0.02834008097165992
11.638994636104313
1
HPa
6
2
HPa6211
2
CA1
0.11722488038277512
0.34688796680497924
2
418
0.4090909090909091
12.0074641798351
1
HPa
6
2
HPa6212
3
CA1
0.04294478527607362
0.4058091286307054
3
489
0.20245398773006135
9.818915554815934
1
HPa
6
2
HPa6213
4
CA1
0.10416666666666667
0.15933609958506223
4
192
0.4270833333333333
9.136247973738753
1
HPa
6
2
HPa6214
5
CA1
0.09194977843426884
2.2473029045643154
5
2708
0.33899556868537667
11.291874042349674
1
HPa
6
2
HPa6215
6
CA1
0.09351432880844646
0.5502074688796681
6
663
0.3257918552036199
9.665083455651253
1
HPa
6
2
HPa6216
4
1
CA1
0.20634920634920634
0.15684647302904564
1
189
0.6666666666666666
8.197871324149913
4
HPa
6
2
HPa6241
2
CA1
0.08823529411764706
0.11286307053941909
2
136
0.3897058823529412
9.155198352025812
4
HPa
6
2
HPa6242
3
CA1
0.023166023166023165
0.2149377593360996
3
259
0.0888030888030888
9.871494807397777
4
HPa
6
2
HPa6243
4
CA1
0.15186028853454822
1.0929460580912862
4
1317
0.4510250569476082
9.432374135235689
4
HPa
6
2
HPa6244
5
CA1
0.15492957746478872
0.23568464730290456
5
284
0.5211267605633803
9.741539046552957
4
HPa
6
2
HPa6245
6
CA1
0.04779411764705882
0.45145228215767635
6
544
0.11764705882352941
11.103046308020144
4
HPa
6
2
HPa6246
7
NaN
NaN
NaN
7
NaN
NaN
NaN
4
HPa
6
2
HPa6247
8
NaN
NaN
NaN
8
NaN
NaN
NaN
4
HPa
6
2
HPa6248
5
1
CA1
0.26758409785932724
0.5427385892116182
1
654
0.7691131498470948
8.12454996691276
5
HPa
6
2
HPa6251
2
CA1
0.09761824890976183
2.473858921161826
2
2981
0.33814156323381417
9.22819963534934
5
HPa
6
2
HPa6252
3
NaN
NaN
NaN
3
NaN
NaN
NaN
5
HPa
6
2
HPa6253
4
CA1
0.1951219512195122
0.5443983402489626
4
656
0.600609756097561
10.408533181462253
5
HPa
6
2
HPa6254
5
CA1
0.06997929606625258
2.004149377593361
5
2415
0.27701863354037265
9.628117714100009
5
HPa
6
2
HPa6255
8
1
iCA1
0.04825737265415549
0.3095435684647303
1
373
0.17158176943699732
11.028880358555128
8
HPa
6
2
HPa6281
9
1
iCA1
0.08766233766233766
0.5112033195020746
1
616
0.31655844155844154
13.288148027690363
9
HPa
6
2
HPa6291
10
1
iCA1
0.06779661016949153
0.2937759336099585
1
354
0.23728813559322035
11.26401493403312
10
HPa
6
2
HPa62101
11
1
iCA1
0.1391057487579844
1.1692946058091287
1
1409
0.47764371894960966
10.202388012245862
11
HPa
6
2
HPa62111
12
1
iCA1
0.03225806451612903
0.3087136929460581
1
372
0.12365591397849462
10.944528891560092
12
HPa
6
2
HPa62121
14
1
iCA1
0.08710801393728224
0.23817427385892115
1
287
0.26480836236933797
10.001442777092178
14
HPa
6
2
HPa62141
17
1
PFC
0.20539862821742016
56.261410788381745
1
67795
0.6073456744597684
4.94589895880427
17
HPa
6
2
HPa62171
18
1
PFC
0.006277463904582548
1.3219917012448132
1
1593
0.03327055869428751
11.816300042092637
18
HPa
6
2
HPa62181
get_spike_indicator_dataframeThe spike indicator dataframe is a dataframe where each row corresponds to the recording timestamp and is_spike is an indicator function where 1 indicates a spike has occurred at that timestamp and 0 indicates no spike has occurred at that timestamp. We can access the spike indicator dataframe for a particular neuron by using the neuron key (Animal, Day, Epoch, Tetrode, Neuron).
