#!/usr/bin/python
import argparse
import getpass
from datetime import datetime
from enum import Enum
import numpy as np
import openmdao.api as om
from aviary.subsystems.atmosphere.atmosphere import Atmosphere
from openmdao.components.interp_util.interp import InterpND
from aviary.utils.conversion_utils import _rep, _parse, _read_map
from aviary.subsystems.propulsion.engine_deck import normalize
from aviary.subsystems.propulsion.utils import EngineModelVariables, default_units
from aviary.variable_info.variables import Dynamic
from aviary.utils.csv_data_file import write_data_file
from aviary.utils.functions import get_path
from aviary.utils.named_values import NamedValues
[docs]
class EngineDeckType(Enum):
FLOPS = 'FLOPS'
GASP = 'GASP'
GASP_TS = 'GASP_TS'
def __str__(self):
return self.value
MACH = EngineModelVariables.MACH
ALTITUDE = EngineModelVariables.ALTITUDE
THROTTLE = EngineModelVariables.THROTTLE
HYBRID_THROTTLE = EngineModelVariables.HYBRID_THROTTLE
THRUST = EngineModelVariables.THRUST
TAILPIPE_THRUST = EngineModelVariables.TAILPIPE_THRUST
GROSS_THRUST = EngineModelVariables.GROSS_THRUST
SHAFT_POWER_CORRECTED = EngineModelVariables.SHAFT_POWER_CORRECTED
RAM_DRAG = EngineModelVariables.RAM_DRAG
FUEL_FLOW = EngineModelVariables.FUEL_FLOW
ELECTRIC_POWER_IN = EngineModelVariables.ELECTRIC_POWER_IN
NOX_RATE = EngineModelVariables.NOX_RATE
TEMPERATURE = EngineModelVariables.TEMPERATURE_T4
# EXIT_AREA = EngineModelVariables.EXIT_AREA
flops_keys = [
MACH,
ALTITUDE,
THROTTLE,
GROSS_THRUST,
RAM_DRAG,
FUEL_FLOW,
NOX_RATE] # , EXIT_AREA]
# later code assumes T4 is last item in keys
gasp_keys = [MACH, ALTITUDE, THROTTLE, FUEL_FLOW, TEMPERATURE]
header_names = {
MACH: 'Mach_Number',
ALTITUDE: 'Altitude',
THROTTLE: 'Throttle',
THRUST: 'Thrust',
GROSS_THRUST: 'Gross_Thrust',
RAM_DRAG: 'Ram_Drag',
FUEL_FLOW: 'Fuel_Flow',
NOX_RATE: 'NOx_Rate',
TEMPERATURE: 'T4',
SHAFT_POWER_CORRECTED: 'Shaft_Power_Corrected',
TAILPIPE_THRUST: 'Tailpipe_Thrust',
# EXIT_AREA: 'Exit Area',
}
[docs]
def EngineDeckConverter(input_file, output_file, data_format: EngineDeckType):
'''
Converts FLOPS- or GASP-formatted engine decks into Aviary csv format.
FLOPS decks are changed from column-delimited to csv format with added headers.
GASP decks are reorganized into csv. T4 is recovered using assumptions used in GASPy.
Data points whose T4 exceeds T4max are removed.
Parameters
----------
input_file : (str, Path)
path to engine deck file to be converted
output_file : (str, Path)
path to file where new converted data will be written
data_format : (EngineDeckType)
data format used by input_file (FLOPS or GASP)
'''
