def scale(observations_hdlr_ref, observations_hdlr, observations_hdlr_set,
iparams):
#scale two observations and return the merged observations and the
#updated observations set.
txt_scale = ''
postrefine_hdlr = postrefine_handler(iparams)
observations_hdlr, txt_out = postrefine_hdlr.scale_with_reference(
observations_hdlr_ref,
observations_hdlr=observations_hdlr,
use_binning=True)
txt_scale += txt_out
if observations_hdlr is not None:
merge_hdlr = merge_handler(iparams)
observations_hdlr_merge, txt_out = merge_hdlr.merge_observations_handler(
observations_hdlr_ref, observations_hdlr)
txt_scale += '\n' + txt_out
for observations_hdlr_in_set in observations_hdlr_set:
observations_hdlr_in_set.set_params(
postref_params=observations_hdlr.postref_params)
observations_hdlr_in_set.refresh()
else:
observations_hdlr_merge = observations_hdlr_ref
observations_hdlr_set = None
print txt_scale
return observations_hdlr_merge, observations_hdlr_set, txt_scale + '\n'
def scale_cluster_mproc(cluster_no, cluster_paths, iparams):
frame_files = read_pickles([cluster_paths[cluster_no]])
#scale and merge
observations_hdlr_set = []
txt_scale = ' SCALE CLUSTER %3.0f\n' % (cluster_no + 1)
postrefine_hdlr = postrefine_handler(iparams)
for i_frame in range(len(frame_files)):
observations_hdlr_0, txt_out = postrefine_hdlr.scale_0(
frame_files[i_frame])
txt_scale += '\n' + txt_out
if observations_hdlr_0 is not None:
observations_hdlr_0.refresh()
observations_hdlr_set.append(observations_hdlr_0)
merge_hdlr = merge_handler(iparams)
observations_hdlr_ref, observations_hdlr_selected_set, txt_out = merge_hdlr.merge_multi_observations_handler(
observations_hdlr_set, flag_show_summary=False)
txt_scale += '\n' + txt_out
if observations_hdlr_ref is None:
print txt_scale
return None, None, txt_scale + '\n'
print txt_scale
return observations_hdlr_ref, observations_hdlr_selected_set, txt_scale + '\n'
def postrefine_mproc(obs_hdlr_no, observations_hdlr_ref, observations_hdlr_set,
iparams):
observations_hdlr = observations_hdlr_set[obs_hdlr_no]
postrefine_hdlr = postrefine_handler(iparams)
observations_hdlr_out, txt_out = postrefine_hdlr.postrefine_with_reference(
observations_hdlr_ref, observations_hdlr)
observations_hdlr_out.refresh()
print txt_out
return observations_hdlr_out, txt_out + '\n'
def scale_observations_mproc(observations_no,
observations_hdlr_ref,
iparams,
observations_hdlr_set=None,
observations_file_set=None):
#scale an observation set to the reference set
txt_scale = ''
postrefine_hdlr = postrefine_handler(iparams)
if observations_hdlr_set is not None:
observations_hdlr, txt_out = postrefine_hdlr.scale_with_reference(
observations_hdlr_ref,
observations_hdlr=observations_hdlr_set[observations_no],
use_binning=True)
elif observations_file_set is not None:
observations_hdlr, txt_out = postrefine_hdlr.scale_with_reference(
observations_hdlr_ref,
filename=observations_file_set[observations_no],
use_binning=True)
txt_scale += txt_out
print txt_scale
return observations_hdlr, txt_scale + '\n'
n_conn_list.append(n_conn)
else:
conn_images_set.append([])
n_conn_list.append(0)
orgh = organize_handler(iparams)
txt_cluster = orgh.generate_clusters(observations_handler_set,
conn_images_set, n_conn_list)
print txt_cluster
txt_step1 += txt_cluster
#2. scale images in each cluster
txt_step2 = 'STEP 2: scale images in each cluster\n'
print txt_step2
path_to_cluster = iparams.run_no + '/clusters'
postref_hdlr = postrefine_handler(iparams)
cluster_paths = []
for cluster_lst_file in os.listdir(path_to_cluster):
observations_hdlr_ref = None
if cluster_lst_file.endswith('.lst'):
cluster_paths.append(path_to_cluster + '/' + cluster_lst_file)
def scale_cluster_mproc_wrapper(arg):
return scale_cluster_mproc(arg, cluster_paths, iparams)
results = pool_map(iterable=range(len(cluster_paths)),
func=scale_cluster_mproc_wrapper,
processes=iparams.n_processors)
observations_hdlr_scaled_set = []
i_found_good_cluster = 0
def get_ma_match_dict(image_seq, image_dict, iparams):
postref_hdlr = postrefine_handler(iparams)
observations_hdlr, txt_out = postref_hdlr.scale_0(image_dict[image_seq],
image_seq)
observations_full_dict = observations_hdlr.get_full_observations_as_dict()
return observations_full_dict