def peak_finder(self, map_data, mask_pf=None):
'''
Function to generate initial parameters of gaussian(s) to be used as guesses in the fitting algorithm.
The code is searching automatically for peaks location using astropy and photutils routines.
Parameters:
- map_data: map to be searched for gaussian(s)
- mask_pf: a mask to be used in the searching algorithm. The alorithm is looking for gaussians only where
the mask is true. In case of an iterative fitting, the mask is updated everytime to mask out
the gaussians that have been already found.
'''
x_lim = np.size(self.xgrid)
y_lim = np.size(self.ygrid)
fact = 20.
bs = np.array(
[int(np.floor(y_lim / fact)),
int(np.floor(x_lim / fact))])
mean, median, std = sigma_clipped_stats(self.data, sigma=3.0)
threshold = median + (5. * std)
if mask_pf is None:
tbl = find_peaks(map_data, threshold, box_size=bs)
mask_pf = np.zeros_like(self.xy_mesh[0])
else:
self.mask = mask_pf.copy()
tbl = find_peaks(map_data, threshold, box_size=bs, mask=self.mask)
tbl['peak_value'].info.format = '%.8g'
guess = np.array([])
x_i = np.array([])
y_i = np.array([])
for i in range(len(tbl['peak_value'])):
guess_temp = np.array([tbl['peak_value'][i], self.xgrid[tbl['x_peak'][i]], \
self.ygrid[tbl['y_peak'][i]], 1., 1., 0.])
guess = np.append(guess, guess_temp)
index_x = self.xgrid[tbl['x_peak'][i]]
index_y = self.ygrid[tbl['y_peak'][i]]
x_i = np.append(x_i, index_x)
y_i = np.append(y_i, index_y)
mask_pf[index_y - bs[1]:index_y + bs[1],
index_x - bs[0]:index_x + bs[0]] = True
if self.param is None:
self.param = guess_temp
self.mask = mask_pf.copy()
else:
self.param = np.append(self.param, guess_temp)
self.mask = np.logical_or(self.mask, mask_pf)
def center(s_post, member_indices):
mem = gcat[member_indices]
""" Member Galaxy Locations """
mempix = convpix(mem, w)
x = mempix[:, 0]
y = mempix[:, 1]
""" Configuring Pixel Space"""
init_x, init_y = w.wcs_world2pix(s_post[0], s_post[1],
1) # initial estimate to draw box
wid_arcsec = 302 # 5'x 5' area around s-post
pix_scale = 0.8627 # "/pixel ---> 0.000239 deg/pixel
wid_pixels = int(wid_arcsec / pix_scale) # ~350x350 pixels
xmin = int(init_x - (wid_pixels / 2.0)) # boundaries
xmax = int(init_x + (wid_pixels / 2.0))
ymin = int(init_y - (wid_pixels / 2.0))
ymax = int(init_y + (wid_pixels / 2.0))
""" 2D-Histogram & Smoothing """
binstep = 10 # 10 pixel ~ 8.62 "
wght = np.asarray(
gcat[member_indices]['fch1']) # weights: irac ch1[3.6 um] flux
xedges = np.arange(xmin, xmax + binstep, binstep)
yedges = np.arange(ymin, ymax + binstep, binstep)
h, xedges, yedges = np.histogram2d(x,
y,
bins=(xedges, yedges),
weights=wght)
size = int(round(
(np.shape(h)[0]) / 4.0)) # kernel size : at least ~ 4 times smaller
stdev_ker = size / 3.0 # size = 3-sigma
gauss_kernel = Gaussian2DKernel(stddev=stdev_ker, x_size=size,
y_size=size) # kernel
smoothed_data_gauss = convolve_fft(h.T,
gauss_kernel) # convolution/smoothing
""" Finds Peak """
x_org, y_org = (xedges[0] + xedges[1]) / 2.0, (
yedges[0] + yedges[1]) / 2.0 # origin of bin-coordinate
fp_m = find_peaks(smoothed_data_gauss, 0,
npeaks=1) # 1 peak with max value
xbin_peak, ybin_peak = fp_m['x_peak'][0], fp_m['y_peak'][
0] # x, y positions of the peak
x_p, y_p = (x_org + xbin_peak * binstep), (
y_org + ybin_peak * binstep) # conversion to pixel coordinates
peak = (x_p, y_p)
ra_p, dec_p = w.wcs_pix2world(peak[0], peak[1], 1)
peak_fk5 = (ra_p, dec_p)
""" Centroid from Image Moments """
cen_pos = centroid_com(smoothed_data_gauss)
cenx, ceny = (x_org + cen_pos[0] * binstep), (y_org + cen_pos[1] * binstep)
ra_c, dec_c = w.wcs_pix2world(cenx, ceny, 1)
centre = (ra_c, dec_c)
return centre, peak_fk5
def measure_hfr(buf):
"""
measure focus using HFR
:param buf:
:return:
"""
im = np.frombuffer(buf, dtype=np.uint8).reshape((240, 320, 3))
# sub background
median = np.median(im[:, :, 1])
Im = im[:, :, 1] - median
# find star
max = np.max(Im)
tbl = find_peaks(Im, max - median, box_size=100, subpixel=False)
x, y = int(tbl['x_peak'][0]), int(tbl['y_peak'][0])
# improve center estimate by centroid
d = 50
X, Y = np.arange(x - d, x + d), np.arange(y - d, y + d)
V = Im[X, Y]
D = sum(V)
x = int(sum(V * X) / D)
y = int(sum(V * Y) / D)
# use new center for hfr
X, Y = np.arange(x - d, x + d), np.arange(y - d, y + d)
V = Im[X, Y]
D = np.array(np.vstack((X - x, Y - y)))
D = np.linalg.norm(D, axis=0)
hfr = np.sum(V * D) / np.sum(V)
return hfr
def locate_peaks(self, sigma_level, kernel=None):
"""
This function ...
