def _update_completed_things(current, max_num, thing):
io.clear_line()
io.write("{current} out of {max} {thing} complete. ".format(
current=current,
max=max_num,
thing=thing
))
io.flush()
def _source_free_region(counter, current, max_num):
io.clear_line()
io.write("Encountered a source-free region -- recalculating...{counter} - {current}/{max} complete".format(
counter=counter,
current=current,
max=max_num
))
io.flush()
def _update_completed_things(current, max_num, thing):
io.move_cursor_left(1000)
io.write("{current} out of {max} {thing} complete.".format(
current=current,
max=max_num,
thing=thing
))
io.flush()
def _update_effective_exposure_time(obsid, current_region, number_regions, time_elapsed):
#io.clear_line()
#io.write("{current_region} of {num_regions} complete. Time elapsed: {time}".format(
print("ObsID {obsid} -\t{current_region} of {num_regions} complete. Time elapsed: {time}".format(
obsid=obsid,
current_region=current_region,
num_regions=number_regions,
time=time_elapsed
))
io.flush()
def calculate_effective_time_for(observation: cluster.Observation):
"""This function returns 2 image maps, data and background, of the cluster representing the same area
of the cluster the scale map represents. Each pixel of the map represents the effective time observed
for each of the ACB regions. The effective observed time is essentially the integrated observing time
for each acb region. This is value differs from a simple, area of region * exposure time as some parts
of the region may be masked (i.e. a removed point source within the ACB region)."""
print("Starting observation {obs}".format(obs=observation.id))
start_time = time.time()
scale_map = observation.cluster.scale_map
nx = scale_map.shape[0]
ny = scale_map.shape[1]
effective_data_times = np.zeros(scale_map.shape)
effective_background_times = np.zeros(scale_map.shape)
if observation.acisI_nosrc_combined_mask.shape != scale_map.shape:
observation.reproject_nosrc_combined_mask(observation.cluster.scale_map_file)
if observation.acisI_combined_mask.shape != scale_map.shape:
observation.reproject_combined_mask(observation.cluster.scale_map_file)
high_energy_data = observation.acisI_high_energy_combined_image
background = observation.backI_high_energy_combined_image
sum_acis_high_energy = np.sum(high_energy_data) # get the total counts in the high energy image
sum_back_high_energy = np.sum(background)
bg_to_data_ratio = sum_back_high_energy / sum_acis_high_energy
source_subtracted_mask = observation.acisI_nosrc_combined_mask
exposure_time = observation.acisI_high_energy_combined_image_header['EXPOSURE']
indices = np.vstack(np.where(observation.acisI_combined_mask * scale_map > 0)).T
total = len(indices)
counter = 1
for index in indices:
x,y = index
if counter % 5000 == 0:
time_elapsed = time.strftime("%H h %M m %S s",
time.gmtime(time.time() - start_time))
print("ObsID {}\t {} of {} regions calculated. Time elapsed: {}. Avg {:2f} ms/region".format(
observation.id,
counter, total, time_elapsed, ((time.time() - start_time)/counter)*1000))
io.flush()
radius_map = generate_radius_map(x, y, nx, ny)
circle_mask = radius_map <= scale_map[x, y]
source_subtracted_area = np.sum(source_subtracted_mask[circle_mask])
total_area = source_subtracted_mask[circle_mask].size
fractional_area = source_subtracted_area / total_area
fractional_exposure_time = fractional_area * exposure_time
effective_data_times[x, y] = fractional_exposure_time
effective_background_times[x, y] = fractional_exposure_time * bg_to_data_ratio
counter += 1
observation.effective_data_time = effective_data_times
observation.effective_background_time = effective_background_times
time_elapsed = time.strftime("%H hours %M minutes %S seconds.",
time.gmtime(time.time() - start_time))
print("ObsID {} complete. Time elapsed: {}".format(observation.id, time_elapsed))