# TODO rounding for calculated values?
timestamp = datetime.now().strftime('%m/%d/%y at %H:%M')
user = getpass.getuser()
comments = []
header = {}
data = {}
data_file = get_path(input_file)
comments.append(f'# created {timestamp} by {user}')
legacy_code = data_format.value
engine_type = 'engine'
if legacy_code == 'GASP_TS':
engine_type = 'turboshaft engine'
legacy_code = 'GASP'
comments.append(
f'# {legacy_code}-derived {engine_type} deck converted from {data_file.name}')
if data_format == EngineDeckType.FLOPS:
header = {key: default_units[key] for key in flops_keys}
data = {key: np.array([]) for key in flops_keys}
with open(data_file, newline='', encoding='utf-8-sig') as file:
reader = _read_flops_engine(file)
data_starts = False
for line in reader:
if not data_starts:
# pull out comments before data starts
if line[0][0] == '#':
comments.append(line)
continue
data_starts = True
data[MACH] = np.append(data[MACH], line[0])
data[ALTITUDE] = np.append(data[ALTITUDE], line[1])
data[THROTTLE] = np.append(data[THROTTLE], line[2])
data[GROSS_THRUST] = np.append(data[GROSS_THRUST], line[3])
data[RAM_DRAG] = np.append(data[RAM_DRAG], line[4])
data[FUEL_FLOW] = np.append(data[FUEL_FLOW], line[5])
data[NOX_RATE] = np.append(data[NOX_RATE], line[6])
# data[EXIT_AREA].append(line[7])
elif data_format in (EngineDeckType.GASP, EngineDeckType.GASP_TS):
is_turbo_prop = True if data_format == EngineDeckType.GASP_TS else False
temperature = gasp_keys.pop()
fuelflow = gasp_keys.pop()
if is_turbo_prop:
gasp_keys.extend((SHAFT_POWER_CORRECTED, TAILPIPE_THRUST))
else:
gasp_keys.extend((THRUST,)) # must keep "," here
gasp_keys.extend((fuelflow, temperature))
data = {key: [] for key in gasp_keys}
scalars, tables, fields = _read_gasp_engine(data_file, is_turbo_prop)
if 'throttle_type' in scalars:
throttle_type = scalars.pop('throttle_type')
else:
throttle_type = 1
t4max = scalars['t4max']
if t4max <= 100 or throttle_type == 3:
throttle_step = 2
else:
throttle_step = .05
# save scalars as comments
comments.extend(['# ' + key + ': ' + str(scalars[key])
for key in scalars.keys()])
# recommended to always generate structured grid
structure_data = True
if structure_data:
structured_data = _make_structured_grid(tables, method='lagrange3',
fields=fields, throttle_step=throttle_step)
data[MACH] = structured_data['fuelflow']['machs']
data[ALTITUDE] = structured_data['fuelflow']['alts']
data[FUEL_FLOW] = structured_data['fuelflow']['vals']
T4T2 = structured_data['fuelflow']['t4t2s']
if is_turbo_prop:
data[SHAFT_POWER_CORRECTED] = structured_data['shaft_power_corrected']['vals']
data[TAILPIPE_THRUST] = structured_data['tailpipe_thrust']['vals']
else:
data[THRUST] = structured_data['thrust']['vals']
else:
data[MACH] = tables['fuelflow'][:, 2]
data[ALTITUDE] = tables['fuelflow'][:, 0]
data[FUEL_FLOW] = tables['fuelflow'][:, 3]
T4T2 = tables['fuelflow'][:, 1]
if is_turbo_prop:
data[SHAFT_POWER_CORRECTED] = tables['shaft_power_corrected'][:, 3]
data[TAILPIPE_THRUST] = tables['tailpipe_thrust'][:, 3]
else:
data[THRUST] = tables['thrust'][:, 3]
generate_flight_idle = True
if generate_flight_idle and not is_turbo_prop:
data, T4T2 = _generate_flight_idle(data, T4T2,
ref_sls_airflow=scalars['sls_airflow'],
ref_sfn_idle=scalars['sfn_idle'])
# if t4max 100 or less, it is actually throttle. Remove temperature as variable
if t4max <= 100 or throttle_type == 3:
compute_T4 = False
data.pop(TEMPERATURE)
# temperature is assumed last in keys
gasp_keys.pop(-1)
else:
compute_T4 = True
# define header now that we know what is in the engine deck
header = {key: default_units[key] for key in gasp_keys}
if compute_T4:
# compute T4 using atmospheric model
prob = om.Problem()
prob.model.add_subsystem('T4T2', om.IndepVarComp('T4:T2',
T4T2,
units='unitless'),
promotes=['*'])
prob.model.add_subsystem(
Dynamic.Atmosphere.MACH,
om.IndepVarComp(Dynamic.Atmosphere.MACH, data[MACH], units='unitless'),
promotes=['*'],
)
prob.model.add_subsystem(
Dynamic.Mission.ALTITUDE,
om.IndepVarComp(Dynamic.Mission.ALTITUDE, data[ALTITUDE], units='ft'),
promotes=['*'],
)
prob.model.add_subsystem(
name='atmosphere',
subsys=Atmosphere(num_nodes=len(data[MACH])),
promotes_inputs=[Dynamic.Mission.ALTITUDE],
promotes_outputs=[Dynamic.Atmosphere.TEMPERATURE],
)
prob.model.add_subsystem(
name='conversion',
subsys=AtmosCalc(num_nodes=len(data[MACH])),
promotes_inputs=[
Dynamic.Atmosphere.MACH,
Dynamic.Atmosphere.TEMPERATURE,
],
promotes_outputs=['t2'],
)
prob.setup()
prob.run_model()
T2 = prob.get_val('t2')