:param sigma_level:
:param kernel:
:return:
"""
# If a subtracted box is present, use it to locate the peaks
box = self.subtracted if self.has_background else self.cutout
# Calculate the sigma-clipped statistics of the box
mean, median, stddev = statistics.sigma_clipped_statistics(
box, sigma=3.0, mask=self.background_mask
) # Sigma 3.0 for clipping is what photutils uses in detect_threshold
#sigma_level = 1.5 # I once tried to investigate why some clear peaks were not detected, did not have time ..
threshold = median + (sigma_level * stddev)
# Convolve the box with the given kernel, if any
if kernel is not None:
box = convolve_fft(box, kernel, normalize_kernel=True)
# Find peaks
#threshold = detect_threshold(box, snr=2.0) # other method (snr corresponds to sigma_level as I use it above)
peaks = find_peaks(box,
threshold,
box_size=5,
mask=self.background_mask)
# For some reason, once in a while, an ordinary list comes out of the find_peaks routine instead of an
# Astropy Table instance. We assume we need an empty table in this case
if type(peaks) is list:
peaks = Table([[], []], names=('x_peak', 'y_peak'))
# Initialize a list to contain the peak positions
positions = []
# Loop over the peaks
for peak in peaks:
# Calculate the absolute x and y coordinate of the peak
x_rel = peak['x_peak']
y_rel = peak['y_peak']
x = x_rel + self.cutout.x_min
y = y_rel + self.cutout.y_min
# Check whether the peak position falls in the box and then add it to the list
peak_position = Position(x, y)
if self.cutout.contains(peak_position):
# Add the coordinates to the positions list
positions.append(peak_position)
else:
print("DEBUG: peak position", peak_position,
"falls outside of box with shape", box.shape)
# If exactly one peak was found, set the self.peak attribute accordingly
if len(positions) == 1: self.peak = positions[0]
# Return the list of peak positions
return positions
def peak_detection(denormed_img, band, shifts, img_size, npeaks,
nb_blended_gal, training_or_test, dist_cut):
'''
Return coordinates of the centroid of the closest galaxy from the center
Parameters:
----------
denormed_img: denormalized image of the blended galaxies
band: filter in which the detection is done
shifts: initial shifts used for the image generation
img_size: size of the image
npeaks: maximum number of peaks to return for photutils find_peaks function
nb_blended_gal: number of blended galaxies in the image
training_or_test: choice of training or test sample being generated
dist_cut: cut in distance to check if detected galaxy is not too close from its neighbours
'''
gal = denormed_img
df_temp = photutils.find_peaks(gal,
threshold=5 *
np.sqrt(sky_level_pixel[band]),
npeaks=npeaks,
centroid_func=centroid_com)
if df_temp is not None:
df_temp['x_peak'] = (df_temp['x_centroid'] -
((img_size / 2.) - 0.5)) * pixel_scale[band]
df_temp['y_peak'] = (df_temp['y_centroid'] -
((img_size / 2.) - 0.5)) * pixel_scale[band]
df_temp.sort('peak_value', reverse=True)
# Distances of true centers to brightest peak
qq = [
np.sqrt(
float((shifts[j, 0] - df_temp['x_peak'][0])**2 +
(shifts[j, 1] - df_temp['y_peak'][0])**2))
for j in range(nb_blended_gal)
]
idx_closest = np.argmin(qq)
if nb_blended_gal > 1:
# Distance from peak galaxy to others
qq_prime = [
np.sqrt(
float((shifts[idx_closest, 0] - shifts[j, 0])**2 +
(shifts[idx_closest, 1] - shifts[j, 1])**2))
if j != idx_closest else np.inf for j in range(nb_blended_gal)
]
idx_closest_to_peak_galaxy = np.argmin(qq_prime)
if training_or_test != 'test':
if not np.all(np.array(qq_prime) > dist_cut):
print(
'TRAINING CUT: closest is not central and others are too close'
)
return False
else:
idx_closest_to_peak_galaxy = np.nan
return idx_closest, idx_closest_to_peak_galaxy, df_temp[0][
'x_centroid'], df_temp[0]['y_centroid'], df_temp[0][
'x_peak'], df_temp[0]['y_peak'], len(df_temp)
else:
return False
def peak_finder(self, map_data, mask_pf=False):
x_lim = np.size(self.xgrid)
y_lim = np.size(self.ygrid)
fact = 20.
bs = np.array(
[int(np.floor(y_lim / fact)),
int(np.floor(x_lim / fact))])
mean, median, std = sigma_clipped_stats(self.data, sigma=3.0)
threshold = median + (5. * std)
if mask_pf is False:
tbl = find_peaks(map_data, threshold, box_size=bs)
mask_pf = np.zeros_like(self.xy_mesh[0])
else:
self.mask = mask_pf.copy()
tbl = find_peaks(map_data, threshold, box_size=bs, mask=self.mask)
tbl['peak_value'].info.format = '%.8g'
guess = np.array([])
x_i = np.array([])
y_i = np.array([])
for i in range(len(tbl['peak_value'])):
guess_temp = np.array([tbl['peak_value'][i], self.xgrid[tbl['x_peak'][i]], \
self.ygrid[tbl['y_peak'][i]], 1., 1., 0.])
guess = np.append(guess, guess_temp)
index_x = self.xgrid[tbl['x_peak'][i]]
index_y = self.ygrid[tbl['y_peak'][i]]
x_i = np.append(x_i, index_x)
y_i = np.append(y_i, index_y)
mask_pf[index_y - bs[1]:index_y + bs[1],
index_x - bs[0]:index_x + bs[0]] = True
if self.param is None:
self.param = guess_temp
self.mask = mask_pf.copy()
else:
self.param = np.append(self.param, guess_temp)
self.mask = np.logical_or(self.mask, mask_pf)
def _find_saturated(self, data, sat_thresh, search_fwhm):
"""Use threshold search to look for regions of saturated pixels
"""
boxsize = int(4 * search_fwhm)
self._logger.debug(f'Looking for possibly saturated regions above {sat_thresh} ADU separated by {boxsize} pixels.')
saturated_positions = find_peaks(data,
threshold=sat_thresh,
box_size=boxsize)
print('Saturated position table:\n', saturated_positions)
return saturated_positions
def locate_peaks(self, sigma_level, kernel=None):
"""
This function ...
:param sigma_level:
:param kernel:
:return:
"""
# If a subtracted box is present, use it to locate the peaks
box = self.subtracted if self.has_background else self.cutout
# Calculate the sigma-clipped statistics of the box
mean, median, stddev = statistics.sigma_clipped_statistics(box, sigma=3.0, mask=self.background_mask.data) # Sigma 3.0 for clipping is what photutils uses in detect_threshold
#sigma_level = 1.5 # I once tried to investigate why some clear peaks were not detected, did not have time ..
