def test_camera_display_single():
""" test CameraDisplay functionality """
from ..mpl_camera import CameraDisplay
geom = CameraGeometry.from_name("LSTCam")
disp = CameraDisplay(geom)
image = np.random.normal(size=len(geom.pix_x))
disp.image = image
disp.add_colorbar()
disp.cmap = "nipy_spectral"
disp.set_limits_minmax(0, 10)
disp.set_limits_percent(95)
disp.enable_pixel_picker()
disp.highlight_pixels([1, 2, 3, 4, 5])
disp.norm = "log"
disp.norm = "symlog"
disp.cmap = "rainbow"
with pytest.raises(ValueError):
disp.image = np.ones(10)
with pytest.raises(ValueError):
disp.add_colorbar()
disp.add_ellipse(centroid=(0, 0), width=0.1, length=0.1, angle=0.1)
disp.clear_overlays()
def nsb_rate(
files, aux_basepath, dark_histo_file, param_file, template_filename,
output=None, plot="show", plot_nsb_range=None, norm="log",
plot_baselines=False, disable_bar=False, max_events=None, n_skip=10,
stars=True,
bias_resistance=1e4 * u.Ohm, cell_capacitance=5e-14 * u.Farad
):
files = np.atleast_1d(files)
if len(files) == 1 and not files[0].endswith('.fz'):
table = Table.read(files[0])[:max_events]
data = dict(table)
data['nsb_rate'] = np.array(data['nsb_rate']) * u.GHz
else:
dark_histo = Histogram1D.load(dark_histo_file)
n_pixel = len(DigiCam.geometry.neighbors)
pixels = np.arange(n_pixel, dtype=int)
with open(param_file) as file:
pulse_template = NormalizedPulseTemplate.load(template_filename)
pulse_area = pulse_template.integral() * u.ns
charge_to_amplitude = pulse_template.compute_charge_amplitude_ratio(7, 4)
calibration_parameters = yaml.load(file)
gain_integral = np.array(calibration_parameters['gain'])
gain_amplitude = gain_integral * charge_to_amplitude
crosstalk = np.array(calibration_parameters['mu_xt'])
events = calibration_event_stream(files, max_events=max_events,
disable_bar=disable_bar)
events = add_slow_data_calibration(
events, basepath=aux_basepath,
aux_services=('DriveSystem', )
)
data = {
"baseline": [],
"nsb_rate": [],
"good_pixels_mask": [],
"timestamp": [],
"event_id": [],
"az": [],
"el": [],
}
bad_pixels = get_bad_pixels(
calib_file=param_file, nsigma_gain=5, nsigma_elecnoise=5,
dark_histo=dark_histo_file, nsigma_dark=8, plot=None, output=None
)
events_skipped = 0
for event in events:
if event.event_type.INTERNAL not in event.event_type:
continue
events_skipped += 1
if events_skipped < n_skip:
continue
events_skipped = 0
data['baseline'].append(event.data.digicam_baseline)
baseline_shift = event.data.digicam_baseline - dark_histo.mean()
rate = _compute_nsb_rate(
baseline_shift=baseline_shift, gain=gain_amplitude,
pulse_area=pulse_area, crosstalk=crosstalk,
bias_resistance=bias_resistance, cell_capacitance=cell_capacitance
)
bad_pixels_event = np.unique(np.hstack(
(
bad_pixels,
pixels[rate < 0],
pixels[rate > 5 * u.GHz]
)
))
avg_matrix = _get_average_matrix_bad_pixels(
DigiCam.geometry, bad_pixels_event
)
good_pixels_mask = np.ones(n_pixel, dtype=bool)
good_pixels_mask[bad_pixels_event] = False
good_pixels = pixels[good_pixels_mask]
rate[bad_pixels_event] = avg_matrix[bad_pixels_event, :].dot(
rate[good_pixels]
)
data['good_pixels_mask'].append(good_pixels_mask)
data['timestamp'].append(event.data.local_time)
data['event_id'].append(event.event_id)
data['nsb_rate'].append(rate)
data['az'].append(event.slow_data.DriveSystem.current_position_az)
