How to use the nexusformat.nexus.tree.NXfield function in nexusformat

if len(scan.G) > 0:
                entry.G = NXlog()
                desc = "SPEC geometry arrays, meanings defined by SPEC diffractometer support"
                # e.g.: SPECD/four.mac
                # http://certif.com/spec_manual/fourc_4_9.html
                entry.G.attrs['description'] = desc
                for item, value in scan.G.items():
                    entry.G[item] = NXfield(list(map(float, value.split())))
            if scan.T != '':
                entry['counting_basis'] = NXfield('SPEC scan with constant counting time')
                entry['T'] = NXfield(float(scan.T))
                entry['T'].units = 'seconds'
                entry['T'].description = 'SPEC scan with constant counting time'
            elif scan.M != '':
                entry['counting_basis'] = NXfield('SPEC scan with constant monitor count')
                entry['M'] = NXfield(float(scan.M))
                entry['M'].units = 'counts'
                entry['M'].description = 'SPEC scan with constant monitor count'
            if scan.Q != '':
                entry['Q'] = NXfield(list(map(float,scan.Q)))
                entry['Q'].description = 'hkl at start of scan'

            root['scan_' + str(key)] = entry

            self.progress_bar.setValue(int(key))
            self.update_progress()

        return root

if '_mca_' in scan.data:    # 3-D array
            # TODO: ?merge with parser_mca_spectra()?
            for mca_key, mca_data in scan.data['_mca_'].items():
                key = "__" + mca_key

                spectra_lengths = list(map(len, mca_data))
                num_channels = max(spectra_lengths)
                if num_channels != min(spectra_lengths):
                    msg = 'MCA spectra have different lengths'
                    msg += ' in scan #' + str(scan.scanNum)
                    msg += ' in file ' + str(scan.specFile)
                    raise ValueError(msg)

                data_shape += [num_channels, ]
                mca = np.array(mca_data)
                nxdata[key] = NXfield(utils.reshape_data(mca, data_shape))
                nxdata[key].units = "counts"

                try:
                    # use MCA channel numbers as known at time of scan
                    chan1 = scan.MCA['first_saved']
                    chanN = scan.MCA['last_saved']
                    channel_range = range(chan1, chanN+1)
                except:
                    # basic indices
                    channel_range = range(1, num_channels+1)

                ch_key = key + "_channel"
                nxdata[ch_key] = NXfield(channel_range)
                nxdata[ch_key].units = 'channel'
                axes = (label1, label2, ch_key)
                nxdata[key].axes = ':'.join( axes )

# TODO: what about the MCA data in this case?
        else:
            axis1 = axis1[0:intervals1+1]
            axis2 = [axis2[row] for row in range(len(axis2)) if row % (intervals1+1) == 0]

            column_labels = scan.L
            column_labels.remove(label1)    # special handling
            column_labels.remove(label2)    # special handling
            if scan.scanCmd.startswith('hkl'):
                # find the reciprocal space axis held constant
                label3 = [key for key in ('H', 'K', 'L') if key not in (label1, label2)][0]
                axis3 = scan.data.get(label3)[0]
                nxdata[label3] = NXfield(axis3)
                column_labels.remove(label3)    # already handled

            nxdata[label1] = NXfield(axis1)    # 1-D array
            nxdata[label2] = NXfield(axis2)    # 1-D array

            # build 2-D data objects (do not build label1, label2, [or label3] as 2-D objects)
            data_shape = [len(axis2), len(axis1)]
            for label in column_labels:
                axis = np.array( scan.data.get(label) )
                clean_name = utils.sanitize_name(nxdata, label)
                nxdata[clean_name] = NXfield(utils.reshape_data(axis, data_shape))
                nxdata[clean_name].original_name = label

            signal_axis_label = utils.sanitize_name(nxdata, scan.column_last)
            nxdata.nxsignal = nxdata[signal_axis_label]
            nxdata.nxaxes = [nxdata[label2], nxdata[label1]]

        if '_mca_' in scan.data:    # 3-D array
            # TODO: ?merge with parser_mca_spectra()?

