import os, sys
base_path = os.path.dirname(__file__)
sys.path.append(os.path.abspath(os.path.join(base_path, '..')))
from common.pogs_analysis_tables import *
from common.database import db_init
from sqlalchemy.engine import create_engine
from sqlalchemy import select, exists
from sqlalchemy import func
from sqlalchemy.sql.expression import func as ffunc
pogs_connection = create_engine(
'mysql://*****:*****@munro.icrar.org/pogs_analysis').connect()
nn_connection = db_init('sqlite:///Database_run01.db')
filter_map = {
123: 'fuv',
124: 'nuv',
229: 'u',
230: 'g',
323: 'g',
324: 'r',
231: 'r',
325: 'i',
232: 'i',
326: 'z',
233: 'z',
327: 'y',
280: 'WISEW1',
)
parser.add_argument("-n", dest="num_to_load", type=int, nargs=1, help="Number of galaxies to load")
parser.add_argument(
"-r",
dest="run_id",
nargs="*",
help="Run ID folders to load. If ommitted, simply searches for all .fit files in subdirectories of the working directory.",
)
parser.add_argument("-d", dest="database", nargs=1, help="SQLite database to use")
args = vars(parser.parse_args())
working_directory = args["working_directory"][0]
num_to_load = args["num_to_load"][0]
run_ids = args["run_id"]
# Fire up the DB based on our command line args
db_init(config.DB_LOGIN + args["database"][0])
run_dirs = []
num_added = 0
sh_files = 0
current_run_dir = ""
# Is everything valid?
if os.path.exists(working_directory):
# Check each of the run_ids and check if they're valid
invalid_folders = []
for id in run_ids:
full_path = os.path.join(working_directory, id)
run_dirs.append(full_path)
if not os.path.exists(full_path):
invalid_folders.append(full_path)
#
import os, sys
base_path = os.path.dirname(__file__)
sys.path.append(os.path.abspath(os.path.join(base_path, '..')))
from common.pogs_analysis_tables import *
from common.database import db_init
from sqlalchemy.engine import create_engine
from sqlalchemy import select, exists
from sqlalchemy import func
from sqlalchemy.sql.expression import func as ffunc
pogs_connection = create_engine('mysql://*****:*****@munro.icrar.org/pogs_analysis').connect()
nn_connection = db_init('sqlite:///Database_run01.db')
filter_map = {123: 'fuv',
124: 'nuv',
229: 'u',
230: 'g',
323: 'g',
324: 'r',
231: 'r',
325: 'i',
232: 'i',
326: 'z',
233: 'z',
327: 'y',
280: 'WISEW1',
281: 'WISEW2',
out_tuple = list()
for count, item in enumerate(args):
# Apply permutation to each item, then append them to a list for outputting
out_tuple.append(item[perm])
return tuple(out_tuple)
inp = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
inp2 = [500, 700, 300, 900, 400, 150, 300, 700, 200, 600, 800, 900, 800]
print get_percentile_groups(inp2, 0.1)
print get_dataspace_groups(inp2, 0.1)
exit()
db_init('sqlite:///Database_run06.db')
DatabaseConfig = {
'database_connection_string': 'sqlite:///Database_run06.db',
'train_data': 200000,
'test_data': 1000,
'run_id': '06',
'output_type':
'median', # median, best_fit, best_fit_model, best_fit_inputs
'input_type': 'normal', # normal, Jy
'include_sigma': False, # True, False
'unknown_input_handler': None,
'input_filter_types': None
}
if check_temp('nn_last_tmp_input3.tmp', {1: 1}):
dest='run_id',
nargs='*',
help=
'Run ID folders to load. If ommitted, simply searches for all .fit files in subdirectories of the working directory.'
)
parser.add_argument('-d',
dest='database',
nargs=1,
help='SQLite database to use')
args = vars(parser.parse_args())
working_directory = args['working_directory'][0]
num_to_load = args['num_to_load'][0]
run_ids = args['run_id']
# Fire up the DB based on our command line args
db_init(config.DB_LOGIN + args['database'][0])
run_dirs = []
num_added = 0
sh_files = 0
current_run_dir = ''
# Is everything valid?
if os.path.exists(working_directory):
# Check each of the run_ids and check if they're valid
invalid_folders = []
for id in run_ids:
full_path = os.path.join(working_directory, id)
run_dirs.append(full_path)
if not os.path.exists(full_path):
invalid_folders.append(full_path)
out_tuple = list()
for count, item in enumerate(args):
# Apply permutation to each item, then append them to a list for outputting
out_tuple.append(item[perm])
return tuple(out_tuple)
inp = [0,1,2,3,4,5,6,7,8,9,10]
inp2 = [500,700,300,900,400,150,300,700,200,600,800,900,800]
print get_percentile_groups(inp2, 0.1)
print get_dataspace_groups(inp2, 0.1)
exit()
db_init('sqlite:///Database_run06.db')
DatabaseConfig = {'database_connection_string': 'sqlite:///Database_run06.db',
'train_data': 200000,
'test_data': 1000,
'run_id': '06',
'output_type': 'median', # median, best_fit, best_fit_model, best_fit_inputs
'input_type': 'normal', # normal, Jy
'include_sigma': False, # True, False
'unknown_input_handler': None,
'input_filter_types': None
}
if check_temp('nn_last_tmp_input3.tmp', {1:1}):
all_in, all_out, redshifts, galaxy_ids = load_from_file('nn_last_tmp_input3.tmp')
else:
