import pandas as pd
import pickle
import statsmodels
from statsmodels.regression.linear_model import (
RegressionResults,
RegressionResultsWrapper,
)
from .epochs import Epochs
from .fitgrid import FitGrid, LMFitGrid, LMERFitGrid
from .errors import FitGridError
from . import defaults
[docs]def epochs_from_hdf(filename, key, time, epoch_id, channels):
"""Construct Epochs object from an HDF5 file containing an epochs table.
The HDF5 file should contain columns with names defined by `epoch_id` and
`time` either as index columns or as regular columns. This is added as a
convenience, in general, input epochs tables should contain these columns
in the index.
Parameters
----------
filename : str
HDF5 file name
key : str
group identifier for the dataset when HDF5 file contains more than one
time : str
time column name
epoch_id : str
epoch identifier column name
channels : list of str
list of string channel names
Returns
-------
epochs : Epochs
an Epochs object with the data
"""
df = pd.read_hdf(filename, key=key)
# time and epoch id already present in index
if epoch_id in df.index.names and time in df.index.names:
return Epochs(df, time=time, epoch_id=epoch_id, channels=channels)
# time and epoch id present in columns, set index
if epoch_id in df.columns and time in df.columns:
df.set_index([epoch_id, time], inplace=True)
return Epochs(df, time=time, epoch_id=epoch_id, channels=channels)
raise FitGridError(
f'Dataset has to contain {epoch_id} and {time} as columns or indices.'
)
[docs]def epochs_from_dataframe(dataframe, time, epoch_id, channels):
"""Construct Epochs object from a Pandas DataFrame epochs table.
The DataFrame should contain columns with names defined by epoch_id and
time as index columns.
Parameters
----------
dataframe : pandas DataFrame
a pandas DataFrame object
time : str
time column name
epoch_id : str
epoch identifier column name
channels : list of str
list of string channel names
Returns
-------
epochs : Epochs
an Epochs object with the data
"""
return Epochs(dataframe, time=time, epoch_id=epoch_id, channels=channels)
[docs]def epochs_from_feather(filename, time, epoch_id, channels):
"""Construct Epochs object from a Feather file containing an epochs table.
The file should contain columns with names defined by epoch_id and time.
Parameters
----------
filename : str
Feather file name
time : str
time column name
epoch_id : str
epoch identifier column name
channels : list of str
list of string channel names
Returns
-------
epochs : Epochs
an Epochs object with the data
"""
df = pd.read_feather(filename)
# time and epoch id present in columns, set index
if epoch_id in df.columns and time in df.columns:
df.set_index([epoch_id, time], inplace=True)
return Epochs(df, time=time, epoch_id=epoch_id, channels=channels)
raise FitGridError(
f'Dataset has to contain {epoch_id} and {time} as columns or indices.'
)
[docs]def load_grid(filename):
"""Load a FitGrid object from file (created by running grid.save).
Parameters
----------
filename : str
indicates file to load from
Returns
-------
grid : FitGrid
loaded FitGrid object
"""
from pymer4 import Lmer
with open(filename, 'rb') as file:
_grid, epoch_index, time = pickle.load(file)
tester = _grid.iloc[0, 0]
if isinstance(tester, (RegressionResults, RegressionResultsWrapper)):
return LMFitGrid(_grid, epoch_index, time)
elif isinstance(tester, Lmer):
return LMERFitGrid(_grid, epoch_index, time)
else:
return FitGrid(_grid, epoch_index, time)