def parser_1D_columns(self, nxdata, scan):
        '''generic data parser for 1-D column data'''
        from spec2nexus import utils
        for column in scan.L:
            if column in scan.data:
                clean_name = utils.sanitize_name(nxdata, column)
                nxdata[clean_name] = NXfield(scan.data[column])
                nxdata[clean_name].original_name = column

        signal = utils.sanitize_name(nxdata, scan.column_last)      # primary Y axis
        axis = utils.sanitize_name(nxdata, scan.column_first)       # primary X axis
        nxdata.nxsignal = nxdata[signal]
        nxdata.nxaxes = nxdata[axis]
        
        self.parser_mca_spectra(nxdata, scan, axis)

def parser_mca_spectra(self, nxdata, scan, primary_axis_label):
        '''parse for optional MCA spectra'''
        if '_mca_' in scan.data:        # check for it
            for mca_key, mca_data in scan.data['_mca_'].items():
                key = "__" + mca_key
                nxdata[key] = NXfield(mca_data)
                nxdata[key].units = "counts"
                ch_key = key + "_channel"
                nxdata[ch_key] = NXfield(range(1, len(mca_data[0])+1))
                nxdata[ch_key].units = 'channel'
                axes = (primary_axis_label, ch_key)
                nxdata[key].axes = ':'.join( axes )

time = float(time)
        if len(axis1) < intervals1:     # stopped scan before second row started
            self.parser_1D_columns(nxdata, scan)        # fallback support
            # TODO: what about the MCA data in this case?
        else:
            axis1 = axis1[0:intervals1+1]
            axis2 = [axis2[row] for row in range(len(axis2)) if row % (intervals1+1) == 0]

            column_labels = scan.L
            column_labels.remove(label1)    # special handling
            column_labels.remove(label2)    # special handling
            if scan.scanCmd.startswith('hkl'):
                # find the reciprocal space axis held constant
                label3 = [key for key in ('H', 'K', 'L') if key not in (label1, label2)][0]
                axis3 = scan.data.get(label3)[0]
                nxdata[label3] = NXfield(axis3)
                column_labels.remove(label3)    # already handled

            nxdata[label1] = NXfield(axis1)    # 1-D array
            nxdata[label2] = NXfield(axis2)    # 1-D array

            # build 2-D data objects (do not build label1, label2, [or label3] as 2-D objects)
            data_shape = [len(axis2), len(axis1)]
            for label in column_labels:
                axis = np.array( scan.data.get(label) )
                clean_name = utils.sanitize_name(nxdata, label)
                nxdata[clean_name] = NXfield(utils.reshape_data(axis, data_shape))
                nxdata[clean_name].original_name = label

            signal_axis_label = utils.sanitize_name(nxdata, scan.column_last)
            nxdata.nxsignal = nxdata[signal_axis_label]
            nxdata.nxaxes = [nxdata[label2], nxdata[label1]]

else:
            axis1 = axis1[0:intervals1+1]
            axis2 = [axis2[row] for row in range(len(axis2)) if row % (intervals1+1) == 0]

            column_labels = scan.L
            column_labels.remove(label1)    # special handling
            column_labels.remove(label2)    # special handling
            if scan.scanCmd.startswith('hkl'):
                # find the reciprocal space axis held constant
                label3 = [key for key in ('H', 'K', 'L') if key not in (label1, label2)][0]
                axis3 = scan.data.get(label3)[0]
                nxdata[label3] = NXfield(axis3)
                column_labels.remove(label3)    # already handled

            nxdata[label1] = NXfield(axis1)    # 1-D array
            nxdata[label2] = NXfield(axis2)    # 1-D array

            # build 2-D data objects (do not build label1, label2, [or label3] as 2-D objects)
            data_shape = [len(axis2), len(axis1)]
            for label in column_labels:
                axis = np.array( scan.data.get(label) )
                clean_name = utils.sanitize_name(nxdata, label)
                nxdata[clean_name] = NXfield(utils.reshape_data(axis, data_shape))
                nxdata[clean_name].original_name = label

            signal_axis_label = utils.sanitize_name(nxdata, scan.column_last)
            nxdata.nxsignal = nxdata[signal_axis_label]
            nxdata.nxaxes = [nxdata[label2], nxdata[label1]]

        if '_mca_' in scan.data:    # 3-D array
            # TODO: ?merge with parser_mca_spectra()?
            for mca_key, mca_data in scan.data['_mca_'].items():

def parser_mca_spectra(self, nxdata, scan, primary_axis_label):
        '''parse for optional MCA spectra'''
        if '_mca_' in scan.data:        # check for it
            for mca_key, mca_data in scan.data['_mca_'].items():
                key = "__" + mca_key
                nxdata[key] = NXfield(mca_data)
                nxdata[key].units = "counts"
                ch_key = key + "_channel"
                nxdata[ch_key] = NXfield(range(1, len(mca_data[0])+1))
                nxdata[ch_key].units = 'channel'
                axes = (primary_axis_label, ch_key)
                nxdata[key].axes = ':'.join( axes )