How to use the mnelab.utils.have function in mnelab

else:
        events = events[:, [0, 2]]
    pybv.write_brainvision(data, fs, ch_names, name, parent, events=events)


# supported write file formats
# this dict contains each supported file extension as a key
# the corresponding value is a list with three elements: (1) the writer
# function, (2) the full file format name, and (3) a (comma-separated) string
# indicating the supported objects (currently either raw or epoch)
writers = {".fif": [write_fif, "Elekta Neuromag", "raw,epoch"],
           ".fif.gz": [write_fif, "Elekta Neuromag", "raw,epoch"],
           ".set": [write_set, "EEGLAB", "raw"]}
if have["pybv"]:
    writers.update({".eeg": [write_bv, "BrainVision", "raw"]})
if have["pyedflib"]:
    writers.update({".edf": [write_edf, "European Data Format", "raw"],
                    ".bdf": [write_edf, "Biosemi Data Format", "raw"]})


def write_raw(fname, raw):
    ext = "".join(Path(fname).suffixes)
    if ext in writers:
        writers[ext][0](fname, raw)
    else:
        raise ValueError(f"Unknown file type {ext}.")

"Elekta Neuromag": [".fif", ".fif.gz"],
                  "BrainVision": ".vhdr",
                  "EEGLAB": ".set",
                  "Neuroscan": ".cnt",
                  "EGI Netstation": ".mff",
                  "Nexstim eXimia": ".nxe"}
if have["pyxdf"]:
    IMPORT_FORMATS["Extensible Data Format"] = [".xdf", ".xdfz", ".xdf.gz"]

EXPORT_FORMATS = {"Elekta Neuromag": ".fif",
                  "Elekta Neuromag compressed": ".fif.gz",
                  "EEGLAB": ".set"}
if have["pyedflib"]:
    EXPORT_FORMATS["European Data Format"] = ".edf"
    EXPORT_FORMATS["BioSemi Data Format"] = ".bdf"
if have["pybv"]:
    EXPORT_FORMATS["BrainVision"] = ".eeg"


def image_path(fname):
    """Return absolute path to image fname."""
    root = Path(__file__).parent.parent
    return str((root / "images" / Path(fname)).resolve())


def split_fname(fname, ffilter):
    """Split file name into name and known extension parts.

    Parameters
    ----------
    fname : str or pathlib.Path
        File name (can include full path).

def run_ica(self):
        """Run ICA calculation."""

        methods = ["Infomax"]
        if have["picard"]:
            methods.insert(0, "Picard")
        if have["sklearn"]:
            methods.append("FastICA")

        dialog = RunICADialog(self,
                              self.model.current["data"].info["nchan"],
                              methods)

        if dialog.exec_():
            calc = CalcDialog(self, "Calculating ICA", "Calculating ICA.")

            method = dialog.method.currentText().lower()
            exclude_bad_segments = dialog.exclude_bad_segments.isChecked()

            fit_params = {}
            if dialog.extended.isEnabled():
                fit_params["extended"] = dialog.extended.isChecked()
            if dialog.ortho.isEnabled():

def run_ica(self):
        """Run ICA calculation."""

        methods = ["Infomax"]
        if have["picard"]:
            methods.insert(0, "Picard")
        if have["sklearn"]:
            methods.append("FastICA")

        dialog = RunICADialog(self,
                              self.model.current["data"].info["nchan"],
                              methods)

        if dialog.exec_():
            calc = CalcDialog(self, "Calculating ICA", "Calculating ICA.")

            method = dialog.method.currentText().lower()
            exclude_bad_segments = dialog.exclude_bad_segments.isChecked()

            fit_params = {}
            if dialog.extended.isEnabled():