def run_with_args(args):
d = IndexedLogDB(args.database)
if args.keplerian != None:
print_mode = Keplerian(args.keplerian)
else:
print_mode = TABLE
if args.initial_conditions:
q = d.initial_conditions(args.system_range)
elif args.final_conditions:
q = d.final_conditions(args.system_range)
else:
q = d.query(args.time_range, args.system_range, args.evt_id)
for r in truncate(args.max_records, q):
print_record(print_mode, r, args.body_range)
def run_with_args(args):
d = IndexedLogDB(args.database)
if args.keplerian != None:
print_mode = Keplerian(args.keplerian)
else:
print_mode = TABLE
if args.initial_conditions:
q = d.initial_conditions(args.system_range)
elif args.final_conditions:
q = d.final_conditions(args.system_range)
else:
q = d.query(args.time_range, args.system_range, args.evt_id)
for r in truncate(args.max_records, q):
print_record(print_mode, r, args.body_range)
fileName = "/scratch/hpc/salehqt/kepler37ecc/kep37_log.db"
# Range of times to accept in the query, universal accepts all times
time_range = Range.interval((0.0, 100.0))
# Range of systems to include in the query
system_range = Range.universal() # single(0)
# Types of events to include in the results
event_range = Range.single(1)
# Bodies to record parameters for, only use interval or single
body_range = Range.interval((1, 2))
###### Execution Starts here ########################
# Open the input log file
d = IndexedLogDB(fileName)
# Execute a query on the log, the result is a Python iterable
q = d.query(time_range, system_range, event_range)
STARTING_SIZE = 1024
# Set up some lists to hold our numbers for later
body_parameters = {}
for i in body_range:
body_parameters[i] = zeros(STARTING_SIZE)
times = zeros(STARTING_SIZE)
record_count = 0
# Main loop of processing the records, we use the
# truncate function to select only the first MAX_RECORDS entries
# from q. The ones that are selected are return in (k, l) tuples
# k is the primary key for the record, l is a logrecord object.
# for most processings we only need l
for k, l in truncate(MAX_RECORDS, q):
P.scatter(final_time.keys(), final_time.values())
P.axis([
system_range.lower(),
system_range.upper(), 10,
max(final_time.values())
])
P.gca().set_yscale('log', basey=10)
P.savefig("figure_final_time")
final_time = get_final_times()
plot_final_times_vs_systemid(final_time)
exit(1)
# Execute a query on the log, the result is a Python iterable
q = d.query(time_range, system_range, event_range)
STARTING_SIZE = 1024
# Set up some lists to hold our numbers for later
body_parameters = {}
for i in body_range:
body_parameters[i] = zeros(STARTING_SIZE)
times = zeros(STARTING_SIZE)
record_count = 0
# Main loop of processing the records, we use the
# truncate function to select only the first MAX_RECORDS entries
# from q. The ones that are selected are return in (k, l) tuples
# k is the primary key for the record, l is a logrecord object.
# for most processings we only need l
for k, l in truncate(MAX_RECORDS, q):