T4 = T2 * T4T2
data[TEMPERATURE] = T4
# Throttle is T4 normalized from 0 to 1 (T4max)
# By always keeping minimum T4 zero for normalization, throttle stays
# consistent with fraction of T4max
# TODO flight condition dependent throttle range?
# NOTE this often leaves max throttles less than 1 in the deck - this causes
# problems when finding reference SLS thrust, as there is often no max
# power data at that point in the engine deck. It is recommended GASP
# engine decks override Aircraft.Engine.REFERENCE_THRUST in EngineDecks
data[THROTTLE] = normalize(data[TEMPERATURE], minimum=0.0, maximum=t4max)
else:
# data[THROTTLE] = normalize(
# T4T2, minimum=scalars['t4flight_idle'], maximum=t4max
# )
data[THROTTLE] = normalize(T4T2, minimum=0.0, maximum=t4max)
# TODO save these points as commented out?
# remove points above T4max
valid_idx = np.where(data[THROTTLE] <= 1.0)
data[MACH] = data[MACH][valid_idx]
data[ALTITUDE] = data[ALTITUDE][valid_idx]
data[THROTTLE] = data[THROTTLE][valid_idx]
data[FUEL_FLOW] = data[FUEL_FLOW][valid_idx]
if compute_T4:
data[TEMPERATURE] = data[TEMPERATURE][valid_idx]
if is_turbo_prop:
data[SHAFT_POWER_CORRECTED] = data[SHAFT_POWER_CORRECTED][valid_idx]
data[TAILPIPE_THRUST] = data[TAILPIPE_THRUST][valid_idx]
else:
data[THRUST] = data[THRUST][valid_idx]
# data needs to be string so column length can be easily found later
for var in data:
data[var] = np.array([str(item) for item in data[var]])
else:
quit("Invalid engine deck format provided")
# sort data
# create parallel dict to data that stores floats
formatted_data = {}
for key in data:
formatted_data[key] = data[key].astype(float)
# convert engine_data from dict to list so it can be sorted
sorted_values = np.array(list(formatted_data.values())).transpose()
# Sort by mach, then altitude, then throttle, then hybrid throttle
sorted_values = sorted_values[np.lexsort(
[formatted_data[THROTTLE],
formatted_data[ALTITUDE],
formatted_data[MACH]])]
for idx, key in enumerate(formatted_data):
formatted_data[key] = sorted_values[:, idx]
# store formatted data into NamedValues object
write_data = NamedValues()
for key in data:
write_data.set_val(header_names[key], formatted_data[key], default_units[key])
if output_file is None:
sfx = data_file.suffix
if sfx == '.deck':
ext = '_aviary.deck'
else:
ext = '.deck'
output_file = data_file.stem + ext
write_data_file(output_file, write_data, comments, include_timestamp=False)
def _read_flops_engine(input_file):
'''
Read engine data file using FLOPS standard, which is column delimited data
always assumed to be in the order defined in the FLOPS manual
'''
for line in input_file:
sz = len(line)
if sz < 80:
line = line + (80 - sz) * ' '
if line[0] == '#':
data = line.strip()
else:
data = [
_flops_field_convert(line[0:5]),
_flops_field_convert(line[5:15]),
_flops_field_convert(line[15:20]),
_flops_field_convert(line[20:30]),
_flops_field_convert(line[30:40]),
_flops_field_convert(line[40:50]),
# intenional gap from 50:60 - column is left blank in FLOPS standard
_flops_field_convert(line[60:70]),
_flops_field_convert(line[70:80]),
]
yield data
def _flops_field_convert(arg: str):
rvalue = arg.strip()
if not rvalue:
rvalue = _flops_empty_field
return rvalue
# in FLOPS, empty fields are converted to zero
_flops_empty_field = '0'
def _read_gasp_engine(fp, is_turbo_prop=False):
"""Read a GASP engine deck file and parse its scalars and tabular data.
