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 main():
"""
Take datas (2D numpy.array) from the fits file specific.fits
and find cluster of pixels in which each pixel has a luminosity
greater than a threshold (linked to the value of the background)
"""
# 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)
# Get the list of clusters
cluster_array = mylib.get_cluster_array(pixels, background, dispersion)
# Find the cluster with the greatest integral
main_clust = mylib.find_main_centroid(cluster_array)
# Write informations about clusters on ex3.txt file
results = 'number of clusters: %2d, ' % (len(cluster_array)) \
+ 'greatest integral: %7d, ' % (main_clust.integrated_luminosity) \
+ 'centroid x: %4.1f, centroid y: %4.1f' % \
(main_clust.centroid_pixel[0], main_clust.centroid_pixel[1])
with open("ex3.txt", 'w') as output_file:
output_file.write(results)
return 0
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():
"""
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():
"""
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():
"""
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():
"""
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