threshold = median + (sigma_level * stddev)
# Convolve the box with the given kernel, if any
if kernel is not None: box = convolve_fft(box, kernel, normalize_kernel=True)
# Find peaks
#threshold = detect_threshold(box, snr=2.0) # other method (snr corresponds to sigma_level as I use it above)
peaks = find_peaks(box, threshold, box_size=5, mask=self.background_mask)
# For some reason, once in a while, an ordinary list comes out of the find_peaks routine instead of an
# Astropy Table instance. We assume we need an empty table in this case
if type(peaks) is list: peaks = Table([[], []], names=('x_peak', 'y_peak'))
# Initialize a list to contain the peak positions
positions = []
# Loop over the peaks
for peak in peaks:
# Calculate the absolute x and y coordinate of the peak
x_rel = peak['x_peak']
y_rel = peak['y_peak']
x = x_rel + self.cutout.x_min
y = y_rel + self.cutout.y_min
# Check whether the peak position falls in the box and then add it to the list
peak_position = PixelCoordinate(x, y)
if self.cutout.contains(peak_position):
# Add the coordinates to the positions list
positions.append(peak_position)
else: print("DEBUG: peak position", peak_position, "falls outside of box with shape", box.shape)
# If exactly one peak was found, set the self.peak attribute accordingly
if len(positions) == 1: self.peak = positions[0]
# Return the list of peak positions
return positions
def construct_epsf(imdata, mean):
peaks_tbl = find_peaks(imdata, threshold=20.0)
peaks_tbl['peak_value'].info.format = '%.8g'
for i in range(len(peaks_tbl)):
print(i, peaks_tbl['x_peak'][i], peaks_tbl['y_peak'][i])
#rem_indA = [0,1,3,4,5,6,8,9,11,12,13,16,84,85,86,87,91,92,99,102]
#rem_indB = [0,1,3,4,5,6,8,9,11,12,13,15,16,17,24,25,61,62,73,78,84,85,86,87,91,92,99,102]
#rem_indC = [0,1,2,3,4,5,6,8,9,10,11,12,13,15,16,17,22,23,24,25,61,62,71,73,77,78,84,85,86,87,91,92,94,96,99,102]
#rem_indVIS = [2,3,4,5,6,7,8,9,10,12,13,14]
#rem_indD = [3,5]
#rem_indVIS2 = [0,1,2,7]
#rem_indE = [22,23,24,25,27,28,29]
#rem_indF = [11,12,13,15,16,18,19,20,21,22,23,24,25,26]
#rem_indG = [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]
#rem_indH=[4,5,6,7,8,9,10,11,12,13]
#rem_indI = [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]
#rem_indJ = [0,1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,22,25,27,28,29,30,33,34,36,37,38,39]
#rem_indK = [0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,23,24,25,26,29,30,31,33,34,35]
#rem_indL = [4,5,6,7,8,10,11,12,13]
#rem_indM = [5,6,7,8,9,10,11,13,14,15,16,18,27,29]
#rem_indN = [4,5,6,7,8,9,10,11,13,14,15,16,17,19,20,21]
#peaks_tbl.remove_rows([rem_indM])
plt.figure()
plt.scatter(peaks_tbl['x_peak'], peaks_tbl['y_peak'], c='k')
plt.imshow(imdata, vmin=-0.2, vmax=2., origin='lowerleft')
plt.show()
stars_tbl = Table()
stars_tbl['x'] = peaks_tbl['x_peak']
stars_tbl['y'] = peaks_tbl['y_peak']
mean_val, median_val, std_val = sigma_clipped_stats(imdata, sigma=2.0)
imdata -= median_val
nddata = NDData(data=imdata)
stars = extract_stars(nddata, stars_tbl, size=20)
epsf_builder = EPSFBuilder(oversampling=4, maxiters=3, progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
hdu = fits.PrimaryHDU(epsf.data)
hdul = fits.HDUList([hdu])
hdul.writeto('Serpens3/epsf.fits')
return stars, epsf
def find_sources_experimental(self):
"""
This function ...
:return:
"""
kernel = self.star_finder.kernel._array
result = match_template(self.frame, kernel, pad_input=True)
#plotting.plot_box(result)
#y, x = np.unravel_index(np.argmax(result), result.shape)
#source = Source.around_coordinate(self.frame, Coordinate(x,y), radius=5, factor=1.3)
#source.plot()
#from ...core.basics.distribution import Distribution
#distribution = Distribution.from_values(result.flatten())
#distribution.plot()
from photutils import find_peaks
mask = Mask(self.galaxy_finder.segments) + Mask(
self.star_finder.segments)
peaks = find_peaks(result, 0.8, box_size=5, mask=mask)
index = 1
self.segments = Frame.zeros_like(self.frame)
# Loop over the peaks
for peak in peaks:
# Calculate the absolute x and y coordinate of the peak
x = peak['x_peak']
y = peak['y_peak']
coordinate = Coordinate(x, y)
source = Source.around_coordinate(self.frame,
coordinate,
radius=5,
factor=1.3)
self.segments[source.y_slice, source.x_slice][source.mask] = index
index += 1
def find_sources_experimental(self):
"""
This function ...
:return:
"""
# Inform the user
log.info("Finding sources based on template matching ...")
kernel = self.kernel._array
result = match_template(self.frame, kernel, pad_input=True)
#plotting.plot_box(result)
#y, x = np.unravel_index(np.argmax(result), result.shape)
#source = Source.around_coordinate(self.frame, Coordinate(x,y), radius=5, factor=1.3)
#source.plot()
#from ...core.basics.distribution import Distribution
#distribution = Distribution.from_values(result.flatten())
#distribution.plot()
from photutils import find_peaks
mask = Mask(self.galaxy_segments) + Mask(self.star_segments)
peaks = find_peaks(result, 0.8, box_size=5, mask=mask)
index = 1
self.segments = Frame.zeros_like(self.frame)
# Loop over the peaks
for peak in peaks:
# Calculate the absolute x and y coordinate of the peak
x = peak['x_peak']
y = peak['y_peak']
coordinate = Coordinate(x,y)
source = Detection.around_coordinate(self.frame, coordinate, radius=5, factor=1.3)
self.segments[source.y_slice, source.x_slice][source.mask] = index
index += 1
def get_photometry(image, mask=None, gain=4., pos=(dx_stamp, dx_stamp),
radii=10.):
sigma_clip = SigmaClip(sigma=3., iters=2)
bkg_estimator = MedianBackground()
bkg = Background2D(image, (10, 10), sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
print '\tBackground stats: %f, %f' % (bkg.background_median,
bkg.background_rms_median)
data = image - bkg.background
# bkg_err = np.random.normal(bkg.background_median,
# bkg.background_rms_median, image.shape)
if False:
plt.imshow(data, cmap='viridis', interpolation='nearest')
plt.colorbar()
plt.show()
# error = calc_total_error(image, bkg_err, gain)
back_mean, back_median, back_std = sigma_clipped_stats(data, mask, sigma=3,
iters=3,
cenfunc=np.median)
print '\tBackground stats: %f, %f' % (back_median, back_std)
tbl = find_peaks(data, np.minimum(back_std, bkg.background_rms_median) * 3,
box_size=5, subpixel=True)
print tbl
tree_XY = cKDTree(np.array([tbl['x_peak'], tbl['y_peak']]).T)
dist, indx = tree_XY.query(pos, k=1, distance_upper_bound=5)
if np.isinf(dist):
print '\tNo source found in the asked position...'