Scalars (T4 max, SLS airflow, etc.) are read from the first line of the engine deck
(IREAD=1 is assumed) and returned in a dictionary.
Data tables are also returned as a dictionary, with separate tables for thrust,
fuelflow, and airflow, since they may have different grids in general.
Each table consists of both the independent variables and the dependent variable for
the corresponding field. The table is a "tidy format" 2D array where the first three
columns are the independent varaiables (altitude, T4/T2, and Mach number) and the
final column is the dependent variable (one of thrust, fuelflow, or airflow for
turbofans or shaft_power_corrected, fuelflow, or tailpipe_thrust for turboshafts).
"""
with open(fp, "r") as f:
if is_turbo_prop:
table_types = ["shaft_power_corrected", "fuelflow", "tailpipe_thrust"]
scalars = _read_tp_header(f)
else:
table_types = ["thrust", "fuelflow", "airflow"]
scalars = _read_header(f)
tables = {k: _read_table(f, is_turbo_prop) for k in table_types}
return scalars, tables, table_types
def _read_tp_header(f):
"""Read GASP engine deck header, returning the engine scalars in a dict"""
# file header: FORMAT(2I5,10X,6F10.4)
iread, iprint, t4max, t4mcl, t4mc, t4idle, xsfc, cexp = _parse(
f, [*_rep(2, (int, 5)), (None, 10), *_rep(6, (float, 10))]
)
# file header: FORMAT(7F10.4)
sls_hp, xncref, prop_rpm, gbx_rat, torque_lim, waslrf = _parse(
f, [*_rep(6, (float, 10))]
)
return {
"throttle_type": iread,
"t4max": t4max,
"t4cruise": t4mc,
"t4climb": t4mcl,
"t4flight_idle": t4idle,
"xsfc": xsfc,
"cexp": cexp,
"sls_horsepower": sls_hp,
"freeturbine_rpm": xncref,
"propeller_rpm": prop_rpm,
"gearbox_ratio": gbx_rat,
"torque_limit": torque_lim,
"sls_corrected_airflow": waslrf,
}
def _read_header(f):
"""Read GASP engine deck header, returning the engine scalars in a dict"""
# file header: FORMAT(2I5,10X,5F10.4)
iread, iprint, wamap, t4max, t4mcl, t4mc, sfnidl = _parse(
f, [*_rep(2, (int, 5)), (None, 10), *_rep(5, (float, 10))]
)
if iread != 1:
raise RuntimeError(f"IREAD=1 expected, got {iread}")
return {
"t4max": t4max,
"t4cruise": t4mc,
"t4climb": t4mcl,
"sls_airflow": wamap,
"sfn_idle": sfnidl,
}
def _read_table(f, is_turbo_prop=False):
"""Read an entire table from a GASP engine deck file.
The table data is returned as a "tidy format" array with three columns for the
independent variables (altitude, T4/T2, and Mach number) and the final column for
the table field (one of thrust, fuelflow, or airflow for turbofans or
shaft_power_corrected, fuelflow, or tailpipe_thrust for turboshafts).
"""
tab_data = None
# table title
title = f.readline().strip()
# number of maps in the table
(nmaps,) = _parse(f, [(int, 5)])
# blank line
f.readline()
for i in range(nmaps):
map_data = _read_map(f, is_turbo_prop)
# blank line following all but the last map in the table
if i < nmaps - 1:
f.readline()
if tab_data is None:
tab_data = map_data
else:
tab_data = np.r_[tab_data, map_data]
return tab_data
def _make_structured_grid(data, method="lagrange3", fields=["thrust", "fuelflow", "airflow"], throttle_step=.05):
"""Generate a structured grid of unique mach/T4:T2/alt values in the deck"""
# step size in t4/t2 ratio used in generating the structured grid
# t2t2_step = 0.5 # original value
t4t2_step = throttle_step
# step size in mach number used in generating the structured grid
# mach_step = 0.02 # original value
mach_step = 0.05
structured_data = {}
tt4 = data['fuelflow'][:, 1]
tma = data['fuelflow'][:, 2]
t4t2s = np.arange(min(tt4), max(tt4) + t4t2_step, t4t2_step)
machs = np.arange(min(tma), max(tma) + mach_step, mach_step)
# need t4t2 in first column, mach varies on each row
pts = np.dstack(np.meshgrid(t4t2s, machs, indexing="ij")).reshape(-1, 2)
npts = pts.shape[0]
for field in fields:
map_data = data[field]
all_alts = map_data[:, 0]
alts = np.unique(all_alts)
sizes = (alts.size, t4t2s.size, machs.size)
vals = np.zeros(np.prod(sizes), dtype=float)
alt_vec = np.zeros(np.prod(sizes), dtype=float)
mach_vec = np.zeros(np.prod(sizes), dtype=float)
t4t2_vec = np.zeros(np.prod(sizes), dtype=float)
for i, alt in enumerate(alts):
d = map_data[all_alts == alt]
t4t2 = np.unique(d[:, 1])
mach = np.unique(d[:, 2])
f = d[:, 3].reshape(t4t2.size, mach.size)
