code: def test_boxy_filetypes(fname):
      """Test reading parsed and unparsed BOXY data files."""
      # BOXY data files can be saved in two formats (parsed and unparsed) which
      # mostly determines how the data is organised.
      # For parsed files, each row is a single timepoint and all
      # source/detector combinations are represented as columns.
      # For unparsed files, each row is a source and each group of n rows
      # represents a timepoint. For example, if there are ten sources in the raw
      # data then the first ten rows represent the ten sources at timepoint 1
      # while the next set of ten rows are the ten sources at timepoint 2.
      # Detectors are represented as columns.
  
      # Since p_pod is designed to only load unparsed files, we will first
      # compare MNE and p_pod loaded data from an unparsed data file. If those
      # files are comparable, then we will compare the MNE loaded data between
      # parsed and unparsed files.
      raw = read_raw_boxy(fname, verbose=True)
      assert raw.info["sfreq"] == 79.4722
      _assert_ppod(raw, p_pod_0_84)
  
      # Grab our different data types.
      unp_dc = raw.copy().pick("fnirs_cw_amplitude")
      unp_ac = raw.copy().pick("fnirs_fd_ac_amplitude")
      unp_ph = raw.copy().pick("fnirs_fd_phase")
  
      # Check channel names.
      chans = [
          "S1_D1",
          "S2_D1",
          "S3_D1",
          "S4_D1",
          "S5_D1",
          "S6_D1",
          "S7_D1",
          "S8_D1",
      ]
  
      assert unp_dc.info["ch_names"] == [
          i_chan + " " + "DC" for i_chan in chans
      ]
      assert unp_ac.info["ch_names"] == [
          i_chan + " " + "AC" for i_chan in chans
      ]
      assert unp_ph.info["ch_names"] == [
          i_chan + " " + "Ph" for i_chan in chans
      ]
  
file_path: mne-python/mne/io/boxy/tests/test_boxy.py
imports: import numpy
  import pytest
  import scipy.io
  from numpy.testing import assert_allclose, assert_array_equal, assert_array_less
  from mne import pick_types
  from mne.datasets import testing
  from mne.io import read_raw_boxy
  from mne.io.tests.test_raw import _test_raw_reader
label_desc: Use image node_id 3 for calling a global function with example usage: test_boxy_filetypes(fname) without return types