def radio_action(self, file_name):
"""
On click function for radio button
:param file_name: name of the fits file
"""
# Get pixels and header from a fits file
self.pixels, _ = mylib.get_pixels("../data/%s" % (file_name))
# Process to the fit of the background
_, _, _, self.background, self.dispersion = mylib.modelling_parameters(self.pixels)
# Remove the background
filtered_pixels = mylib.remove_background(self.pixels, self.background, self.dispersion)
# Update the picture
self.axis.cla() # delete the previous imshow
self.axis.imshow(filtered_pixels)
self.axis.set_title('Use of both slider and radio button')
self.fig.canvas.draw() # redraw
# Define the slider widget
threshold_min = max(0, self.background - 6.0 * self.dispersion)
threshold_max = self.background + 10.0*self.dispersion
threshold_init = self.background + 6.0*self.dispersion
if self.my_widget is not None:
self.slider_axis.cla()
self.my_widget = widgets.Slider(self.slider_axis, 'threshold', threshold_min, \
threshold_max, valinit=threshold_init)
def radio_action(self, file_name):
"""
On click function for radio button
:param file_name: name of the fits file
"""
# Get pixels and header from a fits file
self.pixels, _ = mylib.get_pixels("../data/%s" % (file_name))
# Process to the fit of the background
_, _, _, self.background, self.dispersion = mylib.modelling_parameters(
self.pixels)
# Remove the background
filtered_pixels = mylib.remove_background(self.pixels, self.background,
self.dispersion)
# Update the picture
self.axis.cla() # delete the previous imshow
self.axis.imshow(filtered_pixels)
self.axis.set_title('Use of both slider and radio button')
self.fig.canvas.draw() # redraw
# Define the slider widget
threshold_min = max(0, self.background - 6.0 * self.dispersion)
threshold_max = self.background + 10.0 * self.dispersion
threshold_init = self.background + 6.0 * self.dispersion
if self.my_widget is not None:
self.slider_axis.cla()
self.my_widget = widgets.Slider(self.slider_axis, 'threshold', threshold_min, \
threshold_max, valinit=threshold_init)
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# Find the clusters with the selected threshold
cluster_list = mylib.find_clusters(header, pixels,
background + thresh_slider.val)
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
if not cluster_list: # If max recursion depth reached, cluster_list is empty
nb_clusters = "Error: max recursion reached"
else:
nb_clusters = "Number of clusters found : %i" % len(cluster_list)
pads.set_title("Pixels above background + %d sigma. %s" \
% (thresh_slider.val / dispersion, nb_clusters))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(
mylib.remove_background(pixels, background, thresh_slider.val))
# Draw the image
fig.canvas.draw()
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# Find the clusters with the selected threshold
cluster_list = mylib.find_clusters(header, pixels, background + thresh_slider.val)
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
if not cluster_list: # If max recursion depth reached, cluster_list is empty
nb_clusters = "Error: max recursion reached"
else:
nb_clusters = "Number of clusters found : %i" % len(cluster_list)
pads.set_title("Pixels above background + %d sigma. %s" \
% (thresh_slider.val / dispersion, nb_clusters))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(mylib.remove_background(pixels,
background,
thresh_slider.val))
# Draw the image
fig.canvas.draw()
def event_handler(header, pixels, background, dispersion):
"""
Event handler
:param header: header of the FITS file opened
:param pixels: The image to display
:param background: mean value of the image background
:param dispersion: dispersion of the background, fitted on the image
:return:
"""
# plot zone
fig, pads = plt.subplots()
# Add margins to gain space for the slider
plt.subplots_adjust(left=0.25, bottom=0.25)
# Plot the image a first time with no threshold (0)
pads.imshow(mylib.remove_background(pixels, background, 0))
