sqlite_path = os.path.join(database_dir, sqlite_filename)
db_session = db_funcs.init_db_conn_old(db_name=use_db,
chd_base=db_class.Base,
sqlite_path=sqlite_path)
elif use_db in ('mysql-Q', 'mysql-Q_test'):
# setup database connection to MySQL database on Q
db_session = db_funcs.init_db_conn_old(db_name=use_db,
chd_base=db_class.Base,
user=user,
password=password)
# 1.) get instrument combos based on timescale
query_time_min = center_time - (timescale / 2)
query_time_max = center_time + (timescale / 2)
lbc_combo_query, iit_combo_query = apply_lbc_iit.get_inst_combos(
db_session, inst_list, time_min=query_time_min, time_max=query_time_max)
#### STEP ONE: SELECT IMAGES ####
# 1.) query some images
query_pd = db_funcs.query_euv_images(db_session=db_session,
time_min=query_time_min,
time_max=query_time_max)
# 2.) generate a dataframe to record methods
methods_list = db_funcs.generate_methdf(query_pd)
# 3.) generate normal distribution
norm_dist = dp_funcs.gauss_time(query_pd, sigma)
#### LOOP THROUGH IMAGES ####
euv_combined = None
# generate map x,y grids. y grid centered on equator, x referenced from lon=0
map_y = np.linspace(y_range[0], y_range[1], map_nycoord, dtype='<f4')
map_x = np.linspace(x_range[0], x_range[1], map_nxcoord, dtype='<f4')
### --------- NOTHING TO UPDATE BELOW -------- ###
### determine subplots and lists needed
# fig, axes = plt.subplots(len(threshold_values1), sharex=True, sharey=True)
# map_list = [None] * len(threshold_values1)
# chd_map_list = [None] * len(threshold_values1)
# euv_combined = [[datatypes.PsiMap() for j in range(len(query_times))] for i in range(len(threshold_values1))]
# chd_combined = [[datatypes.PsiMap() for j in range(len(query_times))] for i in range(len(threshold_values1))]
### get instrument combos
lbc_combo_query, iit_combo_query = apply_lbc_iit.get_inst_combos(
db_session,
inst_list,
time_min=min(query_times),
time_max=max(query_times))
### LOOP THROUGH EACH OF THE DATES ###
for index, date in enumerate(query_times):
print("Creating synchronic maps for", date)
# times
query_time_min = date - datetime.timedelta(hours=map_freq / 2, minutes=5)
query_time_max = date + datetime.timedelta(hours=map_freq / 2, minutes=5)
#### STEP ONE: SELECT IMAGES ####
# 1.) query some images
query_pd = db_funcs.query_euv_images(db_session=db_session,
time_min=query_time_min,
time_max=query_time_max)
