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 plot_ellipse_shower_on_image_meter(axis, image_array, pixels_position):
xx, yy = pixels_position[0], pixels_position[1]
hillas = hillas_parameters_1(xx.flatten(), # * u.meter,
yy.flatten(), # * u.meter,
image_array.flatten())
centroid = (hillas.x.value, hillas.y.value)
length = hillas.length.value
width = hillas.width.value
angle = hillas.psi.to(u.rad).value
print("centroid:", centroid)
print("length:", length)
print("width:", width)
print("angle:", angle)
#a, b, c = common.angle_and_point_to_equation(angle, centroid)
#print("a:", a)
#print("b:", b)
#print("c:", c)
#x = np.array([-0.2, 0.2])
#f = lambda x: a/(-b) * x + c/(-b)
#axis.plot(x, f(x), "--b")
#print("DEBUG:", hillas[7].value, angle, np.degrees(angle))
ellipse = Ellipse(xy=centroid, width=length, height=width, angle=np.degrees(angle), fill=False, color='red', lw=2)
axis.axes.add_patch(ellipse)
title = axis.axes.get_title()
axis.axes.set_title("{} ({:.2f}°)".format(title, np.degrees(angle)))
# Plot centroid
axis.scatter(*centroid)
# Plot shower axis
#axis.arrow(0, 0, 0.1, 0, head_width=0.001, head_length=0.005, fc='k', ec='k')
#axis.arrow(0, 0, 0, 0.1, head_width=0.001, head_length=0.005, fc='k', ec='k')
p1_x = centroid[0]
p1_y = centroid[1]
p2_x = p1_x + math.cos(angle)
p2_y = p1_y + math.sin(angle)
#p2_x = p1_x + (length / 2.) * math.cos(angle)
#p2_y = p1_y + (length / 2.) * math.sin(angle)
#print(math.cos(math.pi/2.))
#print(math.sin(math.pi/2.))
#p2_x = p1_x + (length / 2.) * math.cos(math.pi/2.)
#p2_y = p1_y + (length / 2.) * math.sin(math.pi/2.)
#print(p1_x, p2_x)
#print(p1_y, p2_x)
axis.arrow(p1_x, p1_y, p2_x, p2_y, head_width=0.001, head_length=0.005, fc='r', ec='r')
p3_x = p1_x + math.cos(angle + math.pi/2.)
p3_y = p1_y + math.sin(angle + math.pi/2.)
#p3_x = p1_x + (width / 2.) * math.cos(angle + math.pi/2.)
#p3_y = p1_y + (width / 2.) * math.sin(angle + math.pi/2.)
axis.arrow(p1_x, p1_y, p3_x, p3_y, head_width=0.001, head_length=0.005, fc='g', ec='g')
def update_plots(self, data=None, save=False): # data is for event callers
if self.current_file_path is not None:
# Read the selected file #########
fits_images_dict, fits_metadata_dict = images.load_benchmark_images(self.current_file_path)
input_img = fits_images_dict["input_image"]
reference_img = fits_images_dict["reference_image"]
pixels_position = fits_images_dict["pixels_position"]
if input_img.ndim != 2:
raise Exception("Unexpected error: the input FITS file should contain a 2D array.")
if reference_img.ndim != 2:
raise Exception("Unexpected error: the input FITS file should contain a 2D array.")