# pad containing the slider : left, bot, width, height
pad_slider = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg='white')
# Default value for slider: 0
# Minimal value: 0
# Maximal value: max value in 'pixels' - background
thresh_slider = widg.Slider(pad_slider, "Threshold", 0,
np.max(pixels) - background, valinit=0)
# Initialize the displayed text
pads.set_title("Pixels above background + %d sigma" \
% (thresh_slider.val / dispersion))
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# Find the clusters with the selected threshold
cluster_list = mylib.find_clusters(header, pixels, background + thresh_slider.val)
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
if not cluster_list: # If max recursion depth reached, cluster_list is empty
nb_clusters = "Error: max recursion reached"
else:
nb_clusters = "Number of clusters found : %i" % len(cluster_list)
pads.set_title("Pixels above background + %d sigma. %s" \
% (thresh_slider.val / dispersion, nb_clusters))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(mylib.remove_background(pixels,
background,
thresh_slider.val))
# Draw the image
fig.canvas.draw()
thresh_slider.on_changed(update)
def main():
"""
Take datas (2D numpy.array) from the fits file specific.fits
and display the associated picture without background,
the threshold can be ajusted with a widget,
calculate the number of clusters
"""
# Get pixels and header from a fits file
pixels, _ = mylib.get_pixels("../data/specific.fits")
# Process to the fit of the background
_, _, _, background, dispersion = mylib.modelling_parameters(pixels)
# Remove the background
filtered_pixels = mylib.remove_background(pixels, background, dispersion)
# Display the picture without the background
fig, axis = plt.subplots()
plt.subplots_adjust(left=0.1, bottom=0.25)
axis.imshow(filtered_pixels)
axis.set_title('Picture without background')
# Define the slider axis and the widget associated
slider_axis = plt.axes([0.2, 0.1, 0.6, 0.105])
threshold_min = background
threshold_max = background + 30.0 * dispersion
threshold_init = background + 6.0 * dispersion
my_widget = widgets.Slider(slider_axis, 'threshold', threshold_min, \
threshold_max, valinit=threshold_init)
# slider_action function definition
def slider_action(threshold):
"""
Function called when the threshold value is changed,
the filtered_pixels array is recalculated
and the canvas is redrawn
"""
axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(pixels, background, \
dispersion, threshold=threshold)
axis.imshow(filtered_pixels)
try: # if too many recursion, say that threshold is too low
cluster_array = mylib.get_cluster_array(pixels, background, dispersion, threshold)
axis.set_title('Picture without background : number of clusters = %s' % \
(len(cluster_array)))
except RuntimeError:
axis.set_title('Picture without background : threshold is too low')
fig.canvas.draw() # redraw
# Use event handler
my_widget.on_changed(slider_action)
plt.show()
return 0
def main():
"""
We read a FITS file, find the clusters,
and convert their centroid coordinates to WCS coordinates.
Then we display the WCS coordinates on the image,
following the mouse movement.
"""
input_file_path = "/Users/npac09/PycharmProjects/npac09/data/specific.fits"
output_file_path = "/Users/npac09/PycharmProjects/npac09/src/ex4.txt"
# open file and retrieve data and header
header, pixels = mylib.open_fits(input_file_path)
# creation of the histogram from the data
bin_number = 200
bin_values, bin_boundaries = np.histogram(pixels.ravel(), bin_number)
# apply the fit (no need for the amplitude (first parameter))
_, background, dispersion = mylib.gaussian_fit(bin_boundaries[:-1],
bin_values)
# We define the threshold at 6 standard deviations above the mean bkg value
threshold = background + (6.0 * dispersion)
# plot
fig, pads = plt.subplots()
# visualization of the image after bkg removal
pads.imshow(mylib.remove_background(pixels, background, threshold))
