How to use the strax.xenon.common.pax_file function in strax

# TODO: get n_top_pmts from config...
        area_top = (p['area_per_channel'][:, :127]
                    * to_pe[:127].reshape(1, -1)).sum(axis=1)
        # Negative-area peaks get 0 AFT - TODO why not NaN?
        m = p['area'] > 0
        r['area_fraction_top'][m] = area_top[m]/p['area'][m]
        return r


@export
@strax.takes_config(
    strax.Option(
        'nn_architecture',
        help='Path to JSON of neural net architecture',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr0.json')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr1.json'))]),   # noqa

    strax.Option(
        'nn_weights',
        help='Path to HDF5 of neural net weights',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr0.h5')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr1.h5'))]),   # noqa

    strax.Option('min_reconstruction_area',
                 help='Skip reconstruction if area (PE) is less than this',
                 default=10)
)
class PeakPositions(strax.Plugin):
    dtype = [('x', np.float32,
              'Reconstructed S2 X position (cm), uncorrected'),

with np.errstate(invalid='ignore'):
            z_cor = -(z_obs ** 2 - delta_r ** 2) ** 0.5
            invalid = np.abs(z_obs) < np.abs(delta_r)        # Why??
        z_cor[invalid] = z_obs[invalid]
        result['z'] = z_cor

        return result


@strax.takes_config(
    strax.Option(
        's1_relative_lce_map',
        help="S1 relative LCE(x,y,z) map",
        default_by_run=[
            (0, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR0_pax-680_fdc-3d_v0.json')),  # noqa
            (first_sr1_run, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR1_pax-680_fdc-3d_v0.json'))]),  # noqa
    strax.Option(
        's2_relative_lce_map',
        help="S2 relative LCE(x, y) map",
        default_by_run=[
            (0, pax_file('XENON1T_s2_xy_ly_SR0_24Feb2017.json')),
            (170118_1327, pax_file('XENON1T_s2_xy_ly_SR1_v2.2.json'))]),
    strax.Option(
        'electron_lifetime',
        help="Electron lifetime (ns)",
        default_by_run=get_elife)
)
class CorrectedAreas(strax.Plugin):
    depends_on = ['event_basics', 'event_positions']
    dtype = [('cs1', np.float32, 'Corrected S1 area (PE)'),
             ('cs2', np.float32, 'Corrected S2 area (PE)')]

with np.errstate(invalid='ignore'):
            z_cor = -(z_obs ** 2 - delta_r ** 2) ** 0.5
            invalid = np.abs(z_obs) < np.abs(delta_r)        # Why??
        z_cor[invalid] = z_obs[invalid]
        result['z'] = z_cor

        return result


@strax.takes_config(
    strax.Option(
        's1_relative_lce_map',
        help="S1 relative LCE(x,y,z) map",
        default_by_run=[
            (0, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR0_pax-680_fdc-3d_v0.json')),  # noqa
            (first_sr1_run, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR1_pax-680_fdc-3d_v0.json'))]),  # noqa
    strax.Option(
        's2_relative_lce_map',
        help="S2 relative LCE(x, y) map",
        default_by_run=[
            (0, pax_file('XENON1T_s2_xy_ly_SR0_24Feb2017.json')),
            (170118_1327, pax_file('XENON1T_s2_xy_ly_SR1_v2.2.json'))]),
    strax.Option(
        'electron_lifetime',
        help="Electron lifetime (ns)",
        default_by_run=get_elife)
)
class CorrectedAreas(strax.Plugin):
    depends_on = ['event_basics', 'event_positions']
    dtype = [('cs1', np.float32, 'Corrected S1 area (PE)'),
             ('cs2', np.float32, 'Corrected S2 area (PE)')]