if self.kill_isolated_pixels_on_ref:
reference_img = scipy_kill_isolated_pixels(reference_img)
# Tailcut #####################
#input_img_copy = copy.deepcopy(input_img)
input_img_copy = input_img.astype('float64', copy=True)
tailcut = tailcut_mod.Tailcut()
initial_time = time.perf_counter()
tailcut_cleaned_img = tailcut.clean_image(input_img_copy,
high_threshold=10,
low_threshold=5,
kill_isolated_pixels=self.kill_isolated_pixels)
tailcut_execution_time = time.perf_counter() - initial_time
# Wavelets ####################
#input_img_copy = copy.deepcopy(input_img)
input_img_copy = input_img.astype('float64', copy=True)
wavelets = wavelets_mod.WaveletTransform()
option_string = self.wavelets_options_entry.get_text()
print(option_string)
initial_time = time.perf_counter()
wavelets_cleaned_img = wavelets.clean_image(input_img_copy,
kill_isolated_pixels=self.kill_isolated_pixels,
input_image_scale=self.input_image_scale,
offset_after_calibration=self.offset_after_calibration,
verbose=True,
raw_option_string=option_string)
wavelets_execution_time = time.perf_counter() - initial_time
# Execution time ##############
print("Tailcut execution time: ", tailcut_execution_time) # TODO
print("Wavelets execution time: ", wavelets_execution_time) # TODO
# Tailcut scores ##############
tailcut_title_suffix = ""
try:
tailcut_score_tuple, tailcut_score_name_tuple = assess_mod.assess_image_cleaning(input_img,
tailcut_cleaned_img,
reference_img,
pixels_position,
benchmark_method="all")
if self.show_scores:
tailcut_title_suffix += " ("
for name, score in zip(tailcut_score_name_tuple, tailcut_score_tuple):
if name == "e_shape":
tailcut_title_suffix += " Es="
tailcut_title_suffix += "{:.2e}".format(score)
elif name == "e_energy":
tailcut_title_suffix += " Ee="
tailcut_title_suffix += "{:.2e}".format(score)
elif name == "hillas_theta":
tailcut_title_suffix += " Th="
tailcut_title_suffix += "{:.2f}".format(score)
tailcut_title_suffix += " )"
print("Tailcut:")
for name in tailcut_score_name_tuple:
print("{:>20}".format(name), end=" ")
print()
for score in tailcut_score_tuple:
print("{:20.12f}".format(score), end=" ")
print()
except assess_mod.AssessError:
print("Tailcut: ", str(assess_mod.AssessError))
# Wavelets scores #############
wavelets_title_suffix = ""
try:
wavelets_score_tuple, wavelets_score_name_tuple = assess_mod.assess_image_cleaning(input_img,
wavelets_cleaned_img,
reference_img,
pixels_position,
benchmark_method="all")
if self.show_scores:
wavelets_title_suffix += " ("
for name, score in zip(wavelets_score_name_tuple, wavelets_score_tuple):
if name == "e_shape":
wavelets_title_suffix += " Es="
wavelets_title_suffix += "{:.2e}".format(score)
elif name == "e_energy":
wavelets_title_suffix += " Ee="
wavelets_title_suffix += "{:.2e}".format(score)
elif name == "hillas_theta":
wavelets_title_suffix += " Th="
wavelets_title_suffix += "{:.2f}".format(score)
wavelets_title_suffix += " )"
print("Wavelets:")
for name in wavelets_score_name_tuple:
print("{:>20}".format(name), end=" ")
print()
for score in wavelets_score_tuple:
print("{:20.12f}".format(score), end=" ")
print()
except assess_mod.AssessError:
print("Wavelets: ", str(assess_mod.AssessError))
# Update the widget ###########
self.clear_figure()
ax1 = self.fig.add_subplot(221)
ax2 = self.fig.add_subplot(222)
ax3 = self.fig.add_subplot(223)
ax4 = self.fig.add_subplot(224)
if self.plot_histogram:
self._draw_histogram(ax1, input_img, "Input")
self._draw_histogram(ax2, reference_img, "Reference")
self._draw_histogram(ax3, tailcut_cleaned_img, "Tailcut" + tailcut_title_suffix)
self._draw_histogram(ax4, wavelets_cleaned_img, "Wavelets" + wavelets_title_suffix)
else:
# AX1 #######
self._draw_image(ax1, input_img, "Input", pixels_position=pixels_position)
# AX2 #######
self._draw_image(ax2, reference_img, "Reference", pixels_position=pixels_position)
if self.plot_ellipse_shower:
try:
common.plot_ellipse_shower_on_image_meter(ax2, reference_img, pixels_position)
except Exception as e:
print(e)
# AX3 #######
if self.plot_perpendicular_hit_distribution is not None:
if self.use_ref_angle_for_perpendicular_hit_distribution:
image_array = copy.deepcopy(reference_img)
xx, yy = pixels_position[0], pixels_position[1]
common_hillas_parameters = hillas_parameters_1(xx.flatten() * u.meter,
yy.flatten() * u.meter,
image_array.flatten())
else:
common_hillas_parameters = None
bins = np.linspace(-0.04, 0.04, 21)
if self.plot_perpendicular_hit_distribution == "Tailcut":
common.plot_perpendicular_hit_distribution(ax3,
[reference_img, tailcut_cleaned_img],
pixels_position,
bins=bins,
label_list=["Ref.", "Cleaned TC"],
hist_type="step",
common_hillas_parameters=common_hillas_parameters,
plot_ratio=True)
elif self.plot_perpendicular_hit_distribution == "Wavelet":
common.plot_perpendicular_hit_distribution(ax3,
[reference_img, wavelets_cleaned_img],
pixels_position,
bins=bins,
label_list=["Ref.", "Cleaned WT"],
hist_type="step",
common_hillas_parameters=common_hillas_parameters,
plot_ratio=True)
ax3.set_title("Perpendicular hit distribution")
ax3.set_xlabel("Distance to the shower axis (in meter)", fontsize=16)
ax3.set_ylabel("Photoelectrons", fontsize=16)
else:
self._draw_image(ax3, tailcut_cleaned_img, "Tailcut" + tailcut_title_suffix, pixels_position=pixels_position)
if self.plot_ellipse_shower:
if self.plot_perpendicular_hit_distribution is None:
try:
# Show ellipse only if "perpendicular hit distribution" is off
common.plot_ellipse_shower_on_image_meter(ax3, tailcut_cleaned_img, pixels_position)
except Exception as e:
print(e)
# AX4 #######
if self.plot_perpendicular_hit_distribution == "Tailcut":
self._draw_image(ax4, tailcut_cleaned_img, "Tailcut" + tailcut_title_suffix, pixels_position=pixels_position)
if self.plot_ellipse_shower:
if (self.plot_perpendicular_hit_distribution is not None) and self.use_ref_angle_for_perpendicular_hit_distribution:
try:
common.plot_ellipse_shower_on_image_meter(ax4, reference_img, pixels_position)
except Exception as e:
print(e)
else:
try:
common.plot_ellipse_shower_on_image_meter(ax4, tailcut_cleaned_img, pixels_position)
except Exception as e:
print(e)
else:
self._draw_image(ax4, wavelets_cleaned_img, "Wavelets" + wavelets_title_suffix, pixels_position=pixels_position)
if self.plot_ellipse_shower:
if (self.plot_perpendicular_hit_distribution is not None) and self.use_ref_angle_for_perpendicular_hit_distribution:
try:
common.plot_ellipse_shower_on_image_meter(ax4, reference_img, pixels_position)
except Exception as e:
print(e)
else:
try:
common.plot_ellipse_shower_on_image_meter(ax4, wavelets_cleaned_img, pixels_position)
except Exception as e:
print(e)
plt.suptitle("{:.3f} TeV ({} photoelectrons in reference image) - Event {} - Telescope {}".format(fits_metadata_dict["mc_energy"], int(fits_metadata_dict["npe"]), fits_metadata_dict["event_id"], fits_metadata_dict["tel_id"]), fontsize=18)