# find the clusters.
cluster_list = mylib.find_clusters(header, pixels, threshold)
cluster_dico = mylib.build_cluster_dico(cluster_list)
# find the maximum-integral cluster
max_integral_key = mylib.find_max_integral_cluster(cluster_list)
# call the event handler
event_handler(fig, header, pixels)
# display
plt.show()
# write result to output file
try:
with open(output_file_path, 'w') as output_file:
output_file.write('right ascension: %.3f, declination: %.3f' \
% (cluster_dico[max_integral_key][0].centroid_wcs))
except IOError:
print "File not found :", output_file_path
return 2
return 0
def slider_action(self, threshold):
"""
On changed function for slider
:param threshold: value of the threshold
"""
self.axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(self.pixels, self.background, \
self.dispersion, threshold=threshold)
self.axis.imshow(filtered_pixels)
self.axis.set_title('Use of both slider and radio button')
self.fig.canvas.draw() # redraw
def main():
"""
Take datas (2D numpy.array) from the fits file specific.fits,
find the clusters array, find there WCS coordinates, display it
"""
# Get pixels and header from a fits file
pixels, header = mylib.get_pixels("../data/specific.fits")
# Process to the fit of the background
_, _, _, background, dispersion = mylib.modelling_parameters(pixels)
# Get the list of clusters
my_wcs = library.WCS(header) # Instantiate WCS conversion object
cluster_array = mylib.get_cluster_array(pixels, background, \
dispersion, my_wcs=my_wcs)
# Remove the background
filtered_pixels = mylib.remove_background(pixels, background, dispersion)
# Display the picture
fig, axis = plt.subplots()
axis.imshow(filtered_pixels)
# Move function definition
def move(event):
"""
Function called when the event 'motion_notify_event' occurs,
the wcs coordinates of the pixel pointed by the mouse are
then displayed at its location
"""
x_position = event.xdata
y_position = event.ydata
if x_position != None: # if we are not outside the picture
rad, dec = my_wcs.convert_to_radec(x_position, y_position)
text = axis.text(x_position, y_position, 'ra: %.6f, dec: %.6f' % \
(rad, dec), fontsize=14, color='white')
event.canvas.draw()
text.remove()
# Use event handler
fig.canvas.mpl_connect('motion_notify_event', move)
plt.show()
# Find the cluster with the greatest integral
main_clust = mylib.find_main_centroid(cluster_array)
# Write informations about WCS coordinates on ex4.txt file
results = 'right ascension: %.3f, declination: %.3f' % \
(main_clust.centroid_wcs[0], main_clust.centroid_wcs[1])
with open("ex4.txt", 'w') as output_file:
output_file.write(results)
return 0
def slider_action(self, threshold):
"""
On changed function for slider
:param threshold: value of the threshold
"""
self.axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(self.pixels, self.background, \
self.dispersion, threshold=threshold)
self.axis.imshow(filtered_pixels)
self.axis.set_title('Use of both slider and radio button')
self.fig.canvas.draw() # redraw
def main():
"""
Take datas (2D numpy.array) from the fits file specific.fits,
find the clusters array, find there WCS coordinates, display it
"""
# Get pixels and header from a fits file
pixels, header = mylib.get_pixels("../data/specific.fits")
# Process to the fit of the background
_, _, _, background, dispersion = mylib.modelling_parameters(pixels)
# Get the list of clusters
my_wcs = library.WCS(header) # Instantiate WCS conversion object
cluster_array = mylib.get_cluster_array(pixels, background, \
dispersion, my_wcs=my_wcs)
# Remove the background
filtered_pixels = mylib.remove_background(pixels, background, dispersion)
# Display the picture
fig, axis = plt.subplots()
axis.imshow(filtered_pixels)
# Move function definition
def move(event):
"""
Function called when the event 'motion_notify_event' occurs,
the wcs coordinates of the pixel pointed by the mouse are
then displayed at its location
"""
x_position = event.xdata
y_position = event.ydata
if x_position != None: # if we are not outside the picture
rad, dec = my_wcs.convert_to_radec(x_position, y_position)
text = axis.text(x_position, y_position, 'ra: %.6f, dec: %.6f' % \
(rad, dec), fontsize=14, color='white')
event.canvas.draw()
text.remove()
# Use event handler
fig.canvas.mpl_connect('motion_notify_event', move)
plt.show()
# Find the cluster with the greatest integral
main_clust = mylib.find_main_centroid(cluster_array)
# Write informations about WCS coordinates on ex4.txt file
results = 'right ascension: %.3f, declination: %.3f' % \
(main_clust.centroid_wcs[0], main_clust.centroid_wcs[1])
with open("ex4.txt", 'w') as output_file:
output_file.write(results)
return 0
def main():
"""
We read a FITS file, find the clusters,
and convert their centroid coordinates to WCS coordinates.
Then we display the WCS coordinates on the image,
following the mouse movement.