data['el'].append(event.slow_data.DriveSystem.current_position_el)
data['nsb_rate'] = np.array(data['nsb_rate']) * u.GHz
if output is not None:
table = Table(data)
if os.path.isfile(output):
os.remove(output)
table.write(output, format='fits')
time_obs = Time(
np.array(data['timestamp'], dtype=np.float64) * 1e-9,
format='unix'
)
if plot_baselines:
fig2, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 8), dpi=50)
baseline_std = np.std(data['baseline'], axis=0)
ax1.hist(baseline_std, 100)
ax1.set_xlabel('std(baseline) [LSB]')
# pixels_shown = np.arange(len(baseline_std))[baseline_std > 10]
pixels_shown = [834,]
ax2.plot_date(
time_obs.to_datetime(),
data['baseline'][:, pixels_shown],
'-'
)
ax2.set_xlabel('time')
ax2.set_ylabel('baseline [LSB]')
plt.tight_layout()
plt.show()
plt.close(fig2)
az_obs = np.array(data['az']) * u.deg
el_obs = np.array(data['el']) * u.deg
n_event = len(data['timestamp'])
fig1, ax = plt.subplots(1, 1, figsize=(16, 12), dpi=50)
date = datetime.fromtimestamp(data['timestamp'][0]*1e-9)
date_str = date.strftime("%H:%M:%S")
display = CameraDisplay(
DigiCam.geometry, ax=ax, norm=norm,
title='NSB rate [GHz], t=' + date_str
)
rate_ghz = np.array(data['nsb_rate'][0].to(u.GHz).value)
display.image = rate_ghz
if plot_nsb_range is None:
min_range_rate = np.max([np.min(rate_ghz), 50e-3])
plot_nsb_range = (min_range_rate, np.max(rate_ghz))
display.set_limits_minmax(*plot_nsb_range)
display.add_colorbar(ax=ax)
bad_pixels = np.arange(
len(data['good_pixels_mask'][0])
)[~data['good_pixels_mask'][0]]
display.highlight_pixels(bad_pixels, color='r', linewidth=2)
display.axes.set_xlim([-500., 500.])
display.axes.set_ylim([-500., 500.])
plt.tight_layout()
if stars is True:
stars_az, stars_alt, stars_pmag = get_stars_in_fov(
az_obs[0], el_obs[0], time_obs
)
stars_x, stars_y = transform_azel_to_xy(
stars_az, stars_alt, az_obs, el_obs
)
point_stars = []
for index_star in range(len(stars_pmag)):
point_star, = ax.plot(
stars_x[index_star, 0],
stars_y[index_star, 0],
'ok',
ms=20-2*stars_pmag[index_star],
mew=3,
mfc='None'
)
point_stars.append(point_star)
def update(i, display):
print('frame', i, '/', len(data['timestamp']))
display.image = data['nsb_rate'][i].to(u.GHz).value
date = datetime.fromtimestamp(data['timestamp'][i] * 1e-9)
date_str = date.strftime("%H:%M:%S")
display.axes.set_title('NSB rate [GHz], t=' + date_str)
bad_pixels = np.arange(
len(data['good_pixels_mask'][i])
)[~data['good_pixels_mask'][i]]
display.highlight_pixels(
bad_pixels, color='r', linewidth=2
)
if stars is True:
for index_star in range(len(stars_pmag)):
point_stars[index_star].set_xdata(
stars_x[index_star, i]
)
point_stars[index_star].set_ydata(
stars_y[index_star, i]
)
anim = FuncAnimation(
fig1,
update,
frames=len(data['timestamp']),
interval=20,
fargs=(display, )
)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=50, metadata=dict(artist='Y. Renier'),
bitrate=4000, codec='h263p')
output_path = os.path.dirname(plot)
if plot == "show" or \
(output_path != "" and not os.path.isdir(output_path)):
if not plot == "show":
print('WARNING: Path ' + output_path + ' for output trigger ' +
'uniformity does not exist, displaying the plot instead.\n')
display.enable_pixel_picker()
plt.show()
else:
anim.save(plot, writer=writer)
print(plot, 'created')
plt.close(fig1)