# would explicitly use lagrange3 here to mimic GASP, but some engine
# decks may not have enough points per dimension
# For GASP engine deck, try to provide at least 4 Mach numbers.
# For GASP_TP engine deck, try to provide at least 4 Mach numbers
# avoid devide-by-zero RuntimeWarning
if len(mach) == 3 and method == "lagrange3":
method = "lagrange2"
elif len(mach) == 2:
method = "slinear"
interp = InterpND(
method="2D-" + method, points=(t4t2, mach), values=f, extrapolate=True
)
sl = slice(i * npts, (i + 1) * npts)
vals[sl] = interp.interpolate(pts)
alt_vec[sl] = [alt] * len(pts)
t4t2_vec[sl] = pts[:, 0]
mach_vec[sl] = pts[:, 1]
structured_data[field] = {
"vals": vals,
"alts": alt_vec,
"t4t2s": t4t2_vec,
"machs": mach_vec,
}
return structured_data
def _generate_flight_idle(data, T4T2, ref_sls_airflow, ref_sfn_idle):
machs = np.unique(data[MACH])
alts = np.unique(data[ALTITUDE])
mach_list, alt_list = np.meshgrid(machs, alts)
mach_list = mach_list.flatten()
alt_list = alt_list.flatten()
nn = len(mach_list)
prob = om.Problem()
prob.model.add_subsystem(
Dynamic.Atmosphere.MACH,
om.IndepVarComp(Dynamic.Atmosphere.MACH, mach_list, units='unitless'),
promotes=['*'],
)
prob.model.add_subsystem(
Dynamic.Mission.ALTITUDE,
om.IndepVarComp(Dynamic.Mission.ALTITUDE, alt_list, units='ft'),
promotes=['*'],
)
prob.model.add_subsystem(
name='atmosphere',
subsys=Atmosphere(num_nodes=nn),
promotes_inputs=[Dynamic.Mission.ALTITUDE],
promotes_outputs=[
Dynamic.Atmosphere.TEMPERATURE,
Dynamic.Atmosphere.STATIC_PRESSURE,
],
)
prob.model.add_subsystem(
name='conversion',
subsys=AtmosCalc(num_nodes=nn),
promotes_inputs=[
Dynamic.Atmosphere.MACH,
Dynamic.Atmosphere.TEMPERATURE,
Dynamic.Atmosphere.STATIC_PRESSURE,
],
promotes_outputs=['t2', 'p2'],
)
prob.model.add_subsystem(
name='flight_idle',
subsys=CalculateIdle(
num_nodes=nn,
ref_sfn_idle=ref_sfn_idle,
ref_sls_airflow=ref_sls_airflow),
promotes_inputs=[
't2',
'p2',
'pct_corr_airflow_idle',
'sfc_idle'],
promotes_outputs=[
'idle_thrust',
'idle_fuelflow'])
prob.setup()
prob.run_model()
idle_thrust = prob.get_val('idle_thrust')
idle_fuelflow = prob.get_val('idle_fuelflow')
data[MACH] = np.append(data[MACH], mach_list)
data[ALTITUDE] = np.append(data[ALTITUDE], alt_list)
data[THRUST] = np.append(data[THRUST], idle_thrust)
data[FUEL_FLOW] = np.append(data[FUEL_FLOW], idle_fuelflow)
T4T2 = np.append(T4T2, np.zeros(nn))
return data, T4T2
_PSLS_PSF = 2116.22 # SLS pressure in psf
_TSLS_DEGR = 518.67 # SLS temperature in deg R
[docs]
class CalculateIdle(om.ExplicitComponent):
'''
Calculates idle conditions of a GASP engine at a specified flight condition
Vectorized to calculate values for entire flight regime
'''
[docs]
def initialize(self):
self.options.declare('num_nodes', types=int)
self.options.declare(
'ref_sfn_idle',
1.0,
desc='Idle thrust-specific fuel consumption, from engine deck')
self.options.declare(
'ref_sls_airflow',
1.0,
desc='Sea-level static airflow of the reference engine')
[docs]
def setup(self):
nn = self.options["num_nodes"]
self.add_input(
"t2", _TSLS_DEGR, units="degR", shape=nn, desc="Engine inlet temperature"