return None
position = [tbl[indx]['x_centroid'], tbl[indx]['y_centroid']]
print '\tObject position: ', position
apertures = [CircularAperture(position, r=r) for r in radii]
phot_table = aperture_photometry(data, apertures, mask=mask,
method='subpixel', subpixels=5)
for k, r in enumerate(radii):
area = np.pi * r ** 2
phot_table['aperture_flx_err_%i' %
k] = np.sqrt(area * bkg.background_rms_median ** 2 +
phot_table['aperture_sum_%i' % k] / gain)
phot_table.remove_columns(['xcenter', 'ycenter'])
phot_table['xcenter'] = position[0]
phot_table['ycenter'] = position[1]
return phot_table
def get_centroid(data):
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
threshold = median + (10.0 * std)
tbl = find_peaks(data, threshold, box_size=5)
#limit location of x and y based on quick look image
half=data.shape[1]/2
xlim = [i for i,(j,k) in enumerate(zip(tbl['x_peak'],tbl['y_peak'])) if half-20 < j < half+20 and 200 < k < 800]
try:
for idx in xlim:
#get index of brightest source within the limit
if tbl['peak_value'][idx] == max(tbl[xlim]['peak_value']):
#import pdb; pdb.set_trace()
x_max, y_max = int(tbl['x_peak'][idx]), int(tbl['y_peak'][idx])
except:
x_max, y_max = 100,100
print('no centroid')
return (x_max, y_max)#tuple
def optimize_m(t_ini, f_ini, alpha_ini, sig_curr):
#keeping in mind that minimize requires flattened arrays
grad_fun = lambda tg: -1 * grad_lnpost(tg, f_ini, alpha_ini, sig_curr)
res = scipy.optimize.minimize(
lambda tt: -1 * lnpost(tt, f_ini, alpha_ini, sig_curr),
t_ini, # theta initial
jac=grad_fun,
method='L-BFGS-B',
bounds=[(1e-5, 10)] * len(t_ini))
tt_prime = res['x']
print(res['nit'])
w_final = tt_prime.reshape((n_grid, n_grid))
print(w_final)
#pick out the peaks using photutils
thresh = detect_threshold(w_final, 3)
tbl = find_peaks(w_final, thresh)
positions = np.transpose((tbl['x_peak'], tbl['y_peak']))
w_peaks = np.zeros((n_grid, n_grid))
w_peaks[positions] = w_final[positions]
return w_peaks
def BuildEPSF(filter='Ks'):
"""Builds the effective PSF used for the photometry.
Currently uses the SCAO PSF from SimCADO.
"""
src = sim.source.star(mag=19, filter_name=filter, spec_type='M0V')
image = img.MakeImage(src,
exposure=1800,
NDIT=1,
view='wide',
chip='centre',
filter=filter,
ao_mode='scao')
# PSF_AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits
peaks_tbl = phu.find_peaks(image, threshold=135000., box_size=11)
peaks_tbl['peak_value'].info.format = '%.8g' # for consistent table output
# make sure the positions are correct (use the exact ones)
peaks_tbl['x_peak'] = src.x_pix
peaks_tbl['y_peak'] = src.y_pix
positions = (peaks_tbl['x_peak'], peaks_tbl['y_peak'])
apertures = phu.CircularAperture(positions, r=5.)
# extract cutouts of the stars using the extract_stars() function
stars_tbl = apta.Table()
stars_tbl['x'] = peaks_tbl['x_peak']
stars_tbl['y'] = peaks_tbl['y_peak']
mean_val, median_val, std_val = apy.stats.sigma_clipped_stats(img_data,
sigma=2.)
img_data -= median_val # subtract background
nddata = apy.nddata.NDData(data=img_data)
stars = phu.psf.extract_stars(nddata, stars_tbl, size=170)
# build the epsf
epsf_builder = phu.EPSFBuilder(oversampling=4,
maxiters=5,
progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
# save the epsf
with open(os.path.join('objects', 'epsf-scao-fv.pkl'), 'wb') as output:
pickle.dump(epsf, output, -1)
def build_epsf(image_num):
size = 7
hsize = (size - 1) / 2
peaks_tbl = find_peaks(Reduced_Image_Data[image_num], threshold=750.)
x = peaks_tbl['x_peak']
y = peaks_tbl['y_peak']
mask = ((x > hsize) &
(x < (Reduced_Image_Data[image_num].shape[1] - 1 - hsize)) &
(y > hsize) &
(y < (Reduced_Image_Data[image_num].shape[0] - 1 - hsize)))
stars_tbl = Table()
stars_tbl['x'] = x[mask]
stars_tbl['y'] = y[mask]
nddata = NDData(data=Reduced_Image_Data[image_num])
stars = extract_stars(nddata, stars_tbl, size=10)
#nrows = 5
#ncols = 5
#fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize = (15, 15), squeeze = True)
#ax = ax.ravel()
#for i in range(nrows*ncols):
#norm = simple_norm(stars[i], 'log', percent = 99.)
#ax[i].imshow(stars[i], norm = norm, origin = 'lower', cmap = 'gray')
epsf_builder = EPSFBuilder(oversampling=8, maxiters=20, progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
#norm = simple_norm(epsf.data, 'log', percent = 99.)
#plt.imshow(epsf.data, norm = norm, origin = 'lower', cmap = 'viridis')
#plt.colorbar()
return epsf
def plot_composites(pdata,outfolder,contours,contour_colors=True,
calibration_plot=True,brown_data=False,paperplot=False):
### image qualities
fs = 10 # fontsize
maxlim = 0.01 # limit of maximum
### contour color limits (customized for W1-W2)
color_limits = [-1.0,2.6]
kernel = None
### output blobs
gradient, gradient_error, arcsec,objname_out, obj_size_kpc, background_out = [], [], [], [], [], []
### begin loop
fig = None
for idx in xrange(len(pdata['objname'])):
if paperplot:
if ('NGC 4168' not in pdata['objname'][idx]) & ('NGC 1275' not in pdata['objname'][idx]):
continue
### load object information
objname = pdata['objname'][idx]
fagn = pdata['pars']['fagn']['q50'][idx]
ra, dec = load_coordinates(objname)
phot_size = load_structure(objname,long_axis=True) # in arcseconds
### load image and WCS
try:
if brown_data:
### convert from DN to flux in Janskies, from this table:
# http://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec2_3f.html
img1, noise1 = load_image(objname,contours[0]), None
img1, noise1 = img1*1.9350E-06, noise1*(1.9350e-06)**-2
img2, noise2 = load_image(objname,contours[1]), None
img2, noise2 = img2*2.7048E-06, noise2*(2.7048E-06)**-2
### translate object location into pixels using WCS coordinates
wcs = WCS(img1.header)
pix_center = wcs.all_world2pix([[ra[0],dec[0]]],1)
else:
img1, noise1 = load_wise_data(objname,contours[0].split(' ')[1])
img2, noise2 = load_wise_data(objname,contours[1].split(' ')[1])
### translate object location into pixels using WCS coordinates
wcs = WCS(img1.header)
pix_center = wcs.all_world2pix([[ra[0],dec[0]]],1)
if (pix_center.squeeze()[0]-4 > img1.shape[1]) or \
(pix_center.squeeze()[1]-4 > img1.shape[0]) or \
(np.any(pix_center < 4)):
print 'object not in image, checking for additional image'
print pix_center, img1.shape
img1, noise1 = load_wise_data(objname,contours[0].split(' ')[1],load_other = True)
img2, noise2 = load_wise_data(objname,contours[1].split(' ')[1],load_other = True)
wcs = WCS(img1.header)
pix_center = wcs.all_world2pix([[ra[0],dec[0]]],1)
print pix_center, img1.shape
except:
gradient.append(None)
gradient_error.append(None)
arcsec.append(None)
kpc.append(None)
objname_out.append(None)
continue
size = calc_dist(wcs, pix_center, phot_size, img1.data.shape)
### convert inverse variance to noise
noise1.data = (1./noise1.data)**0.5
noise2.data = (1./noise2.data)**0.5
### build image extents
extent = image_extent(size,pix_center,wcs)
### convolve W1 to W2 resolution
w1_convolved, kernel = match_resolution(img1.data,contours[0],contours[1],
kernel=kernel,data1_res=px_scale)
w1_convolved_noise, kernel = match_resolution(noise1.data,contours[0],contours[1],
kernel=kernel,data1_res=px_scale)
#### put onto same scale, and grab slices
data2, footprint = reproject_exact(img2, img1.header)
noise2, footprint = reproject_exact(noise2, img1.header)
img1_slice = w1_convolved[size[2]:size[3],size[0]:size[1]]
img2_slice = data2[size[2]:size[3],size[0]:size[1]]
noise1_slice = w1_convolved_noise[size[2]:size[3],size[0]:size[1]]
noise2_slice = noise2[size[2]:size[3],size[0]:size[1]]