"""
input_file_path = "/Users/npac09/PycharmProjects/npac09/data/specific.fits"
output_file_path = "/Users/npac09/PycharmProjects/npac09/src/ex4.txt"
# open file and retrieve data and header
header, pixels = mylib.open_fits(input_file_path)
# creation of the histogram from the data
bin_number = 200
bin_values, bin_boundaries = np.histogram(pixels.ravel(), bin_number)
# apply the fit (no need for the amplitude (first parameter))
_, background, dispersion = mylib.gaussian_fit(bin_boundaries[:-1], bin_values)
# We define the threshold at 6 standard deviations above the mean bkg value
threshold = background + (6.0 * dispersion)
# plot
fig, pads = plt.subplots()
# visualization of the image after bkg removal
pads.imshow(mylib.remove_background(pixels, background, threshold))
# find the clusters.
cluster_list = mylib.find_clusters(header, pixels, threshold)
cluster_dico = mylib.build_cluster_dico(cluster_list)
# find the maximum-integral cluster
max_integral_key = mylib.find_max_integral_cluster(cluster_list)
# call the event handler
event_handler(fig, header, pixels)
# display
plt.show()
# write result to output file
try:
with open(output_file_path, 'w') as output_file:
output_file.write('right ascension: %.3f, declination: %.3f' \
% (cluster_dico[max_integral_key][0].centroid_wcs))
except IOError:
print "File not found :", output_file_path
return 2
return 0
def event_handler(pixels, background, dispersion):
"""
Event handler
:param pixels: The image to display
:param background: mean value of the image background
:param dispersion: dispersion of the background, fitted on the image
:return:
"""
# plot zone
fig, pads = plt.subplots()
# Add margins to gain space for the slider
plt.subplots_adjust(left=0.25, bottom=0.25)
# Plot the image a first time with no threshold (0)
pads.imshow(mylib.remove_background(pixels, background, 0))
# pad containing the slider : left, bot, width, height
pad_slider = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg='white')
# Default value for slider: 0
# Minimal value: 0
# Maximal value: max value in 'pixels' - background
thresh_slider = widg.Slider(pad_slider,
"Threshold",
0,
np.max(pixels) - background,
valinit=0)
# Initialize the displayed text
pads.set_title("pixels above background + %d sigma" \
% (thresh_slider.val / dispersion))
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
pads.set_title("pixels above background + %d sigma" \
% (thresh_slider.val / dispersion))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(
mylib.remove_background(pixels, background, thresh_slider.val))
# Draw the image
fig.canvas.draw()
thresh_slider.on_changed(update)
def slider_action(threshold):
"""
Function called when the threshold value is changed,
the filtered_pixels array is recalculated
and the canvas is redrawn
"""
axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(pixels, background, \
dispersion, threshold=threshold)
axis.imshow(filtered_pixels)
axis.set_title('Picture without background : threshold = %s' % \
(threshold))
fig.canvas.draw() # redraw
def slider_action(threshold):
"""
Function called when the threshold value is changed,
the filtered_pixels array is recalculated
and the canvas is redrawn
"""
axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(pixels, background, \
dispersion, threshold=threshold)
axis.imshow(filtered_pixels)
axis.set_title('Picture without background : threshold = %s' % \
(threshold))
fig.canvas.draw() # redraw
def main():
"""
Reading a FITS file and determining the background parameters.
"""
input_file_path = "/Users/npac09/PycharmProjects/npac09/data/specific.fits"
output_file_path = "/Users/npac09/PycharmProjects/npac09/src/ex2.txt"
# open file and retrieve data
_, pixels = mylib.open_fits(input_file_path)
# creation of the histogram from the data
bin_number = 200
bin_values, bin_boundaries = np.histogram(pixels.ravel(), bin_number)
bin_lower_boundaries = bin_boundaries[:-1]
# apply the fit
maxvalue, background, dispersion = mylib.gaussian_fit(
bin_lower_boundaries, bin_values)
threshold = 6.0 * dispersion
# visualization of the histogram and the fit
_, pads = plt.subplots(1, 3)
# 0: image before bkg removal; # 1: image after bkg removal; 2: histogram and fit
pads[2].plot(bin_lower_boundaries, bin_values, 'b+:', label='data')
pads[2].plot(bin_lower_boundaries, \
mylib.gaussian(bin_lower_boundaries, maxvalue, background, dispersion), \
'r.:', label='fit')
pads[2].legend()
pads[2].set_title('Flux distribution')
pads[2].set_xlabel('Amplitude')
pads[2].set_ylabel('Frequency')
# visualization of the image before and after bkg removal
pads[0].imshow(pixels)
pads[1].imshow(mylib.remove_background(pixels, background, threshold))
plt.show()
# write result to output file
try:
with open(output_file_path, 'w') as output_file:
output_file.write('background: %d, dispersion: %d' %
(int(background), int(dispersion)))
except IOError:
print "File not found :", output_file_path
return 2
return 0
def main():
"""
Reading a FITS file and determining the background parameters.
