def test_hillas_failure():
geom, image = create_sample_image(psi='0d')
blank_image = zeros_like(image)
with pytest.raises(HillasParameterizationError):
hillas_parameters_1(geom, blank_image)
with pytest.raises(HillasParameterizationError):
hillas_parameters_2(geom, blank_image)
with pytest.raises(HillasParameterizationError):
hillas_parameters_3(geom, blank_image)
with pytest.raises(HillasParameterizationError):
hillas_parameters_4(geom, blank_image)
def do_test_hillas(withunits=True):
"""
test all Hillas-parameter routines on a sample image and see if they
agree with eachother and with the toy model (assuming the toy model code
is correct)
"""
# try all quadrants
for psi_angle in ['30d', '120d', '-30d', '-120d']:
px, py, image = create_sample_image(psi_angle)
results = {}
if withunits:
px = px * u.cm
py = py * u.cm
results['v1'] = hillas_parameters_1(px, py, image)
results['v2'] = hillas_parameters_2(px, py, image)
results['v3'] = hillas_parameters_3(px, py, image)
results['v4'] = hillas_parameters_4(px, py, image)
# compare each method's output
for aa in results:
for bb in results:
if aa is not bb:
print("comparing {} to {}".format(aa, bb))
compare_result(results[aa].length, results[bb].length)
compare_result(results[aa].width, results[bb].width)
compare_result(results[aa].r, results[bb].r)
compare_result(results[aa].phi.deg, results[bb].phi.deg)
compare_result(results[aa].psi.deg, results[bb].psi.deg)
compare_result(results[aa].miss, results[bb].miss)
compare_result(results[aa].skewness, results[bb].skewness)
def test_hillas():
"""
test all Hillas-parameter routines on a sample image and see if they
agree with eachother and with the toy model (assuming the toy model code
is correct)
"""
px, py, image = create_sample_image()
results = {}
results['v1'] = hillas_parameters_1(px, py, image)
results['v2'] = hillas_parameters_2(px, py, image)
results['v3'] = hillas_parameters_3(px, py, image)
results['v4'] = hillas_parameters_4(px, py, image)
# compare each method's output
for aa in results:
for bb in results:
if aa is not bb:
print("comparing {} to {}".format(aa, bb))
assert isclose(results[aa].length, results[bb].length)
assert isclose(results[aa].width, results[bb].width)
assert isclose(results[aa].r, results[bb].r)
assert isclose(results[aa].phi.deg, results[bb].phi.deg)
assert isclose(results[aa].psi.deg, results[bb].psi.deg)
assert isclose(results[aa].miss, results[bb].miss)
assert isclose(results[aa].skewness, results[bb].skewness)
def do_test_hillas(withunits=True):
"""
test all Hillas-parameter routines on a sample image and see if they
agree with eachother and with the toy model (assuming the toy model code
is correct)
"""
px, py, image = create_sample_image()
results = {}
if withunits:
px = px * u.cm
py = py * u.cm
results['v1'] = hillas_parameters_1(px, py, image)
results['v2'] = hillas_parameters_2(px, py, image)
results['v3'] = hillas_parameters_3(px, py, image)
results['v4'] = hillas_parameters_4(px, py, image)
# compare each method's output
for aa in results:
for bb in results:
if aa is not bb:
print("comparing {} to {}".format(aa,bb))
compare_result(results[aa].length, results[bb].length)
compare_result(results[aa].width, results[bb].width)
compare_result(results[aa].r, results[bb].r)
compare_result(results[aa].phi.deg, results[bb].phi.deg)
compare_result(results[aa].psi.deg, results[bb].psi.deg)
compare_result(results[aa].miss, results[bb].miss)
compare_result(results[aa].skewness, results[bb].skewness)
def test_hillas_selected():
"""
test Hillas-parameter routines on a sample image with selected values
against a sample image with masked values set tozero
"""
geom, image = create_sample_image_zeros()
geom_selected, image_ma = create_sample_image_selected_pixel()
results = hillas_parameters_4(geom, image)
results_ma = hillas_parameters_4(geom_selected, image_ma)
compare_result(results.length, results_ma.length)
compare_result(results.width, results_ma.width)
compare_result(results.r, results_ma.r)
compare_result(results.phi.deg, results_ma.phi.deg)
compare_result(results.psi.deg, results_ma.psi.deg)
compare_result(results.skewness, results_ma.skewness)
def test_hillas_masked():
"""
test Hillas-parameter routines on a sample image with masked values set to
zero against a sample image with values masked with a numpy.ma.masked_array
"""
geom, image = create_sample_image_zeros()
geom, image_ma = create_sample_image_masked()
results = hillas_parameters_4(geom, image)
results_ma = hillas_parameters_4(geom, image_ma)
compare_result(results.length, results_ma.length)
compare_result(results.width, results_ma.width)
compare_result(results.r, results_ma.r)
compare_result(results.phi.deg, results_ma.phi.deg)
compare_result(results.psi.deg, results_ma.psi.deg)
compare_result(results.miss, results_ma.miss)
compare_result(results.skewness, results_ma.skewness)
def test_hillas_api_change():
with pytest.raises(TypeError):
hillas_parameters_4(arange(10), arange(10), arange(10))
def test_hillas_api_change():
import numpy as np
with pytest.raises(ValueError):
hillas_parameters_4(np.arange(10), np.arange(10), np.arange(10))
def test_hillas_container():
geom, image = create_sample_image(psi='0d')
params = hillas_parameters_4(geom, image, container=True)
assert type(params) is HillasParametersContainer
def get_hillas_parameters(geom: CameraGeometry, image, implementation=4):
r"""Return Hillas parameters [hillas]_ of the given ``image``.
