How to use the tables.Float32Col function in tables

lon  = T.Float32Col()       # Decimal longitude
    alt  = T.Float32Col()       # Altitude, km MSL, WGS84
    chi2 = T.Float32Col()       # Chi-squared solution quality
    power= T.Float32Col()       # Radiated power
    stations = T.UInt8Col()     # Station count
    charge   = T.Int8Col()      # Inferred storm charge
    flash_id    = T.Int32Col()     # Flash ID
    mask     = T.StringCol(4)   # Station mask

class Flash(T.IsDescription):
    flash_id = T.Int32Col()   # Flash ID
    n_points = T.Int16Col()
    start    = T.Float64Col() # flash start
    duration = T.Float32Col() # flash duration    
    ctr_lat  = T.Float32Col() # centroid latitude
    ctr_lon  = T.Float32Col() # centroid longitude
    ctr_alt  = T.Float32Col() # centroid altitude
    init_lat = T.Float32Col() # initation latitude
    init_lon = T.Float32Col() # initation longitude
    init_alt = T.Float32Col() # initation altitude
    init_pts = T.StringCol(256) # Indices of the points (first point in flash is id=0) used to calc init location
    area     = T.Float32Col() # area of convex hull of the points comprising the flash
    volume   = T.Float32Col()
    #Changed variable names: 03-20-17 ---> Check make_grids.py in case of inconsistancies.
    total_energy   =  T.Float32Col()    #Energy
    specific_energy = T.Float32Col()    #tot_energy
        


def write_h5(outfile, flashes, metadata, orig_LMA_file):
    # file_parts = lma.filename.split('.')[0].split('_')
    # time_code  = 'LMA_'+'_'.join(file_parts[1:])

mask     = T.StringCol(4)   # Station mask

class Flash(T.IsDescription):
    flash_id = T.Int32Col()   # Flash ID
    n_points = T.Int16Col()
    start    = T.Float64Col() # flash start
    duration = T.Float32Col() # flash duration    
    ctr_lat  = T.Float32Col() # centroid latitude
    ctr_lon  = T.Float32Col() # centroid longitude
    ctr_alt  = T.Float32Col() # centroid altitude
    init_lat = T.Float32Col() # initation latitude
    init_lon = T.Float32Col() # initation longitude
    init_alt = T.Float32Col() # initation altitude
    init_pts = T.StringCol(256) # Indices of the points (first point in flash is id=0) used to calc init location
    area     = T.Float32Col() # area of convex hull of the points comprising the flash
    volume   = T.Float32Col()
    #Changed variable names: 03-20-17 ---> Check make_grids.py in case of inconsistancies.
    total_energy   =  T.Float32Col()    #Energy
    specific_energy = T.Float32Col()    #tot_energy
        


def write_h5(outfile, flashes, metadata, orig_LMA_file):
    # file_parts = lma.filename.split('.')[0].split('_')
    # time_code  = 'LMA_'+'_'.join(file_parts[1:])
    # LMA_090329_180000_3600
    m=metadata # flashes[0].metadata
    time_code = 'LMA_%s%02d%02d_%02d%02d%02d_%d' % (str(m.startyear)[-2:], m.startmonth, m.startday,
                                m.starthour, m.startminute, m.startsecond, m.sec_analyzed)
    # orig_columns_LYLOUT = m.columns
    
    h5file = T.open_file(outfile, mode='w', title='Flash-sorted New Mexico Tech LMA Data')

line_stats     = tables.Int32Col(shape=(4,))
    score          = tables.Int32Col()
    child_stats    = tables.Float32Col(shape=(3, n_actions))
    cycle          = tables.Int32Col()
    value          = tables.Float32Col()
    variance       = tables.Float32Col()


class Loss(tables.IsDescription):
    loss_train               = tables.Float32Col()
    loss_train_value         = tables.Float32Col()
    loss_train_variance      = tables.Float32Col()
    loss_train_policy        = tables.Float32Col()
    loss_validation          = tables.Float32Col()
    loss_validation_value    = tables.Float32Col()
    loss_validation_variance = tables.Float32Col()
    loss_validation_policy   = tables.Float32Col()
    loss_ewc                 = tables.Float32Col()
    cycle                    = tables.Int32Col()


class DataSaver:

    def __init__(self, save_dir, save_file, cycle, chunksize=500):

        file_name = save_dir + save_file + str(cycle)

        self.chunksize = chunksize

        self.cycle = cycle

        self.file = tables.open_file(file_name, mode='a')