@export
@strax.takes_config(
    strax.Option(
        name='electron_drift_velocity',
        help='Vertical electron drift velocity in cm/ns (1e4 m/ms)',
        default=1.3325e-4
    ),
    strax.Option(
        'fdc_map',
        help='3D field distortion correction map path',
        default_by_run=[
            (0, pax_file('XENON1T_FDC_SR0_data_driven_3d_correction_tf_nn_v0.json.gz')),  # noqa
            (first_sr1_run, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part1_v1.json.gz')),  # noqa
            (170411_0611, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part2_v1.json.gz')),  # noqa
            (170704_0556, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part3_v1.json.gz')),  # noqa
            (170925_0622, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part4_v1.json.gz'))]),  # noqa
)
class EventPositions(strax.Plugin):
    depends_on = ('event_basics',)
    dtype = [
        ('x', np.float32,
         'Interaction x-position, field-distortion corrected (cm)'),
        ('y', np.float32,
         'Interaction y-position, field-distortion corrected (cm)'),
        ('z', np.float32,
         'Interaction z-position, field-distortion corrected (cm)'),
        ('r', np.float32,
         'Interaction radial position, field-distortion corrected (cm)'),
        ('z_naive', np.float32,
         'Interaction z-position using mean drift velocity only (cm)'),
        ('r_naive', np.float32,
         'Interaction r-position using observed S2 positions directly (cm)'),

@export
@strax.takes_config(
    strax.Option(
        'nn_architecture',
        help='Path to JSON of neural net architecture',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr0.json')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr1.json'))]),   # noqa

    strax.Option(
        'nn_weights',
        help='Path to HDF5 of neural net weights',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr0.h5')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr1.h5'))]),   # noqa

    strax.Option('min_reconstruction_area',
                 help='Skip reconstruction if area (PE) is less than this',
                 default=10)
)
class PeakPositions(strax.Plugin):
    dtype = [('x', np.float32,
              'Reconstructed S2 X position (cm), uncorrected'),
             ('y', np.float32,
              'Reconstructed S2 Y position (cm), uncorrected')]
    depends_on = ('peaks',)

    # TODO
    # Parallelization doesn't seem to make it go faster
    # Is there much pure-python stuff in tensorflow?
    # Process-level paralellization might work, but you'd have to do setup

return result


@strax.takes_config(
    strax.Option(
        's1_relative_lce_map',
        help="S1 relative LCE(x,y,z) map",
        default_by_run=[
            (0, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR0_pax-680_fdc-3d_v0.json')),  # noqa
            (first_sr1_run, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR1_pax-680_fdc-3d_v0.json'))]),  # noqa
    strax.Option(
        's2_relative_lce_map',
        help="S2 relative LCE(x, y) map",
        default_by_run=[
            (0, pax_file('XENON1T_s2_xy_ly_SR0_24Feb2017.json')),
            (170118_1327, pax_file('XENON1T_s2_xy_ly_SR1_v2.2.json'))]),
    strax.Option(
        'electron_lifetime',
        help="Electron lifetime (ns)",
        default_by_run=get_elife)
)
class CorrectedAreas(strax.Plugin):
    depends_on = ['event_basics', 'event_positions']
    dtype = [('cs1', np.float32, 'Corrected S1 area (PE)'),
             ('cs2', np.float32, 'Corrected S2 area (PE)')]

    def setup(self):
        from .itp_map import InterpolatingMap
        self.s1_map = InterpolatingMap(
            get_resource(self.config['s1_relative_lce_map']))
        self.s2_map = InterpolatingMap(
            get_resource(self.config['s2_relative_lce_map']))

@export
@strax.takes_config(
    strax.Option(
        'nn_architecture',
        help='Path to JSON of neural net architecture',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr0.json')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr1.json'))]),   # noqa

    strax.Option(
        'nn_weights',
        help='Path to HDF5 of neural net weights',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr0.h5')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr1.h5'))]),   # noqa

    strax.Option('min_reconstruction_area',
                 help='Skip reconstruction if area (PE) is less than this',
                 default=10)
)
class PeakPositions(strax.Plugin):
    dtype = [('x', np.float32,
              'Reconstructed S2 X position (cm), uncorrected'),
             ('y', np.float32,
              'Reconstructed S2 Y position (cm), uncorrected')]
    depends_on = ('peaks',)

