def get_pixel_nvs(digicam_config_file=camera_config_file):
"""Camera and Geometry objects
(mapping, pixel, patch + x,y coordinates pixels)
"""
digicam = Camera(_config_file=digicam_config_file)
digicam_geometry = geometry.generate_geometry_from_camera(camera=digicam)
pixels_pos_mm = np.array(
[digicam_geometry.pix_x.to(u.mm),
digicam_geometry.pix_y.to(u.mm)]).transpose()
pixels_pos_mm = pixels_pos_mm.dot(np.array([[0, -1], [1, 0]]))
pixels_v1 = pixels_pos_mm[
digicam_geometry.neighbors[0][1], :] - pixels_pos_mm[0, :]
pixels_v2 = pixels_pos_mm[
digicam_geometry.neighbors[0][0], :] - pixels_pos_mm[0, :]
index_to_pos = np.array([pixels_v1, pixels_v2]).transpose()
relative_pos = (pixels_pos_mm - pixels_pos_mm[0, :]).transpose()
pixels_nvs = np.linalg.pinv(index_to_pos).dot(relative_pos)
pixels_nvs -= np.mean(pixels_nvs, axis=1).reshape(2, 1)
return pixels_nvs
from digicampipe.calib.camera import filter, random_triggers, r1, dl0, dl2, dl1
from digicampipe.io.event_stream import event_stream
from digicampipe.utils import utils, geometry
from cts_core.camera import Camera
import astropy.units as u
from digicampipe.io.containers import save_to_pickle_gz
if __name__ == '__main__':
# Data configuration
directory = '/mnt/calib_data/first_light/20170831/'
filename = directory + 'CameraDigicam@sst1mserver_0_000.%d.fits.fz'
# file_list = [filename % number for number in range(110,120)]
urls = [filename % 110]
camera_config_file = '/usr/src/cts/config/camera_config.cfg'
digicam = Camera(_config_file=camera_config_file)
digicam_geometry = geometry.generate_geometry_from_camera(
camera=digicam, source_x=0 * u.mm, source_y=0. * u.mm)
# Trigger configuration
unwanted_patch = [391, 392, 403, 404, 405, 416, 417]
# Integration configuration
time_integration_options = {
'mask': None,
'mask_edges': None,
'peak': None,
'window_start': 3,
'window_width': 7,
'threshold_saturation': 3500,
'n_samples': 50,
'timing_width': 6,
def main(
files,
outfile_path,
outfile_suffix,
picture_threshold=15,
boundary_threshold=7,
baseline0=9,
baseline1=15,
):
# Input/Output files
digicam_config_file = args['--camera_config']
dark_baseline = None
# Source coordinates (in camera frame)
source_x = 0. * u.mm
source_y = 0. * u.mm
# Camera and Geometry objects
# (mapping, pixel, patch + x,y coordinates pixels)
digicam = Camera(_config_file=digicam_config_file)
digicam_geometry = geometry.generate_geometry_from_camera(
camera=digicam, source_x=source_x, source_y=source_y)
# Noisy pixels not taken into account in Hillas
pixel_not_wanted = [
1038, 1039, 1002, 1003, 1004, 966, 967, 968, 930, 931, 932, 896
]
additional_mask = np.ones(1296)
additional_mask[pixel_not_wanted] = 0
additional_mask = additional_mask > 0
# Integration configuration (signal reco.)
time_integration_options = {
'mask': None,
'mask_edges': None,
'peak': None,
'window_start': 3,
'window_width': 7, # length of integration window
'threshold_saturation': np.inf,
'n_samples': 50,
'timing_width': 6,
'central_sample': 11
}
peak_position = utils.fake_timing_hist(
time_integration_options['n_samples'],
time_integration_options['timing_width'],
time_integration_options['central_sample'])
(time_integration_options['peak'], time_integration_options['mask'],
time_integration_options['mask_edges']) = utils.generate_timing_mask(
time_integration_options['window_start'],
time_integration_options['window_width'], peak_position)
# Image cleaning configuration
shower_distance = 200 * u.mm
# Filtering on big showers
min_photon = 50
# Config for Hillas parameters analysis
n_showers = 100000000
reclean = True
# Define the event stream
data_stream = event_stream(files,
camera_geometry=digicam_geometry,
camera=digicam)
# Clean pixels
data_stream = filter.set_pixels_to_zero(data_stream,
unwanted_pixels=pixel_not_wanted)