"""
    id = tables.UInt32Col()
    station_id = tables.UInt8Col()
    timestamp = tables.Time32Col()
    nanoseconds = tables.UInt32Col()
    ext_timestamp = tables.UInt64Col()

    r = tables.Float32Col()
    phi = tables.Float32Col()
    x = tables.Float32Col()
    y = tables.Float32Col()
    alpha = tables.Float32Col()
    N = tables.UInt8Col()
    t1 = tables.Float32Col()
    t2 = tables.Float32Col()
    t3 = tables.Float32Col()
    t4 = tables.Float32Col()
    n1 = tables.Float32Col()
    n2 = tables.Float32Col()
    n3 = tables.Float32Col()
    n4 = tables.Float32Col()


class Coincidence(tables.IsDescription):

    """Store information about a coincidence of stations within a cluster.

    An extensive air shower can trigger multiple stations, resulting in a set
    of events which are from the same shower.  This is called a coincidence.

    This table assigns an :attr:`id` to a coincidence and provides some
    additional information.  The events making up the coincidence can be looked

return int(s[i:])


class State(tables.IsDescription):
    episode        = tables.Int32Col()
    board          = tables.Int8Col(shape=(20, 10))
    policy         = tables.Float32Col(shape=(n_actions,))
    action         = tables.Int8Col()
    combo          = tables.Int32Col()
    lines          = tables.Int32Col()
    line_stats     = tables.Int32Col(shape=(4,))
    score          = tables.Int32Col()
    child_stats    = tables.Float32Col(shape=(3, n_actions))
    cycle          = tables.Int32Col()
    value          = tables.Float32Col()
    variance       = tables.Float32Col()


class Loss(tables.IsDescription):
    loss_train               = tables.Float32Col()
    loss_train_value         = tables.Float32Col()
    loss_train_variance      = tables.Float32Col()
    loss_train_policy        = tables.Float32Col()
    loss_validation          = tables.Float32Col()
    loss_validation_value    = tables.Float32Col()
    loss_validation_variance = tables.Float32Col()
    loss_validation_policy   = tables.Float32Col()
    loss_ewc                 = tables.Float32Col()
    cycle                    = tables.Int32Col()


class DataSaver:

"""
    """
    setpoint = Float32Col()
    frame_path = StringCol(140)
    ravg = Float32Col()
    gavg = Float32Col()
    bavg = Float32Col()

    # tc_temp=Float32Col()


class DiodePowerScanTableDescription(IsDescription):
    """
    """
    setpoint = Float32Col()
    eq_time = Float32Col()


class TimestampTableDescription(IsDescription):
    """

    """
    timestamp = StringCol(24)
    value = Float32Col()


class PowerScanTableDescription(IsDescription):
    """

    """
    power_requested = Float32Col()
    power_achieved = Float32Col()

if not 'eng' in globals():
    import matlab.engine
    eng = matlab.engine.start_matlab()
    #eng.addpath(eng.genpath('/Users/ajaver/GitHub_repositories/SegWorm/Only_segWorm'));
    eng.addpath(eng.genpath('/Users/ajaver/GitHub_repositories/Multiworm_Tracking/OnlySegWorm/'));
    eng.warning('off', 'normWorms:VulvaContourTooShort')
    eng.warning('off', 'normWorms:NonVulvaContourTooShort')


RESAMPLING_NUM = 65.0
class segworm_results(tables.IsDescription):
#class for the pytables 
    plate_worms_id = tables.Int32Col(pos=0)
    worm_index_joined = tables.Int32Col(pos=1)
    frame_number = tables.Int32Col(pos=2)
    skeleton = tables.Float32Col(shape = (RESAMPLING_NUM,2), pos=3)
    contour_ventral = tables.Float32Col(shape = (RESAMPLING_NUM,2), pos=4)
    contour_dorsal = tables.Float32Col(shape = (RESAMPLING_NUM,2), pos=5)
    
#import StringIO
#out = StringIO.StringIO()