### subtract background from both images
# identify background pixels.
# background is any pixel consistent within X sigma of background!
sigma = 3.0
if paperplot:
sigma = 5.0
mean1, median1, std1 = sigma_clipped_stats(w1_convolved, sigma=sigma,iters=10)
background1 = img1_slice < (median1+std1)
img1_slice -= median1
mean2, median2, std2 = sigma_clipped_stats(data2, sigma=sigma, iters=10)
background2 = img2_slice < (median2+std2)
img2_slice -= median2
#### calculate the color
flux_color = convert_to_color(img1_slice, img2_slice,None,None,contours[0],contours[1],
minflux=-np.inf, vega_conversions=brown_data)
### don't show any "background" pixels!
background = background1 | background2
flux_color[background] = np.nan
### plot colormap
count = 0
if paperplot:
if fig is None:
fig, axall = plt.subplots(1,2, figsize=(12,6))
fig.subplots_adjust(right=0.8,wspace=0.4,hspace=0.3,left=0.12)
cb_ax = fig.add_axes([0.83, 0.15, 0.05, 0.7])
ax = np.ravel(axall[0])
else:
ax = np.ravel(axall[1])
count = 1
vmin, vmax = -0.4,1.05
else:
fig, ax = plt.subplots(1,2, figsize=(12,6))
vmin, vmax = color_limits[0], color_limits[1]
ax = np.ravel(ax)
img = ax[0].imshow(flux_color, origin='lower',extent=extent,vmin=vmin,vmax=vmax,cmap='plasma')
if not paperplot:
cbar = fig.colorbar(img, ax=ax[0])
elif count == 0:
cbar = fig.colorbar(img, cax=cb_ax)
cbar.set_label('(W1-W2) [Vega]', fontdict={'fontsize':18})
ax[0].set_xlabel(r'$\Delta$(arcsec)')
ax[0].set_ylabel(r'$\Delta$(arcsec)')
### plot W1 contours
if not paperplot:
plot_contour(ax[0],np.log10(img2_slice),ncontours=20)
### find image center in W2 image, and mark it
# do this by finding the source closest to center
tbl = []
nthresh, box_size = 20, 4
fake_noise2_error = copy.copy(noise2_slice)
bad = np.logical_or(np.isinf(noise2_slice),np.isnan(noise2_slice))
fake_noise2_error[bad] = fake_noise2_error[~bad].max()
while len(tbl) < 1:
threshold = nthresh * std1 # peak threshold, @ 20 sigma
tbl = find_peaks(img2_slice, threshold, box_size=box_size, subpixel=True, border_width=3, error = fake_noise2_error)
nthresh -=2
if nthresh < 2:
nthresh = 20
box_size += 1
'''
center = np.array(img2_slice.shape)/2.
idxmax = ((center[0]-tbl['x_peak'])**2 + (center[1]-tbl['y_peak'])**2).argmin()
fig, ax = plt.subplots(1,1, figsize=(6,6))
ax.plot(tbl['x_peak'][idxmax],tbl['y_peak'][idxmax],'x',color='red',ms=10)
ax.imshow(img2_slice,origin='lower')
plot_contour(ax, np.log10(img2_slice),ncontours=20)
plt.show()
'''
### find size of biggest one
imgcenter = np.array(img2_slice.shape)/2.