# Computation of baseline
#
# Methods:
#
# simtel_baseline.baseline_data()
# Baseline is computed as a mean of 'n_bins0' first time samples,
# 'n_bins1' last time samples.
# A key assumption in this method is that the shower in simulated
# data is somewhere in the middle of 50 samples.
# Each pixel in each event has its own baseline
#
# simtel_baseline.baseline_simtel()
# Baseline is taken as a value reported by sim_telarray
# event.mc.tel[tel_id].pedestal/event.r0.tel[tel_id].num_samples.
# That should be OK for all sim_telarray version from April
# 2018, where an error for DC coupled simulations was corrected.
data_stream = simtel_baseline.baseline_data(data_stream,
n_bins0=baseline0,
n_bins1=baseline1)
# data_stream = simtel_baseline.baseline_simtel(data_stream)
# Run the r1 calibration (i.e baseline substraction)
data_stream = r1.calibrate_to_r1(data_stream, dark_baseline)
# Run the dl1 calibration (compute charge in photons + cleaning)
data_stream = dl1.calibrate_to_dl1(data_stream,
time_integration_options,
additional_mask=additional_mask,
picture_threshold=picture_threshold,
boundary_threshold=boundary_threshold)
# Return only showers with total number of p.e. above min_photon
data_stream = filter.filter_shower(data_stream, min_photon=min_photon)
# Save cleaned events - pixels and corresponding values
filename_pix = 'pixels.txt'
filename_eventsimage = 'events_image_' + outfile_suffix + '.txt'
data_stream = events_image.save_events(data_stream,
outfile_path + filename_pix,
outfile_path + filename_eventsimage)
# Save simulated shower paramters
filename_showerparam = 'pipedmc_param_' + outfile_suffix + '.txt'
data_stream = mc_shower.save_shower(data_stream,
outfile_path + filename_showerparam)
# Run the dl2 calibration (Hillas)
data_stream = dl2.calibrate_to_dl2(data_stream,
reclean=reclean,
shower_distance=shower_distance)
# Save arrival times of photons in pixels passed cleaning
filename_timing = 'timing_' + outfile_suffix + '.txt'
data_stream = events_image.save_timing(data_stream,
outfile_path + filename_timing)
# Save mean baseline in event pixels
filename_baseline = ('baseline_' + outfile_suffix + '_bas' +
str(baseline0).zfill(2) + str(baseline1).zfill(2) +
'.txt')
# data_stream = simtel_baseline.save_mean_event_baseline(
# data_stream, directory + filename_baseline)
if args['--display']:
with plt.style.context('ggplot'):
display = EventViewer(data_stream)
display.draw()
else:
# Save Hillas
hillas_filename = 'hillas_' + outfile_suffix
save_hillas_parameters(data_stream=data_stream,
n_showers=n_showers,
output_filename=outfile_path + hillas_filename)
# save_hillas_parameters_in_text(
# data_stream=data_stream,
# output_filename=outfile_path + hillas_filename)
"""
'tests',
'resources',
'example_100_evts.000.fits.fz'
)
)
digicam_config_file = pkg_resources.resource_filename(
'digicampipe',
os.path.join(
'tests',
'resources',
'camera_config.cfg'
)
)
digicam = Camera(_config_file=digicam_config_file)
digicam_geometry = geometry.generate_geometry_from_camera(camera=digicam)
def test_and_benchmark_simple_event_source(benchmark):
@benchmark
def simple_loop():
for _ in zfits_event_source(
example_file_path,
camera=digicam,
camera_geometry=digicam_geometry,
expert_mode=False,):
pass