#masked_image_file = '/Volumes/behavgenom$/GeckoVideo/Compressed/20150220/CaptureTest_90pc_Ch3_20022015_183607.hdf5'
#trajectories_file = '/Volumes/behavgenom$/GeckoVideo/Trajectories/20150220/CaptureTest_90pc_Ch3_20022015_183607.hdf5'

#masked_image_file = '/Users/ajaver/Desktop/Gecko_compressed/CaptureTest_90pc_Ch2_18022015_230213.hdf5'
#trajectories_file = '/Users/ajaver/Desktop/Gecko_compressed/Trajectory_CaptureTest_90pc_Ch2_18022015_230213.hdf5'

#masked_image_file = '/Users/ajaver/Desktop/Gecko_compressed/CaptureTest_90pc_Ch2_18022015_230213.hdf5'
#trajectories_file = '/Users/ajaver/Desktop/Gecko_compressed/Trajectory_CaptureTest_90pc_Ch2_18022015_230213.hdf5'

#masked_image_file = '/Users/ajaver/Desktop/Gecko_compressed/prueba/CaptureTest_90pc_Ch1_02022015_141431.hdf5'

label = StringCol(64)
    curve_type = EnumCol(curve_type, 'HEAD', base='uint8')
    num_points = Int8Col()

class CurvePointsTable(IsDescription):
    label = StringCol(64)
    x = Float32Col()
    y = Float32Col()

class PatternTable(IsDescription):
    label = StringCol(72)
    num_points = Int32Col()

class PatternPointsTable(IsDescription):
    label = StringCol(72)
    mult = Float32Col()

#
#  NETWORK OPERATIONS TABLES
#

class DemandTable(IsDescription):
    junction_label = StringCol(64)
    base_demand = Float32Col()
    demand_pattern = StringCol(72)
    demand_category = StringCol(16)

class ControlTable(IsDescription):
    link_label = StringCol(64)
    control_type = EnumCol(control_type, 'CONDITIONAL', base='uint8')
    status = EnumCol(link_status, 'OPEN', base='uint8')
    new_setting = Float32Col()

rotation of the station around its center

    .. attribute:: N

        number of detectors with at least one particle

    """
    id = tables.UInt32Col()
    station_id = tables.UInt8Col()
    r = tables.Float32Col()
    phi = tables.Float32Col()
    x = tables.Float32Col()
    y = tables.Float32Col()
    alpha = tables.Float32Col()
    N = tables.UInt8Col()
    t1 = tables.Float32Col()
    t2 = tables.Float32Col()
    t3 = tables.Float32Col()
    t4 = tables.Float32Col()
    n1 = tables.Float32Col()
    n2 = tables.Float32Col()
    n3 = tables.Float32Col()
    n4 = tables.Float32Col()


class ShowerParticle(tables.IsDescription):
    """Store information about shower particles reaching round level

    This table stores particles from shower simulations.  For example, AIRES
    simulations produce ``grdpcles`` files containing all particles which
    reached ground level.  These files can be read and their contents can be
    stored in this table.

SID = tbl.StringCol(itemsize=16)
                path = tbl.StringCol(itemsize=160)
                log_analysed = tbl.BoolCol()
                successful = tbl.BoolCol()
                algorithm = tbl.StringCol(itemsize=16)
                cpu_time = tbl.Float32Col()
                successful_steps = tbl.Int32Col()
                steps_nok = tbl.Int32Col()
                timed_out = tbl.BoolCol()
                perc_wrong = tbl.Float32Col()
                time_events_model = tbl.Int32Col()
                time_events_U = tbl.Int32Col()
                state_events = tbl.Int32Col()
                step_events = tbl.Int32Col()
                step_size_min = tbl.Float32Col()
                step_size_max = tbl.Float32Col()
                int_order_max = tbl.Int32Col()
                
                
                
            self.openh5()
            
            # if it's the first simulation, we need to create the Metadata tbl
            try:
                meta = self.h5.getNode(self.h5.root.Metadata)
            except(tbl.NoSuchNodeError):
                meta = self.h5.createTable('/', 'Metadata', Meta, 
                            title='All metadata for the simulations')
            
            # check if there's a log file 
            logfilename = simulation.filename.replace('result_','dslog_')\
                                             .replace('.mat','.txt')