if save:
output_file_path_base = "ev{}_tel{}".format(fits_metadata_dict["event_id"], fits_metadata_dict["tel_id"]) # TODO: add WT options
if self.plot_histogram:
output_file_path_base += "_hist"
if self.plot_log_scale:
output_file_path_base += "_log"
## Save in PDF
#output_file_path = output_file_path_base + ".pdf"
#print("Save", output_file_path)
#plt.savefig(output_file_path, bbox_inches='tight')
## Save in SVG
#output_file_path = output_file_path_base + ".svg"
#print("Save", output_file_path)
#plt.savefig(output_file_path, bbox_inches='tight')
# Save in PNG
output_file_path = output_file_path_base + ".png"
print("Save", output_file_path)
plt.savefig(output_file_path, bbox_inches='tight')
else:
self.fig.canvas.draw()
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 plot_ellipse_shower_on_image_meter(axis, image_array, pixels_position):
xx, yy = pixels_position[0], pixels_position[1]
hillas = hillas_parameters_1(xx.flatten(), # * u.meter,
yy.flatten(), # * u.meter,
image_array.flatten())
centroid = (hillas.cen_x.value, hillas.cen_y.value)
length = hillas.length.value
width = hillas.width.value
angle = hillas.psi.to(u.rad).value
print("centroid:", centroid)
print("length:", length)
print("width:", width)
print("angle:", angle)
#a, b, c = common.angle_and_point_to_equation(angle, centroid)
#print("a:", a)
#print("b:", b)
#print("c:", c)
#x = np.array([-0.2, 0.2])
#f = lambda x: a/(-b) * x + c/(-b)
#axis.plot(x, f(x), "--b")
#print("DEBUG:", hillas[7].value, angle, np.degrees(angle))
ellipse = Ellipse(xy=centroid, width=length, height=width, angle=np.degrees(angle), fill=False, color='red', lw=2)
axis.axes.add_patch(ellipse)
title = axis.axes.get_title()
axis.axes.set_title("{} ({:.2f}°)".format(title, np.degrees(angle)))
# Plot centroid
axis.scatter(*centroid)
# Plot shower axis
#axis.arrow(0, 0, 0.1, 0, head_width=0.001, head_length=0.005, fc='k', ec='k')
#axis.arrow(0, 0, 0, 0.1, head_width=0.001, head_length=0.005, fc='k', ec='k')
p1_x = centroid[0]
p1_y = centroid[1]
p2_x = p1_x + math.cos(angle)
p2_y = p1_y + math.sin(angle)
#p2_x = p1_x + (length / 2.) * math.cos(angle)
#p2_y = p1_y + (length / 2.) * math.sin(angle)
#print(math.cos(math.pi/2.))
#print(math.sin(math.pi/2.))
#p2_x = p1_x + (length / 2.) * math.cos(math.pi/2.)
#p2_y = p1_y + (length / 2.) * math.sin(math.pi/2.)
#print(p1_x, p2_x)
#print(p1_y, p2_x)
axis.arrow(p1_x, p1_y, p2_x, p2_y, head_width=0.001, head_length=0.005, fc='r', ec='r')
p3_x = p1_x + math.cos(angle + math.pi/2.)
p3_y = p1_y + math.sin(angle + math.pi/2.)
#p3_x = p1_x + (width / 2.) * math.cos(angle + math.pi/2.)
#p3_y = p1_y + (width / 2.) * math.sin(angle + math.pi/2.)
axis.arrow(p1_x, p1_y, p3_x, p3_y, head_width=0.001, head_length=0.005, fc='g', ec='g')
def update_plots(self, data=None, save=False): # data is for event callers
if self.current_file_path is not None:
# Read the selected file #########
fits_images_dict, fits_metadata_dict = images.load_benchmark_images(
self.current_file_path)
input_img = fits_images_dict["input_image"]
reference_img = fits_images_dict["reference_image"]
pixels_position = fits_images_dict["pixels_position"]
if input_img.ndim != 2:
raise Exception(
"Unexpected error: the input FITS file should contain a 2D array."
)
if reference_img.ndim != 2:
raise Exception(
"Unexpected error: the input FITS file should contain a 2D array."