In [13]:
from src.data_processing import get_spike_indicator_dataframe
neuron_key = ('HPa', 6, 2, 1, 4)
get_spike_indicator_dataframe(neuron_key, ANIMALS)
Out[13]:
is_spike
time
4122.992000
0
4122.992667
0
4122.993333
0
4122.994000
0
4122.994667
0
4122.995333
0
4122.996000
0
4122.996667
0
4122.997333
0
4122.998000
0
4122.998667
0
4122.999333
0
4123.000000
0
4123.000667
0
4123.001333
0
4123.002000
0
4123.002667
0
4123.003333
0
4123.004000
0
4123.004667
0
4123.005333
0
4123.006000
0
4123.006667
0
4123.007333
0
4123.008000
0
4123.008667
0
4123.009333
0
4123.010000
0
4123.010667
0
4123.011333
0
...
...
5327.999333
0
5328.000000
0
5328.000667
0
5328.001333
0
5328.002000
0
5328.002667
0
5328.003333
0
5328.004000
0
5328.004667
0
5328.005333
0
5328.006000
0
5328.006667
0
5328.007333
0
5328.008000
0
5328.008667
0
5328.009333
0
5328.010000
0
5328.010667
0
5328.011333
0
5328.012000
0
5328.012667
0
5328.013333
0
5328.014000
0
5328.014667
0
5328.015333
0
5328.016000
0
5328.016667
0
5328.017333
0
5328.018000
0
5328.018667
0
1807541 rows × 1 columns
This information can be obtained from the neuron dataframe. The index of the neuron dataframe gives the key.
In [14]:
neuron_info[epoch_key].index.tolist()
Out[14]:
[('HPa', 6, 2, 1, 1),
('HPa', 6, 2, 1, 2),
('HPa', 6, 2, 1, 3),
('HPa', 6, 2, 1, 4),
('HPa', 6, 2, 1, 5),
('HPa', 6, 2, 1, 6),
('HPa', 6, 2, 4, 1),
('HPa', 6, 2, 4, 2),
('HPa', 6, 2, 4, 3),
('HPa', 6, 2, 4, 4),
('HPa', 6, 2, 4, 5),
('HPa', 6, 2, 4, 6),
('HPa', 6, 2, 4, 7),
('HPa', 6, 2, 4, 8),
('HPa', 6, 2, 5, 1),
('HPa', 6, 2, 5, 2),
('HPa', 6, 2, 5, 3),
('HPa', 6, 2, 5, 4),
('HPa', 6, 2, 5, 5),
('HPa', 6, 2, 8, 1),
('HPa', 6, 2, 9, 1),
('HPa', 6, 2, 10, 1),
('HPa', 6, 2, 11, 1),
('HPa', 6, 2, 12, 1),
('HPa', 6, 2, 14, 1),
('HPa', 6, 2, 17, 1),
('HPa', 6, 2, 18, 1)]
Like the epoch and tetrode dataframe, this allows us to filter for certain attributes (like if we want neurons in a certain brain area) and select only those neurons. For example, if we want only CA1 neurons:
In [15]:
neuron_info[epoch_key].query('area == "CA1"')
Out[15]:
area
csi
meanrate
neuron_number
numspikes
propbursts
spikewidth
tetrode_number
animal
day
epoch
neuron_id
animal
day
epoch
tetrode_number
neuron_number
HPa
6
2
1
1
CA1
0.004048582995951417
0.4099585062240664
1
494
0.02834008097165992
11.638994636104313
1
HPa
6
2
HPa6211
2
CA1
0.11722488038277512
0.34688796680497924
2
418
0.4090909090909091
12.0074641798351
1
HPa
6
2
HPa6212
3
CA1
0.04294478527607362
0.4058091286307054
3
489
0.20245398773006135
9.818915554815934
1
HPa
6
2
HPa6213
4
CA1
0.10416666666666667
0.15933609958506223
4
192
0.4270833333333333
9.136247973738753
1
HPa
6
2
HPa6214
5
CA1
0.09194977843426884
2.2473029045643154
5
2708
0.33899556868537667
11.291874042349674
1
HPa
6
2
HPa6215
6
CA1