    # TODO
    # Parallelization doesn't seem to make it go faster
    # Is there much pure-python stuff in tensorflow?

area_top = (p['area_per_channel'][:, :127]
                    * to_pe[:127].reshape(1, -1)).sum(axis=1)
        # Negative-area peaks get 0 AFT - TODO why not NaN?
        m = p['area'] > 0
        r['area_fraction_top'][m] = area_top[m]/p['area'][m]
        return r


@export
@strax.takes_config(
    strax.Option(
        'nn_architecture',
        help='Path to JSON of neural net architecture',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr0.json')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_20171217_sr1.json'))]),   # noqa

    strax.Option(
        'nn_weights',
        help='Path to HDF5 of neural net weights',
        default_by_run=[
            (0, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr0.h5')),
            (first_sr1_run, pax_file('XENON1T_tensorflow_nn_pos_weights_20171217_sr1.h5'))]),   # noqa

    strax.Option('min_reconstruction_area',
                 help='Skip reconstruction if area (PE) is less than this',
                 default=10)
)
class PeakPositions(strax.Plugin):
    dtype = [('x', np.float32,
              'Reconstructed S2 X position (cm), uncorrected'),
             ('y', np.float32,

@export
@strax.takes_config(
    strax.Option(
        name='electron_drift_velocity',
        help='Vertical electron drift velocity in cm/ns (1e4 m/ms)',
        default=1.3325e-4
    ),
    strax.Option(
        'fdc_map',
        help='3D field distortion correction map path',
        default_by_run=[
            (0, pax_file('XENON1T_FDC_SR0_data_driven_3d_correction_tf_nn_v0.json.gz')),  # noqa
            (first_sr1_run, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part1_v1.json.gz')),  # noqa
            (170411_0611, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part2_v1.json.gz')),  # noqa
            (170704_0556, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part3_v1.json.gz')),  # noqa
            (170925_0622, pax_file('XENON1T_FDC_SR1_data_driven_time_dependent_3d_correction_tf_nn_part4_v1.json.gz'))]),  # noqa
)
class EventPositions(strax.Plugin):
    depends_on = ('event_basics',)
    dtype = [
        ('x', np.float32,
         'Interaction x-position, field-distortion corrected (cm)'),
        ('y', np.float32,
         'Interaction y-position, field-distortion corrected (cm)'),
        ('z', np.float32,
         'Interaction z-position, field-distortion corrected (cm)'),
        ('r', np.float32,
         'Interaction radial position, field-distortion corrected (cm)'),
        ('z_naive', np.float32,
         'Interaction z-position using mean drift velocity only (cm)'),
        ('r_naive', np.float32,

return result


@strax.takes_config(
    strax.Option(
        's1_relative_lce_map',
        help="S1 relative LCE(x,y,z) map",
        default_by_run=[
            (0, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR0_pax-680_fdc-3d_v0.json')),  # noqa
            (first_sr1_run, pax_file('XENON1T_s1_xyz_lce_true_kr83m_SR1_pax-680_fdc-3d_v0.json'))]),  # noqa
    strax.Option(
        's2_relative_lce_map',
        help="S2 relative LCE(x, y) map",
        default_by_run=[
            (0, pax_file('XENON1T_s2_xy_ly_SR0_24Feb2017.json')),
            (170118_1327, pax_file('XENON1T_s2_xy_ly_SR1_v2.2.json'))]),
    strax.Option(
        'electron_lifetime',
        help="Electron lifetime (ns)",
        default_by_run=get_elife)
)
class CorrectedAreas(strax.Plugin):
    depends_on = ['event_basics', 'event_positions']
    dtype = [('cs1', np.float32, 'Corrected S1 area (PE)'),
             ('cs2', np.float32, 'Corrected S2 area (PE)')]

    def setup(self):
        from .itp_map import InterpolatingMap
        self.s1_map = InterpolatingMap(
            get_resource(self.config['s1_relative_lce_map']))
        self.s2_map = InterpolatingMap(