idxmax = ((imgcenter[0]-tbl['x_centroid'])**2 + (imgcenter[1]-tbl['y_centroid'])**2).argmin()
center = [tbl['x_centroid'][idxmax], tbl['y_centroid'][idxmax]]
### find center in arcseconds (NEW)
center_coordinates = SkyCoord.from_pixel(imgcenter[0],imgcenter[1],wcs)
x_pos_obj = SkyCoord.from_pixel(center[0],imgcenter[1],wcs)
y_pos_obj = SkyCoord.from_pixel(imgcenter[0],center[1],wcs)
xarcsec = x_pos_obj.separation(center_coordinates).arcsec
if center[0] < imgcenter[0]:
xarcsec = -xarcsec
yarcsec = y_pos_obj.separation(center_coordinates).arcsec
if center[1] < imgcenter[1]:
yarcsec = -yarcsec
#xarcsec = (extent[1]-extent[0])*center[0]/float(img2_slice.shape[0]) + extent[0]
#yarcsec = (extent[3]-extent[2])*center[1]/float(img2_slice.shape[1]) + extent[2]
ax[0].scatter(xarcsec,yarcsec,color='black',marker='x',s=50,linewidth=2)
### add in WISE PSF
wise_psf = 6 # in arcseconds
start = 0.85*extent[0]
if not paperplot:
ax[0].plot([start,start+wise_psf],[start,start],lw=2,color='k')
ax[0].text(start+wise_psf/2.,start+1, '6"', ha='center')
ax[0].set_xlim(extent[0],extent[1]) # reset plot limits b/c of text stuff
ax[0].set_ylim(extent[2],extent[3])
else:
ax[0].set_xlim(-65,65)
ax[0].set_ylim(-65,65)
### gradient
phys_scale = float(1./WMAP9.arcsec_per_kpc_proper(pdata['z'][idx]).value)
if objname == 'CGCG 436-030':
center[1] = center[1]+1.5
yarcsec += px_scale*1.5
grad, graderr, x_arcsec, back = measure_gradient(img1_slice,img2_slice,
noise1_slice, noise2_slice, background,
ax, center,
tbl['peak_value'][idxmax], (xarcsec,yarcsec),
phys_scale,paperplot=paperplot)
obj_size_phys = phot_size*phys_scale
if not paperplot:
ax[1].text(0.05,0.06,r'f$_{\mathrm{AGN,MIR}}$='+"{:.2f}".format(pdata['pars']['fagn']['q50'][idx])+\
' ('+"{:.2f}".format(pdata['pars']['fagn']['q84'][idx]) +
') ('+"{:.2f}".format(pdata['pars']['fagn']['q16'][idx])+')',
transform=ax[1].transAxes,color='black',fontsize=9)
ax[1].axvline(phot_size, linestyle='--', color='0.2',lw=2,zorder=-1)
else:
ax[0].text(0.98,0.94,objname,transform=ax[0].transAxes,fontsize=14,weight='bold',ha='right')
ax[0].text(0.98,0.88,r'$\nabla$(2 kpc)='+"{:.2f}".format(grad[1]),fontsize=14,transform=ax[0].transAxes,ha='right')
gradient.append(grad)
gradient_error.append(graderr)
arcsec.append(x_arcsec)
obj_size_kpc.append(obj_size_phys)
objname_out.append(objname)
background_out.append(back)
print objname, back
# I/O
outname = outfolder+'/'+objname+'.png'
if paperplot:
outname = outfolder+'/sample_wise_gradient.png'
if (not paperplot) | (count == 1):
if not paperplot:
plt.tight_layout()
plt.savefig(outname,dpi=150)
plt.close()
out = {
'gradient': np.array(gradient),
'gradient_error': np.array(gradient_error),
'arcsec': np.array(arcsec),
'obj_size_brown_kpc': np.array(obj_size_kpc),
'objname': objname_out,
'background_fraction': np.array(background_out)
}
if not paperplot:
pickle.dump(out,open(outfile, "wb"))
def get_most_confident_outputs(img_filename, patch_center_row,
patch_center_col, confident_th, gpu_id):
patch_size = 64
center = (patch_center_col, patch_center_row)
x_tmp = int(center[0] - patch_size / 2)
y_tmp = int(center[1] - patch_size / 2)
confident_connections = {}
confident_connections['x_peak'] = []
confident_connections['y_peak'] = []
confident_connections['peak_value'] = []
root_dir = './gt_dbs/MassachusettsRoads/test/images/'
img = Image.open(os.path.join(root_dir, img_filename))
img = np.array(img, dtype=np.float32)
h, w = img.shape[:2]
if x_tmp > 0 and y_tmp > 0 and x_tmp + patch_size < w and y_tmp + patch_size < h:
img_crop = img[y_tmp:y_tmp + patch_size, x_tmp:x_tmp + patch_size, :]
img_crop = img_crop.transpose((2, 0, 1))
img_crop = torch.from_numpy(img_crop)
img_crop = img_crop.unsqueeze(0)
inputs = img_crop / 255 - 0.5
# Forward pass of the mini-batch
inputs = Variable(inputs)
if gpu_id >= 0:
inputs = inputs.cuda()
p = {}
p['useRandom'] = 1 # Shuffle Images
p['useAug'] = 0 # Use Random rotations in [-30, 30] and scaling in [.75, 1.25]
p['inputRes'] = (64, 64) # Input Resolution
p['outputRes'] = (64, 64) # Output Resolution (same as input)
p['g_size'] = 64 # Higher means narrower Gaussian
p['trainBatch'] = 1 # Number of Images in each mini-batch
p['numHG'] = 2 # Number of Stacked Hourglasses
p['Block'] = 'ConvBlock' # Select: 'ConvBlock', 'BasicBlock', 'BottleNeck'
p['GTmasks'] = 0 # Use GT Vessel Segmentations as input instead of Retinal Images
model_dir = './results_dir/'
modelName = tb.construct_name(p, "HourGlass")
numHGScales = 4 # How many times to downsample inside each HourGlass
net = nt.Net_SHG(p['numHG'], numHGScales, p['Block'], 128, 1)
epoch = 130
net.load_state_dict(
torch.load(os.path.join(
model_dir,
os.path.join(model_dir,
modelName + '_epoch-' + str(epoch) + '.pth')),
map_location=lambda storage, loc: storage))
if gpu_id >= 0:
net = net.cuda()
output = net.forward(inputs)
pred = np.squeeze(
np.transpose(
output[len(output) - 1].cpu().data.numpy()[0, :, :, :],
(1, 2, 0)))
mean, median, std = sigma_clipped_stats(pred, sigma=3.0)
threshold = median + (10.0 * std)
sources = find_peaks(pred, threshold, box_size=3)
if visualize_graph_step_by_step:
fig, axes = plt.subplots(1, 2)
axes[0].imshow(img.astype(np.uint8))
mask_graph_skel = skeletonize(mask_graph > 0)
indxs = np.argwhere(mask_graph_skel == 1)
axes[0].scatter(indxs[:, 1], indxs[:, 0], color='red', marker='+')
axes[0].add_patch(
patches.Rectangle((x_tmp, y_tmp),
patch_size,
patch_size,
fill=False,
color='cyan',
linewidth=5))
img_crop_array = img[y_tmp:y_tmp + patch_size,
x_tmp:x_tmp + patch_size, :]
axes[1].imshow(img_crop_array.astype(np.uint8),
interpolation='nearest')
tmp_vector_x = []
tmp_vector_y = []
for ii in range(0, len(sources['peak_value'])):
if sources['peak_value'][ii] > confident_th:
tmp_vector_x.append(sources['x_peak'][ii])
tmp_vector_y.append(sources['y_peak'][ii])
axes[1].plot(tmp_vector_x,
tmp_vector_y,
ls='none',
color='red',
marker='+',
ms=25,
markeredgewidth=10)
axes[1].plot(32,
32,
ls='none',
color='cyan',
marker='+',
ms=25,
markeredgewidth=10)
plt.show()
if visualize_evolution:
if iter < 20 or (iter < 200 and iter % 20 == 0) or iter % 100 == 0:
plt.figure(figsize=(12, 12), dpi=60)
plt.imshow(img.astype(np.uint8))
mask_graph_skeleton = skeletonize(mask_graph > 0)
indxs_skel = np.argwhere(mask_graph_skeleton == 1)
plt.scatter(indxs_skel[:, 1],
indxs_skel[:, 0],
color='red',
marker='+')
plt.axis('off')
plt.savefig(directory + 'iter_%05d.png' % iter,
bbox_inches='tight')
plt.close()
indxs = np.argsort(sources['peak_value'])
