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# -*- coding: utf-8 -*-
"""inst.to_data_frame() helper functions."""
# Authors: Daniel McCloy <dan@mccloy.info>
#
# License: BSD-3-Clause
from inspect import signature
import numpy as np
from ._logging import logger, verbose
from ..defaults import _handle_default
@verbose
def _set_pandas_dtype(df, columns, dtype, verbose=None):
"""Try to set the right columns to dtype."""
for column in columns:
df[column] = df[column].astype(dtype)
logger.info('Converting "%s" to "%s"...' % (column, dtype))
def _scale_dataframe_data(inst, data, picks, scalings):
ch_types = inst.get_channel_types()
ch_types_used = list()
scalings = _handle_default('scalings', scalings)
for tt in scalings.keys():
if tt in ch_types:
ch_types_used.append(tt)
for tt in ch_types_used:
scaling = scalings[tt]
idx = [ii for ii in range(len(picks)) if ch_types[ii] == tt]
if len(idx):
data[:, idx] *= scaling
return data
def _convert_times(inst, times, time_format):
"""Convert vector of time in seconds to ms, datetime, or timedelta."""
# private function; pandas already checked in calling function
from pandas import to_timedelta
if time_format == 'ms':
times = np.round(times * 1e3).astype(np.int64)
elif time_format == 'timedelta':
times = to_timedelta(times, unit='s')
elif time_format == 'datetime':
times = (to_timedelta(times + inst.first_time, unit='s') +
inst.info['meas_date'])
return times
def _inplace(df, method, **kwargs):
"""Handle transition: inplace=True (pandas <1.5) → copy=False (>=1.5)."""
_meth = getattr(df, method) # used for set_index() and rename()
if 'copy' in signature(_meth).parameters:
return _meth(**kwargs, copy=False)
else:
_meth(**kwargs, inplace=True)
return df
@verbose
def _build_data_frame(inst, data, picks, long_format, mindex, index,
default_index, col_names=None, col_kind='channel',
verbose=None):
"""Build DataFrame from MNE-object-derived data array."""
# private function; pandas already checked in calling function
from pandas import DataFrame
from ..source_estimate import _BaseSourceEstimate
# build DataFrame
if col_names is None:
col_names = [inst.ch_names[p] for p in picks]
df = DataFrame(data, columns=col_names)
for i, (k, v) in enumerate(mindex):
df.insert(i, k, v)
# build Index
if long_format:
df = _inplace(df, 'set_index', keys=default_index)
df.columns.name = col_kind
elif index is not None:
df = _inplace(df, 'set_index', keys=index)
if set(index) == set(default_index):
df.columns.name = col_kind
# long format
if long_format:
df = df.stack().reset_index()
df = _inplace(df, 'rename', columns={0: 'value'})
# add column for channel types (as appropriate)
ch_map = (None if isinstance(inst, _BaseSourceEstimate) else
dict(zip(np.array(inst.ch_names)[picks],
np.array(inst.get_channel_types())[picks])))
if ch_map is not None:
col_index = len(df.columns) - 1
ch_type = df['channel'].map(ch_map)
df.insert(col_index, 'ch_type', ch_type)
# restore index
if index is not None:
df = _inplace(df, 'set_index', keys=index)
# convert channel/vertex/ch_type columns to factors
to_factor = [c for c in df.columns.tolist()
if c not in ('freq', 'time', 'value')]
_set_pandas_dtype(df, to_factor, 'category')
return df
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