)
if self.kill_isolated_pixels_on_ref:
reference_img = scipy_kill_isolated_pixels(reference_img)
# Tailcut #####################
#input_img_copy = copy.deepcopy(input_img)
input_img_copy = input_img.astype('float64', copy=True)
tailcut = tailcut_mod.Tailcut()
initial_time = time.perf_counter()
tailcut_cleaned_img = tailcut.clean_image(
input_img_copy,
high_threshold=10,
low_threshold=5,
kill_isolated_pixels=self.kill_isolated_pixels)
tailcut_execution_time = time.perf_counter() - initial_time
# Wavelets ####################
#input_img_copy = copy.deepcopy(input_img)
input_img_copy = input_img.astype('float64', copy=True)
wavelets = wavelets_mod.WaveletTransform()
option_string = self.wavelets_options_entry.get_text()
print(option_string)
initial_time = time.perf_counter()
wavelets_cleaned_img = wavelets.clean_image(
input_img_copy,
kill_isolated_pixels=self.kill_isolated_pixels,
input_image_scale=self.input_image_scale,
offset_after_calibration=self.offset_after_calibration,
verbose=True,
raw_option_string=option_string)
wavelets_execution_time = time.perf_counter() - initial_time
# Execution time ##############
print("Tailcut execution time: ", tailcut_execution_time) # TODO
print("Wavelets execution time: ", wavelets_execution_time) # TODO
# Tailcut scores ##############
tailcut_title_suffix = ""
try:
tailcut_score_tuple, tailcut_score_name_tuple = assess_mod.assess_image_cleaning(
input_img,
tailcut_cleaned_img,
reference_img,
pixels_position,
benchmark_method="all")
if self.show_scores:
tailcut_title_suffix += " ("
for name, score in zip(tailcut_score_name_tuple,
tailcut_score_tuple):
if name == "e_shape":
tailcut_title_suffix += " Es="
tailcut_title_suffix += "{:.2e}".format(score)
elif name == "e_energy":
tailcut_title_suffix += " Ee="
tailcut_title_suffix += "{:.2e}".format(score)
elif name == "hillas_theta":
tailcut_title_suffix += " Th="
tailcut_title_suffix += "{:.2f}".format(score)
tailcut_title_suffix += " )"
print("Tailcut:")
for name in tailcut_score_name_tuple:
print("{:>20}".format(name), end=" ")
print()
for score in tailcut_score_tuple:
print("{:20.12f}".format(score), end=" ")
print()
except assess_mod.AssessError:
print("Tailcut: ", str(assess_mod.AssessError))
# Wavelets scores #############
wavelets_title_suffix = ""
try:
wavelets_score_tuple, wavelets_score_name_tuple = assess_mod.assess_image_cleaning(
input_img,
wavelets_cleaned_img,
reference_img,
pixels_position,
benchmark_method="all")
if self.show_scores:
wavelets_title_suffix += " ("
for name, score in zip(wavelets_score_name_tuple,
wavelets_score_tuple):
if name == "e_shape":
wavelets_title_suffix += " Es="
wavelets_title_suffix += "{:.2e}".format(score)
elif name == "e_energy":
wavelets_title_suffix += " Ee="
wavelets_title_suffix += "{:.2e}".format(score)
elif name == "hillas_theta":
wavelets_title_suffix += " Th="
wavelets_title_suffix += "{:.2f}".format(score)
wavelets_title_suffix += " )"
print("Wavelets:")
for name in wavelets_score_name_tuple:
print("{:>20}".format(name), end=" ")
print()
for score in wavelets_score_tuple:
print("{:20.12f}".format(score), end=" ")
print()
except assess_mod.AssessError:
print("Wavelets: ", str(assess_mod.AssessError))
# Update the widget ###########
self.clear_figure()
ax1 = self.fig.add_subplot(221)
ax2 = self.fig.add_subplot(222)
ax3 = self.fig.add_subplot(223)
ax4 = self.fig.add_subplot(224)
if self.plot_histogram:
self._draw_histogram(ax1, input_img, "Input")
self._draw_histogram(ax2, reference_img, "Reference")
self._draw_histogram(ax3, tailcut_cleaned_img,
"Tailcut" + tailcut_title_suffix)
self._draw_histogram(ax4, wavelets_cleaned_img,
"Wavelets" + wavelets_title_suffix)
else:
# AX1 #######
self._draw_image(ax1,
input_img,
"Input",
pixels_position=pixels_position)
# AX2 #######
self._draw_image(ax2,
reference_img,
"Reference",
pixels_position=pixels_position)
if self.plot_ellipse_shower:
try:
common.plot_ellipse_shower_on_image_meter(
ax2, reference_img, pixels_position)
except Exception as e:
print(e)
# AX3 #######
if self.plot_perpendicular_hit_distribution is not None:
if self.use_ref_angle_for_perpendicular_hit_distribution:
image_array = copy.deepcopy(reference_img)
xx, yy = pixels_position[0], pixels_position[1]
common_hillas_parameters = hillas_parameters_1(
xx.flatten() * u.meter,
yy.flatten() * u.meter, image_array.flatten())
else:
common_hillas_parameters = None
bins = np.linspace(-0.04, 0.04, 21)
if self.plot_perpendicular_hit_distribution == "Tailcut":
common.plot_perpendicular_hit_distribution(
ax3, [reference_img, tailcut_cleaned_img],
pixels_position,
bins=bins,
label_list=["Ref.", "Cleaned TC"],
hist_type="step",
common_hillas_parameters=common_hillas_parameters,
plot_ratio=True)
elif self.plot_perpendicular_hit_distribution == "Wavelet":
common.plot_perpendicular_hit_distribution(
ax3, [reference_img, wavelets_cleaned_img],
pixels_position,
bins=bins,
label_list=["Ref.", "Cleaned WT"],
hist_type="step",
common_hillas_parameters=common_hillas_parameters,
plot_ratio=True)
ax3.set_title("Perpendicular hit distribution")
ax3.set_xlabel("Distance to the shower axis (in meter)",
fontsize=16)
ax3.set_ylabel("Photoelectrons", fontsize=16)
else:
self._draw_image(ax3,
tailcut_cleaned_img,
"Tailcut" + tailcut_title_suffix,
pixels_position=pixels_position)
if self.plot_ellipse_shower:
if self.plot_perpendicular_hit_distribution is None:
try:
# Show ellipse only if "perpendicular hit distribution" is off
common.plot_ellipse_shower_on_image_meter(
ax3, tailcut_cleaned_img, pixels_position)
except Exception as e:
print(e)
# AX4 #######
if self.plot_perpendicular_hit_distribution == "Tailcut":
self._draw_image(ax4,
tailcut_cleaned_img,
"Tailcut" + tailcut_title_suffix,
pixels_position=pixels_position)
if self.plot_ellipse_shower:
if (
self.plot_perpendicular_hit_distribution
is not None
) and self.use_ref_angle_for_perpendicular_hit_distribution:
try:
common.plot_ellipse_shower_on_image_meter(
ax4, reference_img, pixels_position)
except Exception as e:
print(e)
else:
try:
common.plot_ellipse_shower_on_image_meter(
ax4, tailcut_cleaned_img, pixels_position)
except Exception as e:
print(e)
else:
self._draw_image(ax4,
wavelets_cleaned_img,
"Wavelets" + wavelets_title_suffix,
pixels_position=pixels_position)
if self.plot_ellipse_shower:
if (
self.plot_perpendicular_hit_distribution
is not None
) and self.use_ref_angle_for_perpendicular_hit_distribution:
try:
common.plot_ellipse_shower_on_image_meter(
ax4, reference_img, pixels_position)
except Exception as e:
print(e)
else:
try:
common.plot_ellipse_shower_on_image_meter(
ax4, wavelets_cleaned_img, pixels_position)
except Exception as e:
print(e)
plt.suptitle(
"{:.3f} TeV ({} photoelectrons in reference image) - Event {} - Telescope {}"
.format(fits_metadata_dict["mc_energy"],
int(fits_metadata_dict["npe"]),
fits_metadata_dict["event_id"],
fits_metadata_dict["tel_id"]),
fontsize=18)
if save:
output_file_path_base = "ev{}_tel{}".format(
fits_metadata_dict["event_id"],
fits_metadata_dict["tel_id"]) # TODO: add WT options
if self.plot_histogram:
output_file_path_base += "_hist"
if self.plot_log_scale:
output_file_path_base += "_log"
## Save in PDF
#output_file_path = output_file_path_base + ".pdf"
#print("Save", output_file_path)
#plt.savefig(output_file_path, bbox_inches='tight')
## Save in SVG
#output_file_path = output_file_path_base + ".svg"
#print("Save", output_file_path)
#plt.savefig(output_file_path, bbox_inches='tight')
# Save in PNG
output_file_path = output_file_path_base + ".png"
print("Save", output_file_path)
plt.savefig(output_file_path, bbox_inches='tight')
else:
self.fig.canvas.draw()
def plot_perpendicular_hit_distribution(histogram_axis, image_axis, image_array, pixels_position, title):
image_array = copy.deepcopy(image_array)