"""
input_file_path = "/Users/npac09/PycharmProjects/npac09/data/specific.fits"
output_file_path = "/Users/npac09/PycharmProjects/npac09/src/ex2.txt"
# open file and retrieve data
_, pixels = mylib.open_fits(input_file_path)
# creation of the histogram from the data
bin_number = 200
bin_values, bin_boundaries = np.histogram(pixels.ravel(), bin_number)
bin_lower_boundaries = bin_boundaries[:-1]
# apply the fit
maxvalue, background, dispersion = mylib.gaussian_fit(bin_lower_boundaries, bin_values)
threshold = 6.0 * dispersion
# visualization of the histogram and the fit
_, pads = plt.subplots(1, 3)
# 0: image before bkg removal; # 1: image after bkg removal; 2: histogram and fit
pads[2].plot(bin_lower_boundaries, bin_values, 'b+:', label='data')
pads[2].plot(bin_lower_boundaries, \
mylib.gaussian(bin_lower_boundaries, maxvalue, background, dispersion), \
'r.:', label='fit')
pads[2].legend()
pads[2].set_title('Flux distribution')
pads[2].set_xlabel('Amplitude')
pads[2].set_ylabel('Frequency')
# visualization of the image before and after bkg removal
pads[0].imshow(pixels)
pads[1].imshow(mylib.remove_background(pixels, background, threshold))
plt.show()
# write result to output file
try:
with open(output_file_path, 'w') as output_file:
output_file.write('background: %d, dispersion: %d' % (int(background), int(dispersion)))
except IOError:
print "File not found :", output_file_path
return 2
return 0
def slider_action(threshold):
"""
Function called when the threshold value is changed,
the filtered_pixels array is recalculated
and the canvas is redrawn
"""
axis.cla() # delete the previous imshow
filtered_pixels = mylib.remove_background(pixels, background, \
dispersion, threshold=threshold)
axis.imshow(filtered_pixels)
try: # if too many recursion, say that threshold is too low
cluster_array = mylib.get_cluster_array(pixels, background, dispersion, threshold)
axis.set_title('Picture without background : number of clusters = %s' % \
(len(cluster_array)))
except RuntimeError:
axis.set_title('Picture without background : threshold is too low')
fig.canvas.draw() # redraw
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
pads.set_title("pixels above background + %d sigma" \
% (thresh_slider.val / dispersion))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(
mylib.remove_background(pixels, background, thresh_slider.val))
# Draw the image
fig.canvas.draw()
def update(event):
"""
Action on slider change
:param event: the event
:return:
"""
# clear the previous image
pads.cla()
# Display the number of sigmas above threshold selected with the slider
pads.set_title("pixels above background + %d sigma" \
% (thresh_slider.val / dispersion))
# Display the image with selected background removed
# (we remove 'thresh_slider.val', with a threshold = 'thresh_slider.val - background'
pads.imshow(mylib.remove_background(pixels,
background,
thresh_slider.val))
# Draw the image
fig.canvas.draw()
def main():
"""
Take datas (2D numpy.array) from the fits file specific.fits
and display the associated picture without background
"""
# Get pixels and header from a fits file
pixels, _ = mylib.get_pixels("../data/specific.fits")
# Process to the fit of the background
x_array, y_array, maxvalue, background, dispersion = \
mylib.modelling_parameters(pixels)
# Remove the background
filtered_pixels = mylib.remove_background(pixels, background, dispersion)
# Plot both histograms and picture without background
_, axis = plt.subplots(1, 2)
# Plot histogram of data luminosity and result of the fit
axis[0].plot(x_array, y_array, 'b+:', label='data')
axis[0].plot(x_array, mylib.modelling_function( \
x_array, maxvalue, background, dispersion), \
'r.:', label='fit')
axis[0].legend()
axis[0].set_title('Flux distribution')
axis[0].set_xlabel('Amplitude')
axis[0].set_ylabel('Frequency')
# Display the picture without the background
axis[1].imshow(filtered_pixels)
axis[1].set_title('Picture without background')
plt.show()
# Write informations about background and dispersion on ex2.txt file
results = 'background: %i, dispersion: %i' % (background, dispersion)
with open("ex2.txt", 'w') as output_file:
output_file.write(results)
return 0
def main():
"""
We read a FITS file, find the clusters,
and convert their centroid coordinates to WCS coordinates.