for ii in range(0, len(indxs)):
idx = indxs[len(indxs) - 1 - ii]
if sources['peak_value'][idx] > confident_th:
confident_connections['x_peak'].append(sources['x_peak'][idx])
confident_connections['y_peak'].append(sources['y_peak'][idx])
confident_connections['peak_value'].append(
sources['peak_value'][idx])
else:
break
confident_connections = Table([
confident_connections['x_peak'], confident_connections['y_peak'],
confident_connections['peak_value']
],
names=('x_peak', 'y_peak', 'peak_value'))
return confident_connections
boxsize = 20 for i in range(n_real): realID = i+n_start #image_fits=fits.open(imgpath+'image_fsub1_'+str(realID)+'.fits') image_fits=fits.open(imgpath+'image_sie_'+str(realID)+'.fits') image=image_fits[0].data ## smoothing kernel=convolution.Gaussian2DKernel(beam) image=convolution.convolve(image,kernel) mean,median,std=sigma_clipped_stats(image,sigma=3.0) threshold=median+(20*std) peaks=find_peaks(image,threshold,box_size=5) # w/ convolve #print peaks x_pix,y_pix = np.array(peaks['x_peak']),np.array(peaks['y_peak']) f_img = np.array(peaks['peak_value']) if (len(x_pix)==4): x_img,y_img = x_pix-x_pix[img_idx],y_pix-y_pix[img_idx] ## ------ Gaussian fit ## img cut gaus2d = models.Gaussian2D(amplitude=1., x_mean=0, y_mean=0., x_stddev=1.,y_stddev=1.) fit_g = fitting.LevMarLSQFitter()
bcatall['dec'],
fwhm_small=small_k,
fwhm_big=big_k)
except:
rootLogger.info('Problems with dwarf filter')
# Create done file
f = open(donefile, 'w')
f.write(host)
f.close()
sys.exit()
# find peaks
small_k = 2.0
mn, med, std = stats.sigma_clipped_stats(clipped, sigma=3.0, iters=5)
nsigma = 2.5
tbl = find_peaks(clipped, med + nsigma, box_size=small_k * 2)
rootLogger.info(str(len(tbl)) + ' peaks found')
if len(tbl) == 0:
# Create done file
f = open(donefile, 'w')
f.write(host)
f.close()
sys.exit()
# add ra & dec positions of peaks found
a, b = extent[:2]
xvec = np.arange(a, b, (b - a) / clipped.shape[1])
a, b = extent[2:]
yvec = np.arange(a, b, (b - a) / clipped.shape[0])
# Not enough pixels in density image
def A3d(data, **kwargs):
which = kwargs.get('chosen_by', 'prox')
data_sum = np.nansum(data, axis=0)
Npixels = kwargs.get('N_pixels_max',
data_sum.shape[0] * data_sum.shape[1] / 2.)
sigma = kwargs.get('sigma', 2.)
data_sum_noneg = np.copy(data_sum)
data_sum_noneg[data_sum_noneg <= 0.] = 0.0
bkg2D = Background2D(data_sum_noneg,
data_sum_noneg.shape,
filter_size=(2, 2),
sigma_clip=SigmaClip(sigma=3., iters=2),
bkg_estimator=MedianBackground())
bkg = np.unique(bkg2D.background)
tbl = find_peaks(data_sum, threshold=bkg)
x0 = int(data[int(data.shape[0] * 2 / 3)].shape[1] / 2.)
y0 = int(data[int(data.shape[0] * 2 / 3)].shape[0] / 2.)
dist = np.array([])
for i in range(len(tbl['x_peak'])):
dist = np.append(
dist,
np.sqrt((x0 - tbl['x_peak'][i])**2 + (y0 - tbl['y_peak'][i])**2))
if which == 'prox':
chosen = np.where(dist == min(dist))[0]
if len(chosen) > 1:
for i in chosen:
vec_chos = np.array(
[tbl['peak_value'][i], tbl['peak_value'][i]])
chosen = np.where(tbl['peak_value'] == max(vec_chos))[0]
elif which == 'mxpeak':
chosen = np.where(tbl['peak_value'] == max(tbl['peak_value']))[0]
cx = int(tbl['x_peak'][chosen])
cy = int(tbl['y_peak'][chosen])
aperture = np.zeros_like(data_sum)
aperture[cy, cx] = 1.
c = 1
x = cy
y = cx
for n in count(start=1, step=1):
if n > data_sum.shape[0] and n > data_sum.shape[1]: break
if n % 2:
x += 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
A0 = aperture[x + 1, y] == 1
except:
A0 = False
try:
A1 = aperture[x - 1, y] == 1
except:
A1 = False
try:
A2 = aperture[x, y + 1] == 1
except:
A2 = False
try:
A3 = aperture[x, y - 1] == 1
except:
A3 = False
if A0 or A1 or A2 or A3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
for i in range(n):
y -= 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
B0 = aperture[x + 1, y] == 1
except:
B0 = False
try:
B1 = aperture[x - 1, y] == 1
except:
B1 = False
try:
B2 = aperture[x, y + 1] == 1
except:
B2 = False
try:
B3 = aperture[x, y - 1] == 1
except:
B3 = False
if B0 or B1 or B2 or B3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
for i in range(n):
x -= 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
C0 = aperture[x + 1, y] == 1
except:
C0 = False
try:
C1 = aperture[x - 1, y] == 1
except:
C1 = False
try:
C2 = aperture[x, y + 1] == 1
except:
C2 = False
try:
C3 = aperture[x, y - 1] == 1
except:
C3 = False
if C0 or C1 or C2 or C3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
else:
x -= 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
D0 = aperture[x + 1, y] == 1
except:
D0 = False
try:
D1 = aperture[x - 1, y] == 1
except:
D1 = False
try:
D2 = aperture[x, y + 1] == 1
except:
D2 = False
try:
D3 = aperture[x, y - 1] == 1
except:
D3 = False
if D0 or D1 or D2 or D3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
for i in range(n):
y += 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
E0 = aperture[x + 1, y] == 1
except:
E0 = False
try:
E1 = aperture[x - 1, y] == 1
except:
E1 = False
try:
E2 = aperture[x, y + 1] == 1
except:
E2 = False
try:
E3 = aperture[x, y - 1] == 1
except:
E3 = False
if E0 or E1 or E2 or E3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
for i in range(n):
x += 1
try:
if data_sum[x, y] >= sigma * bkg:
try:
F0 = aperture[x + 1, y] == 1
except:
F0 = False
try:
F1 = aperture[x - 1, y] == 1
except:
F1 = False
try:
F2 = aperture[x, y + 1] == 1
except:
F2 = False
try:
F3 = aperture[x, y - 1] == 1
except:
F3 = False
if F0 or F1 or F2 or F3:
aperture[x, y] = 1
c += 1
if c >= Npixels: break
except:
pass
if which == 'prox':
no_chosen = np.where(dist != min(dist))[0]
elif which == 'mxpeak':
no_chosen = np.where(tbl['peak_value'] != max(tbl['peak_value']))[0]
for k in no_chosen:
ncy = int(tbl['x_peak'][k])
ncx = int(tbl['y_peak'][k])
aperture[ncx, ncy] = 0
try:
aperture[ncx + 1, ncy] = 0
except:
pass
try:
aperture[ncx, ncy + 1] = 0
except:
pass
try:
aperture[ncx - 1, ncy] = 0
except:
pass
try:
aperture[ncx, ncy - 1] = 0
except:
pass
try:
aperture[ncx + 1, ncy + 1] = 0
except:
pass
try:
aperture[ncx + 1, ncy - 1] = 0
except:
pass
try:
aperture[ncx - 1, ncy - 1] = 0
except:
pass
try:
aperture[ncx - 1, ncy - 1] = 0
except:
pass
try:
aperture[ncx - 1, ncy + 1] = 0
except:
pass
try:
aperture[ncx - 1, ncy - 1] = 0
except:
pass
return aperture, c
def find_pinholes_regular(fname,
sname,
fdarkff,
fdark,
fff,
files,
ref_shape,
size,
threshold,
fwhm,
fitshape,
range_psf,
sigma=2.,
oversampling=4,
maxiters=3):
"""Finds and fits regullary spread pinhole positions with a ePSF in a FITS image.