###
#size_m = 0.2 # Size of the "phase space" in meter
size_m = 0.2 # Size of the "phase space" in meter
# # TODO: clean these following hard coded values for Astri
# num_pixels_x = 40
# num_pixels_y = 40
#
# x = np.linspace(-0.142555996776, 0.142555996776, num_pixels_x)
# y = np.linspace(-0.142555996776, 0.142555996776, num_pixels_y)
#
# #x = np.arange(0, np.shape(ref_image_array)[0], 1) # TODO: wrong values -10 10 21
# #y = np.arange(0, np.shape(ref_image_array)[1], 1) # TODO: wrong values (30, ...)
#
# xx, yy = np.meshgrid(x, y)
# print("delta x:", xx - pixels_position[0])
# print("delta y:", yy - pixels_position[1])
xx, yy = pixels_position[0], pixels_position[1]
# Based on Tino's evaluate_cleaning.py (l. 277)
hillas = hillas_parameters_1(xx.flatten() * u.meter, # TODO: essayer avec hillas param 2 !!!
yy.flatten() * u.meter,
image_array.flatten()) # [0]
centroid = (hillas.cen_x, hillas.cen_y)
length = hillas.length
width = hillas.width
angle = np.radians(hillas.psi) # - np.pi/2. # TODO
print("centroid:", centroid)
print("length:", length)
print("width:", width)
print("angle:", angle)
###
# p1 = center of the ellipse
p1_x = hillas.cen_x
p1_y = hillas.cen_y
#image_axis.scatter(p1_x, p1_y) # DEBUG plot
# p2 = intersection between the ellipse and the shower track
p2_x = p1_x + hillas.length * np.cos(angle) # hillas.psi + np.pi/2)
p2_y = p1_y + hillas.length * np.sin(angle) # hillas.psi + np.pi/2)
#image_axis.scatter(p2_x, p2_y) # DEBUG plot
#image_axis.plot([p1_x, p2_x], [p1_y, p2_y]) # DEBUG plot
print(p1_x, p2_x, p1_y, p2_y) # DEBUG plot
d12_x = p1_x - p2_x
d12_y = p1_y - p2_y
print(d12_x, d12_y) # DEBUG plot
# Slope of the shower track
T = linalg.normalise(np.array([d12_x.value, d12_y.value])) # why a dedicated function ? if it's what I understand, it can easily be done on the fly
#print("[p1_x-p2_x, p1_y-p2_y]:", [p1_x-p2_x, p1_y-p2_y])
#print("T:", T)
#image_axis.plot([p1_x, p1_x*T[0]], [p1_y, p1_y*T[1]], "-g", linewidth=3) # DEBUG plot
x = xx.flatten()
y = yy.flatten()
# Manhattan distance of pixels to the center of the ellipse
# Translate (center) on P1 ?
D = [p1_x.value-x, p1_y.value-y]
# Pixels in the new base
dl = D[0]*T[0] + D[1]*T[1]
dp = D[0]*T[1] - D[1]*T[0]
# nparray.ravel(): Return a flattened array.
values, bins, patches = histogram_axis.hist(dp.ravel(),
histtype='step',
bins=np.linspace(-size_m, size_m, 31)) # -10 10 21
# TODO: scatter seulement les points qui sont dans le linspace de bins defini juste au dessus, faire apparaitre chaque bin d'une couleure différente
##image_axis.scatter(dl, dp, s=10, alpha=0.5) # DEBUG plot
#image_axis.scatter(dl, dp, s=10, alpha=0.5) # DEBUG plot
#image_axis.plot(dl.reshape(40, 40).T, dp.reshape(40, 40).T) # DEBUG plot
#print(dl)
#print(dp.shape)
###
histogram_axis.set_xlim([-size_m, size_m])
histogram_axis.set_xlabel('Distance to the shower axis (m)', fontsize=14)
histogram_axis.set_ylabel('Hits', fontsize=14)
histogram_axis.set_title(title)
def plot_perpendicular_hit_distribution(histogram_axis, image_axis,
image_array, pixels_position, title):
image_array = copy.deepcopy(image_array)
