loren_frank_data_processing

The loren_frank_data_processing package imports Matlab data processed by Loren Frank's lab into a friendly python format using pandas.

In order to use, the data must follow the format proscribed in this wiki.

Features

• Takes advantages of pandas DataFrame, a expressive labeled data format, and associated data manipulation and alignment tools.
• Time is encoded as pandas Timedelta, which avoids funny business with representing time series as floating points.
• Handy utility for reshaping time series relative to events of interest (reshape_to_segments)

Installation

pip install loren_frank_data_processing

OR

conda install -c edeno loren_frank_data_processing

Package Dependencies

loren_frank_data_processing requires:

• python>=3.5
• numpy
• scipy
• pandas
• xarray (only if using features from the saving module)
• netcdf4 (only if using features from the saving module)

See environment.yml for the most current list of dependencies.

Example

1. Find all recording epochs:

from collections import namedtuple
from os.path import join

from loren_frank_data_processing import make_epochs_dataframe

ROOT_DIR = 'example/path'
RAW_DATA_DIR = join(ROOT_DIR, 'Raw-Data')

Animal = namedtuple('Animal', {'directory', 'short_name'})
ANIMALS = {
    'HPa': Animal(directory=join(RAW_DATA_DIR, 'HPa_direct'),
                  short_name='HPa'),
}

epoch_info = make_epochs_dataframe(ANIMALS)

epoch_info

2. We can filter by epoch type:

epoch_info.loc[epoch_info.type == 'run']

3. We can use the epoch_info index to access the tetrode information for a recording session.

epoch_info.loc[epoch_info.type == 'run'].index.tolist()
Out:
[('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)]

 from loren_frank_data_processing import make_tetrode_dataframe

epoch_key = ('HPa', 1, 2)
tetrode_info = make_tetrode_dataframe(ANIMALS)
tetrode_info.xs(epoch_key, drop_level=False)

Similarly, the index for the tetrode info can be used as a key to access LFP information