0.09351432880844646
0.5502074688796681
6
663
0.3257918552036199
9.665083455651253
1
HPa
6
2
HPa6216
4
1
CA1
0.20634920634920634
0.15684647302904564
1
189
0.6666666666666666
8.197871324149913
4
HPa
6
2
HPa6241
2
CA1
0.08823529411764706
0.11286307053941909
2
136
0.3897058823529412
9.155198352025812
4
HPa
6
2
HPa6242
3
CA1
0.023166023166023165
0.2149377593360996
3
259
0.0888030888030888
9.871494807397777
4
HPa
6
2
HPa6243
4
CA1
0.15186028853454822
1.0929460580912862
4
1317
0.4510250569476082
9.432374135235689
4
HPa
6
2
HPa6244
5
CA1
0.15492957746478872
0.23568464730290456
5
284
0.5211267605633803
9.741539046552957
4
HPa
6
2
HPa6245
6
CA1
0.04779411764705882
0.45145228215767635
6
544
0.11764705882352941
11.103046308020144
4
HPa
6
2
HPa6246
5
1
CA1
0.26758409785932724
0.5427385892116182
1
654
0.7691131498470948
8.12454996691276
5
HPa
6
2
HPa6251
2
CA1
0.09761824890976183
2.473858921161826
2
2981
0.33814156323381417
9.22819963534934
5
HPa
6
2
HPa6252
4
CA1
0.1951219512195122
0.5443983402489626
4
656
0.600609756097561
10.408533181462253
5
HPa
6
2
HPa6254
5
CA1
0.06997929606625258
2.004149377593361
5
2415
0.27701863354037265
9.628117714100009
5
HPa
6
2
HPa6255
We can get the keys for CA1 neurons only:
In [16]:
neuron_info[epoch_key].query('area == "CA1"').index.tolist()
Out[16]:
[('HPa', 6, 2, 1, 1),
('HPa', 6, 2, 1, 2),
('HPa', 6, 2, 1, 3),
('HPa', 6, 2, 1, 4),
('HPa', 6, 2, 1, 5),
('HPa', 6, 2, 1, 6),
('HPa', 6, 2, 4, 1),
('HPa', 6, 2, 4, 2),
('HPa', 6, 2, 4, 3),
('HPa', 6, 2, 4, 4),
('HPa', 6, 2, 4, 5),
('HPa', 6, 2, 4, 6),
('HPa', 6, 2, 5, 1),
('HPa', 6, 2, 5, 2),
('HPa', 6, 2, 5, 4),
('HPa', 6, 2, 5, 5)]
And then get the spike indicator data for those neurons:
In [17]:
pd.concat(
[get_spike_indicator_dataframe(neuron_key, ANIMALS)
for neuron_key in neuron_info[epoch_key].query('area == "CA1"').index], axis=1)
Out[17]:
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
is_spike
time
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1807541 rows × 16 columns
If we want the numpy array, use .values
In [18]:
pd.concat(
[get_spike_indicator_dataframe(neuron_key, ANIMALS)
for neuron_key in neuron_info[epoch_key].query('area == "CA1"').index], axis=1).values
Out[18]:
array([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]])
In [19]:
from src.data_processing import get_interpolated_position_dataframe
epoch_key = ('HPa', 6, 2)
get_interpolated_position_dataframe(epoch_key, ANIMALS)
Out[19]:
trajectory_category_ind
trajectory_direction
trajectory_turn
trial_number
linear_distance
x_position
y_position
head_direction
speed
linear_position
time
4122.992000
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1807541 rows × 10 columns
Content source: edeno/Jadhav-2016-Data-Analysis
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