API Reference

• ptsa.data
  • TimeSeries
    • TimeSeries
      • TimeSeries.add_mirror_buffer()
      • TimeSeries.append()
      • TimeSeries.astype()
      • TimeSeries.baseline_corrected()
      • TimeSeries.coerce_to()
      • TimeSeries.create()
      • TimeSeries.filter_with()
      • TimeSeries.filtered()
      • TimeSeries.from_hdf()
      • TimeSeries.from_mne_epochs()
      • TimeSeries.item()
      • TimeSeries.query()
      • TimeSeries.reduce()
      • TimeSeries.remove_buffer()
      • TimeSeries.resampled()
      • TimeSeries.searchsorted()
      • TimeSeries.to_hdf()
  • Readers
    • EDFRawReader
      • EDFRawReader.read_file()
    • BaseEventReader
      • BaseEventReader.as_dataframe()
      • BaseEventReader.find_data_dir_prefix()
      • BaseEventReader.modify_eeg_path()
      • BaseEventReader.normalize_paths()
      • BaseEventReader.read_matlab()
    • CMLEventReader
      • CMLEventReader.modify_eeg_path()
    • EEGReader
      • EEGReader.compute_read_offsets()
      • EEGReader.read_events_data()
      • EEGReader.read_session_data()
    • TalReader
      • TalReader.from_dict()
      • TalReader.from_records()
      • TalReader.get_bipolar_pairs()
      • TalReader.get_monopolar_channels()
      • TalReader.mkdtype()
    • LocReader
  • Filters
    • MorletWaveletFilter
      • MorletWaveletFilter.filter()
    • ptsa.data.filters.MorletWaveletFilterCpp
    • ButterworthFilter
      • ButterworthFilter.filter()
    • ResampleFilter
      • ResampleFilter.filter()
• Extension Modules
  • ptsa.extensions.circular_stat
    • circ_diff()
    • circ_diff_par()
    • circ_mean()
    • circ_diff_time_bins()
    • compute_f_stat()
    • compute_z_scores()
    • single_trial_ppc_all_features()
    • single_trial_outsample_ppc_features()
  • ptsa.extensions.morlet
  • ptsa.extensions.edf
    • ChannelInfo
    • EDFFile
      • EDFFile.__init__()
      • EDFFile.get_channel_info()
      • EDFFile.get_channel_numbers()
      • EDFFile.get_samplerate()
      • EDFFile.read_samples()

ptsa.data.concat.concat(objs: ~typing.Iterable[~ptsa.data.timeseries.TimeSeries], dim: ~typing.Hashable | ~ptsa.data.timeseries.TimeSeries | ~xarray.core.dataarray.DataArray, coords: ~typing.Any = 'different', compat: ~typing.Any = 'equals', positions: ~typing.Iterable[~numpy.ndarray] | None = None, fill_value: ~typing.Any = <NA>, join: ~typing.Any = 'outer', combine_attrs: str | ~typing.Callable[[~typing.Sequence[dict[~typing.Any, ~typing.Any]], ~typing.Any], dict[~typing.Any, ~typing.Any]] = 'override') → TimeSeries

Concatenate TimeSeries objects along a new or existing dimension. :param objs: TimeSeries objects to concatenate together. Each object is expected to

consist of variables and coordinates with matching shapes except for along the concatenated dimension.

Parameters:

• dim (str or TimeSeries or pandas.Index) – Name of the dimension to concatenate along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. If dimension is provided as a DataArray or Index, its name is used as the dimension to concatenate along and the values are added as a coordinate.

• coords ({"minimal", "different", "all"} or list of str, optional) –

  These coordinate variables will be concatenated together:

  • ”minimal”: Only coordinates in which the dimension already appears

  are included. * “different”: Coordinates which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of coordinate variables into memory if they are not already loaded. * “all”: All coordinate variables will be concatenated, except those corresponding to other dimensions. * list of str: The listed coordinate variables will be concatenated, in addition to the “minimal” coordinates.

• compat ({"identical", "equals", "broadcast_equals", "no_conflicts", "override"}, optional) –

  String indicating how to compare non-concatenated variables of the same name for potential conflicts. This is passed down to merge. - “broadcast_equals”: all values must be equal when variables are

  broadcast against each other to ensure common dimensions.

  • ”equals”: all values and dimensions must be the same.

  • ”identical”: all values, dimensions and attributes must be the

    same.

  • ”no_conflicts”: only values which are not null in both datasets

    must be equal. The returned dataset then contains the combination of all non-null values.

  • ”override”: skip comparing and pick variable from first dataset

• positions (None or list of integer arrays, optional) – List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order.

• fill_value (scalar or dict-like, optional) – Value to use for newly missing values. If a dict-like, maps variable names to fill values. Use a data array’s name to refer to its values.

• join ({"outer", "inner", "left", "right", "exact"}, optional) –

  String indicating how to combine differing indexes (excluding dim) in objects - “outer”: use the union of object indexes - “inner”: use the intersection of object indexes - “left”: use indexes from the first object with each dimension - “right”: use indexes from the last object with each dimension - “exact”: instead of aligning, raise ValueError when indexes to be

  aligned are not equal

  • ”override”: if indexes are of same size, rewrite indexes to be

    those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects.

• combine_attrs ({"drop", "identical", "no_conflicts", "drop_conflicts", "override"} or callable, default: "override") –

  A callable or a string indicating how to combine attrs of the objects being merged: - “drop”: empty attrs on returned Dataset. - “identical”: all attrs must be the same on every object. - “no_conflicts”: attrs from all objects are combined, any that have

  the same name must also have the same value.

  • ”drop_conflicts”: attrs from all objects are combined, any that have

    the same name but different values are dropped.

  • ”override”: skip comparing and copy attrs from the first dataset to

    the result.

  If a callable, it must expect a sequence of attrs dicts and a context object as its only parameters.

Returns:

concatenated

Return type:

type of objs