)
self.add_input(
"p2", _PSLS_PSF, units="psf", shape=nn, desc="Engine inlet pressure"
)
self.add_input(
"pct_corr_airflow_idle", 0.5, desc="Percent corrected airflow at idle"
)
self.add_input(
"sfc_idle",
1.0,
units="lbm/h/lbf",
desc="Thrust-specific fuel consumption at idle",
)
self.add_output("idle_thrust", units="lbf", shape=nn, desc="Idle thrust")
# self.add_output(
# "idle_airflow", units="lbf/s", shape=nn, desc="Idle corrected airflow"
# )
self.add_output("idle_fuelflow", units="lbm/h", shape=nn, desc="Idle fuel flow")
[docs]
def compute(self, inputs, outputs):
(
t2,
p2,
pct_corr_airflow_idle,
sfc_idle,
) = inputs.values()
ref_sls_airflow = self.options['ref_sls_airflow']
ref_sfn_idle = self.options['ref_sfn_idle']
rthet2 = np.sqrt(t2 / _TSLS_DEGR)
delta2 = p2 / _PSLS_PSF
airflow_ref = pct_corr_airflow_idle * ref_sls_airflow # don't un-correct
thrust_ref = airflow_ref * delta2 / rthet2 * ref_sfn_idle
fuelflow_ref = thrust_ref * sfc_idle
outputs["idle_thrust"] = thrust_ref
# outputs["idle_airflow"] = airflow_ref
outputs["idle_fuelflow"] = fuelflow_ref
[docs]
class AtmosCalc(om.ExplicitComponent):
'''
Calculates T2 and P2 given static temperature and pressure
'''
[docs]
def initialize(self):
self.options.declare('num_nodes', types=int)
[docs]
def setup(self):
nn = self.options['num_nodes']
self.add_input(
Dynamic.Atmosphere.MACH,
val=np.zeros(nn),
desc='current Mach number',
units='unitless',
)
self.add_input(Dynamic.Atmosphere.TEMPERATURE, val=np.zeros(nn),
desc='current atmospheric temperature', units='degR')
self.add_input(
Dynamic.Atmosphere.STATIC_PRESSURE,
_PSLS_PSF,
units="psf",
shape=nn,
desc="Ambient static pressure")
self.add_output(
"t2",
units="degR",
shape=nn,
desc="Engine inlet total temperature")
self.add_output("p2", units="psf", shape=nn, desc="Engine inlet total pressure")
[docs]
def compute(self, inputs, outputs):
mach, T, P = inputs.values()
gamma = 1.4
t2 = T * (1 + 0.5 * (gamma - 1) * mach**2)
p2 = P * (t2 / T) ** (gamma / (gamma - 1))
outputs["t2"] = t2
outputs["p2"] = p2
def _setup_EDC_parser(parser):
parser.add_argument('input_file', type=str,
help='path to engine deck file to be converted')
parser.add_argument('output_file', type=str, nargs='?',
help='path to file where new converted data will be written')
parser.add_argument('-f', '--data_format', type=EngineDeckType, choices=list(EngineDeckType),
help='data format used by input_file')
def _exec_EDC(args, user_args):
EngineDeckConverter(
input_file=args.input_file,
output_file=args.output_file,
data_format=args.data_format
)
EDC_description = 'Converts FLOPS- or GASP-formatted ' \
'engine decks into Aviary csv format.\nFLOPS decks ' \
'are changed from column-delimited to csv format ' \
'with added headers.\nGASP decks are reorganized ' \
'into column based csv. T4 is recovered through ' \
'calculation. Data points whose T4 exceeds T4max ' \
'are removed.'
if __name__ == '__main__':
parser = argparse.ArgumentParser(EDC_description)
_setup_EDC_parser(parser)
args = parser.parse_args()
_exec_EDC(args, None)
