def apply_ipp_2(db_session, center_date, query_pd, inst_list, hdf_data_dir,
n_intensity_bins=200, R0=1.01):
"""
Function to apply image pre-processing (limb-brightening, inter-instrument transformation) corrections
to EUV images for creation of maps. Three major differences from original function:
1. Expects query_pd to contain all images that should be corrected. No temporal
selection of images in this function.
2. Queries the DB directly for previous/next IIT and LBC values. No need to input
combo-query results.
3. methods_list is generated internally and outputted to be appended to an existing
list as appropriate.
@param db_session: database session from which to query correction variable values
@param center_date: date for querying
@param query_pd: pandas dataframe of euv_images
@param inst_list: instrument list
@param hdf_data_dir: directory of hdf5 files
@param n_intensity_bins: number of intensity bins
@param R0: radius
@return: image dataframe (identical to input 'query_pd', but retained for backward compatibility),
list of los images,
list of iit images,
indices used for correction,
methods list,
ref alpha, ref x
"""
start = time.time()
# create image lists
n_images = query_pd.shape[0]
los_list = [None] * n_images
iit_list = [None] * n_images
methods_list = db_funcs.generate_methdf(query_pd)
use_indices = [np.full((2048, 2048), True, dtype=bool)] * len(inst_list)
# convert date to correct format
print("\nStarting corrections for", center_date, "images:")
date_time = np.datetime64(center_date).astype(datetime.datetime)
# alpha, x for threshold
euvia_iit = db_funcs.get_correction_pars(db_session, meth_name="IIT",
date_obs=date_time, instrument='EUVI-A')
ref_alpha = euvia_iit[0]
ref_x = euvia_iit[1]
# create dataframe for date
date_pd = query_pd
if len(date_pd) == 0:
print("No Images to Process for this date.")
else:
for index in range(date_pd.shape[0]):
# get image row
image_row = date_pd.iloc[index]
print("Processing image number", image_row.data_id, "for LBC and IIT Corrections.")
# apply LBC
los_list[index], lbcc_image, mu_indices, use_ind, theoretic_query = \
lbcc_funcs.apply_lbc_2(db_session, hdf_data_dir, image_row=image_row,
n_intensity_bins=n_intensity_bins, R0=R0)
# update method with LBCC parameter values? Would need to associate each LBCC
# parameter set with an image # and store in DB. For now, simply record method
# without values. Same for IIT below.
# apply IIT
lbcc_image, iit_list[index], use_indices[index], alpha, x = \
iit_funcs.apply_iit_2(db_session, lbcc_image, use_ind,
los_list[index], R0=R0)
# set unused points to no_data_val
# if los_list[index].no_data_val is None:
# no_data_val = -9999.0
# iit_list[index].no_data_val = no_data_val
# else:
# no_data_val = iit_list[index].no_data_val
# iit_list[index].iit_data[~use_indices[index]] = no_data_val
# JT - this should be handled in minimum intensity merge, not here
# add methods to dataframe
ipp_method = {'meth_name': ("LBCC", "IIT"), 'meth_description': ["LBCC Theoretic Fit Method",
"IIT Fit Method"],
'var_name': ("LBCC", "IIT"), 'var_description': (" ", " ")}
methods_list[index] = methods_list[index].append(pd.DataFrame(data=ipp_method), sort=False)
# methods_list[inst_ind] = pd.DataFrame(data=ipp_method)
end = time.time()
print("Image Pre-Processing Corrections (Limb-Brightening and Inter-Instrument Transformation) have been "
"applied "
" in", end - start, "seconds.")
return date_pd, los_list, iit_list, use_indices, methods_list, ref_alpha, ref_x
sqlite_path=sqlite_path)
elif use_db == 'mysql-Q':
# setup database connection to MySQL database on Q
db_session = db_funcs.init_db_conn(db_name=use_db,
chd_base=db_class.Base,
user=user,
password=password)
#### 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)
#### STEP TWO: APPLY PRE-PROCESSING CORRECTIONS ####
# 1.) get dates
moving_avg_centers = synch_utils.get_dates(time_min=query_time_min,
time_max=query_time_max,
map_freq=map_freq)
# 2.) loop through center dates
h5file = h5.File(h5_filename, 'w')
for date_ind, center in enumerate(moving_avg_centers):
# choose which images to use in the same way we choose images for synchronic download
synch_images, cluster_method = synch_utils.select_synchronic_images(
center, del_interval, query_pd, inst_list)
if synch_images is None:
# no images fall in the appropriate range, skip