Then we display the celestial objects names on the image,
at click on the corresponding object on the image.
"""
input_file_path = "/Users/npac09/PycharmProjects/npac09/data/specific.fits"
output_file_path = "/Users/npac09/PycharmProjects/npac09/src/ex5.txt"
# open file and retrieve data and header
header, pixels = mylib.open_fits(input_file_path)
# creation of the histogram from the data
bin_number = 200
bin_values, bin_boundaries = np.histogram(pixels.ravel(), bin_number)
# apply the fit (no need for the amplitude (first parameter))
_, background, dispersion = mylib.gaussian_fit(bin_boundaries[:-1],
bin_values)
# We define the threshold at 6 standard deviations above the mean bkg value
threshold = background + (6.0 * dispersion)
# define an accetance radius around a given position to get the name from Simbad
radius = 0.003
cluster_list = mylib.find_clusters(header, pixels, threshold)
cluster_dico = mylib.build_cluster_dico(cluster_list, radius)
# plot
fig, pads = plt.subplots()
# Display the image without background
pads.imshow(mylib.remove_background(pixels, background, threshold))
# Display the boxes around clusters
for cluster in cluster_list:
pads.add_patch(
patches.Rectangle((cluster.box_xmin, cluster.box_ymin),
cluster.box_xmax - cluster.box_xmin,
cluster.box_ymax - cluster.box_ymin,
fill=False,
color='white'))
# find the maximum-integral cluster
max_integral_key = mylib.find_max_integral_cluster(cluster_list)
# call the event handler
event_handler(fig, header, pixels, cluster_list, cluster_dico)
plt.show()
# write result to output file
try:
with open(output_file_path, 'w') as output_file:
output_file.write('celestial object: %s' %
cluster_dico[max_integral_key][1])
except IOError:
print "File not found :", output_file_path
return 2
return 0
def main():
"""
Take datas (2D numpy.array) from the fits file specific.fits,
find the clusters array, find there names, display it
"""
# Get pixels and header from a fits file
pixels, header = mylib.get_pixels("../data/specific.fits")
# Process to the fit of the background
_, _, _, background, dispersion = mylib.modelling_parameters(pixels)
# Get the list of clusters
my_wcs = library.WCS(header) # Instantiate WCS conversion object
cluster_array = mylib.get_cluster_array(pixels, background, \
dispersion, my_wcs=my_wcs)
# Remove the background
filtered_pixels = mylib.remove_background(pixels, background, dispersion)
# Display the picture
fig, axis = plt.subplots()
axis.imshow(filtered_pixels)
# Put bounding boxes
for cluster in cluster_array:
axis.add_patch(patches.Rectangle(cluster.bounding_box[0], \
cluster.bounding_box[1], \
cluster.bounding_box[2], \
fill=False, \
color='white'))
# On_click function definition
def on_click(event):
"""
Function called when the event 'button_release_event' occurs,
if the click was made on a bounding box, the name of the
corresponding celestial object is displayed
"""
x_position = event.xdata
y_position = event.ydata
if x_position != None: # if we are not outside the picture
for clust in cluster_array:
if clust.is_in_bounding(x_position, y_position):
text = axis.text(x_position, y_position, clust.star_name, \
fontsize=14, color='white')
event.canvas.draw()
text.remove()
# Use event handler
fig.canvas.mpl_connect('button_release_event', on_click)
plt.show()
# Find the cluster with the greatest integral
main_clust = mylib.find_main_centroid(cluster_array)
# Write the name of the main celestial object on ex5.txt file
results = 'celestial object: %s' % (main_clust.star_name)
with open("ex5.txt", 'w') as output_file:
output_file.write(results)
return 0