###
#size_m = 0.2 # Size of the "phase space" in meter
size_m = 0.2 # Size of the "phase space" in meter
# # TODO: clean these following hard coded values for Astri
# num_pixels_x = 40
# num_pixels_y = 40
#
# x = np.linspace(-0.142555996776, 0.142555996776, num_pixels_x)
# y = np.linspace(-0.142555996776, 0.142555996776, num_pixels_y)
#
# #x = np.arange(0, np.shape(ref_image_array)[0], 1) # TODO: wrong values -10 10 21
# #y = np.arange(0, np.shape(ref_image_array)[1], 1) # TODO: wrong values (30, ...)
#
# xx, yy = np.meshgrid(x, y)
# print("delta x:", xx - pixels_position[0])
# print("delta y:", yy - pixels_position[1])
xx, yy = pixels_position[0], pixels_position[1]
# Based on Tino's evaluate_cleaning.py (l. 277)
hillas = hillas_parameters_1(
xx.flatten() * u.meter, # TODO: essayer avec hillas param 2 !!!
yy.flatten() * u.meter,
image_array.flatten()) # [0]
centroid = (hillas.x, hillas.y)
length = hillas.length
width = hillas.width
angle = np.radians(hillas.psi) # - np.pi/2. # TODO
print("centroid:", centroid)
print("length:", length)
print("width:", width)
print("angle:", angle)
###
# p1 = center of the ellipse
p1_x = hillas.x
p1_y = hillas.y
#image_axis.scatter(p1_x, p1_y) # DEBUG plot
# p2 = intersection between the ellipse and the shower track
p2_x = p1_x + hillas.length * np.cos(angle) # hillas.psi + np.pi/2)
p2_y = p1_y + hillas.length * np.sin(angle) # hillas.psi + np.pi/2)
#image_axis.scatter(p2_x, p2_y) # DEBUG plot
#image_axis.plot([p1_x, p2_x], [p1_y, p2_y]) # DEBUG plot
print(p1_x, p2_x, p1_y, p2_y) # DEBUG plot
d12_x = p1_x - p2_x
d12_y = p1_y - p2_y
print(d12_x, d12_y) # DEBUG plot
# Slope of the shower track
T = linalg.normalise(
np.array([d12_x.value, d12_y.value])
) # why a dedicated function ? if it's what I understand, it can easily be done on the fly
#print("[p1_x-p2_x, p1_y-p2_y]:", [p1_x-p2_x, p1_y-p2_y])
#print("T:", T)
#image_axis.plot([p1_x, p1_x*T[0]], [p1_y, p1_y*T[1]], "-g", linewidth=3) # DEBUG plot
x = xx.flatten()
y = yy.flatten()
# Manhattan distance of pixels to the center of the ellipse
# Translate (center) on P1 ?
D = [p1_x.value - x, p1_y.value - y]
# Pixels in the new base
dl = D[0] * T[0] + D[1] * T[1]
dp = D[0] * T[1] - D[1] * T[0]
# nparray.ravel(): Return a flattened array.
values, bins, patches = histogram_axis.hist(dp.ravel(),
histtype='step',
bins=np.linspace(
-size_m, size_m,
31)) # -10 10 21
# TODO: scatter seulement les points qui sont dans le linspace de bins defini juste au dessus, faire apparaitre chaque bin d'une couleure différente
##image_axis.scatter(dl, dp, s=10, alpha=0.5) # DEBUG plot
#image_axis.scatter(dl, dp, s=10, alpha=0.5) # DEBUG plot
#image_axis.plot(dl.reshape(40, 40).T, dp.reshape(40, 40).T) # DEBUG plot
#print(dl)
#print(dp.shape)
###
histogram_axis.set_xlim([-size_m, size_m])
histogram_axis.set_xlabel('Distance to the shower axis (m)', fontsize=14)
histogram_axis.set_ylabel('Hits', fontsize=14)
histogram_axis.set_title(title)