Short description of Hillas parameters:
* x: x position of the ellipse's center (in meter)
* y: y position of the ellipse's center (in meter)
* length: measure of the RMS extent along the major axis (in meter) (length >= width)
* width: measure of the RMS extent along the minor axis (in meter) (length >= width)
* intensity: the number of photoelectrons in the image (in PE) (size = np.sum(image))
* psi: angle of the shower (in radian)
* phi: polar coordinate of centroid (in radian)
* r: radial coordinate of centroid (in meter)
* kurtosis: Kurtosis is a measure of whether the data are heavy-tailed or light-tailed
relative to a normal distribution.
That is, data sets with high kurtosis tend to have heavy tails, or outliers.
Data sets with low kurtosis tend to have light tails, or lack of outliers.
See http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
* skewness: Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
A distribution, or data set, is symmetric if it looks the same to the left
and right of the center point. See http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
See https://github.com/cta-observatory/ctapipe/blob/master/ctapipe/image/hillas.py#L83
for more information.
Parameters
----------
geom : CameraGeomatry
The geometry of the image to parametrize
image : Numpy array
The image to parametrize
implementation : integer
Tell which ctapipe's implementation to use (1 or 2).
Returns
-------
namedtuple
Hillas parameters for the given ``image``
References
----------
.. [hillas] Appendix of the Whipple Crab paper Weekes et al. (1998)
http://adsabs.harvard.edu/abs/1989ApJ...342..379W
"""
# Copy image to prevent tricky bugs
image = image.copy()
if implementation == 1:
params = hillas_parameters_1(geom, image)
elif implementation == 2:
params = hillas_parameters_2(geom, image)
elif implementation == 3:
params = hillas_parameters_3(geom, image)
elif implementation == 4:
params = hillas_parameters_4(geom, image)
else:
raise ValueError("Wrong Hillas implementation ID.")
return params
def start(self):
# Get first event information
first_event = self.reader.get_event(0)
n_pixels = first_event.inst.num_pixels[0]
n_samples = first_event.r0.tel[0].num_samples
pos = first_event.inst.pixel_pos[0]
foclen = first_event.inst.optical_foclen[0]
geom = CameraGeometry.guess(*pos, foclen)
# Setup Output
output_dir = self.reader.output_directory
title = self.reader.filename
title = title[:title.find("_")]
# Prepare Output
if not exists(output_dir):
self.log.info("Creating directory: {}".format(output_dir))
makedirs(output_dir)
output_path = join(output_dir, title + "_events.pdf")
# Setup plot
fig = plt.figure(figsize=(10, 10))
ax_camera = fig.add_subplot(1, 1, 1)
fig.patch.set_visible(False)
ax_camera.axis('off')
camera = CameraDisplay(geom,
ax=ax_camera,
image=np.zeros(2048),
cmap='viridis')
camera.add_colorbar()
cb = camera.colorbar
camera.colorbar.set_label("Amplitude (p.e.)")
fig.suptitle(title)
source = self.reader.read()
desc = "Looping through file"
with PdfPages(output_path) as pdf:
for event in tqdm(source, desc=desc):
ev = event.count
event_id = event.r0.event_id
self.r1.calibrate(event)
self.dl0.reduce(event)
self.dl1.calibrate(event)
for t in event.r0.tels_with_data:
dl1 = event.dl1.tel[t].image[0]
# Cleaning
tc = tailcuts_clean(geom, dl1, 20, 10)
if not tc.any():
continue
cleaned_dl1 = np.ma.masked_array(dl1, mask=~tc)
try:
# hillas = hillas_parameters(*pos, cleaned_tc)
hillas = hillas_parameters_4(*pos, cleaned_dl1)
except HillasParameterizationError:
continue
ax_camera.cla()
camera = CameraDisplay(geom,
ax=ax_camera,
image=np.zeros(2048),
cmap='viridis')
camera.colorbar = cb
camera.image = dl1
max_ = cleaned_dl1.max() # np.percentile(dl1, 99.9)
min_ = np.percentile(dl1, 0.1)
camera.set_limits_minmax(min_, max_)
camera.highlight_pixels(tc, 'white')
camera.overlay_moments(hillas, color='red')
camera.update(True)
ax_camera.set_title("Event: {}".format(event_id))
ax_camera.axis('off')
pdf.savefig(fig)
self.log.info("Created images: {}".format(output_path))