Parameters
----------
fname : str
Folder name of the input fits files.
sname : str
Folder name of the returned found and matched pinhole positions (txt files)
fdarkff : string
Location of the dark images for the flat field images.
fdark : string
Location of the dark images for the raw images.
fff : string
Location of the flat field images.
files : (1, 2)-shaped int array
File range to create a median image
ref_shape : (1,2)-shaped array
Number of reference stars in x and y direction [x, y].
size : int
Rectangular size of the ePSF. Size must be an odd number.
threshold : float
The absolute image value above which to select sources.
fwhm : float
The full-width half-maximum (FWHM) of the major axis of the Gaussian kernel in units of pixels.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used to collect the data to do the fitting.
Can be an integer to be the same along both axes. E.g., 5 is the same as (5, 5), which means to fit only at the following
relative pixel positions: [-2, -1, 0, 1, 2]. Each element of fitshape must be an odd number.
range_psf : (1, 4)-shaped int array
Position range to compute epsf [xmin,xmax,ymin,ymax]
sigma : float
Number of standard deviations used to perform sigma clip with a astropy.stats.SigmaClip object.
oversampling : int or tuple of two int
The oversampling factor(s) of the ePSF relative to the input stars along the x and y axes.
The oversampling can either be a single float or a tuple of two floats of the form (x_oversamp, y_oversamp).
If oversampling is a scalar then the oversampling will be the same for both the x and y axes.
maxiters : int
The maximum number of iterations to perform.
Returns
-------
positions_sort : (N,2)-shaped array
Found and matched positions of the pinholes.
ref_positions : (N,2)-shaped array
Matched reference grid positions.
"""
#Load the sample of fits images
entries = os.listdir(fname)
data_col = np.array([fits.getdata(fname + '/' + entries[files[0]], ext=0)])
for k in range(files[0], files[1]):
data_col = np.append(data_col,
[fits.getdata(fname + '/' + entries[k], ext=0)],
axis=0)
#Data reduction: Darc current + Flatfield + bias
data_col = data_correction(data_col, fdarkff, fdark, fff)
#Claculate median image
data_full = np.median(data_col, axis=0)
data = data_full[range_psf[2]:range_psf[3], range_psf[0]:range_psf[1]]
#Find peaks in data
peaks_tbl = find_peaks(data, threshold=threshold)
peaks_tbl['peak_value'].info.format = '%.8g'
#Load data around found peaks
hsize = (size - 1) / 2
x = peaks_tbl['x_peak']
y = peaks_tbl['y_peak']
mask = ((x > hsize) & (x < (data.shape[1] - 1 - hsize)) & (y > hsize) &
(y < (data.shape[0] - 1 - hsize)))
stars_tbl = Table()
stars_tbl['x'] = x[mask]
stars_tbl['y'] = y[mask]
#Calculate mean, median, std
mean_val, median_val, std_val = sigma_clipped_stats(data, sigma=sigma)
data = data - median_val
#Find pinholes and create ePSF
nddata = NDData(data=data)
stars = extract_stars(nddata, stars_tbl, size=size)
epsf_builder = EPSFBuilder(oversampling=oversampling,
maxiters=maxiters,
progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
#Use ePSF to find precise locations of pinholes
daofind = DAOPhotPSFPhotometry(crit_separation=30,
threshold=threshold,
fwhm=fwhm,
psf_model=epsf,
fitshape=fitshape,
aperture_radius=12,
niters=1)
#Get positions
sources = daofind(data_full)
for col in sources.colnames:
sources[col].info.format = '%.8g'
pos_full = np.transpose((sources['x_fit'], sources['y_fit']))
#Plot found pinholes
apertures = CircularAperture(pos_full, r=10)
norm = ImageNormalize(stretch=SqrtStretch())
fig, ax = plt.subplots()
ax.set_title('Pinhole Positions')
ax.set(xlabel='x [pixel]', ylabel='y [pixel]')
ax.imshow(data_full, cmap='Greys', origin='lower', norm=norm)
apertures.plot(color='blue', lw=1.5, alpha=0.5)
ax.legend(['#pinholes = ' + str(len(pos_full[:, 0]))],
loc='lower left',
prop={'size': 12})
plt.show()
#Find central position
xcent = (np.max(pos_full[:, 0]) + np.min(pos_full[:, 0])) / 2
ycent = (np.max(pos_full[:, 1]) + np.min(pos_full[:, 1])) / 2
#Find positions at the edges to set base positions for linear transformatio to match pinholes with reference grid
distance = (pos_full[:, 0] - xcent)**2 + (pos_full[:, 1] - ycent)**2
pins = len(distance)
sort_distance = np.partition(distance,
(pins - 4, pins - 3, pins - 2, pins - 1))
maxpos = pos_full[distance == sort_distance[pins - 1]]
maxpos = np.append(maxpos,
pos_full[distance == sort_distance[pins - 2]],
axis=0)
maxpos = np.append(maxpos,
pos_full[distance == sort_distance[pins - 3]],
axis=0)
maxpos = np.append(maxpos,
pos_full[distance == sort_distance[pins - 4]],
axis=0)
b01 = maxpos[maxpos[:, 1] < ycent]
b23 = maxpos[maxpos[:, 1] > ycent]
posbase = np.array(b01[b01[:, 0] < xcent])
posbase = np.append(posbase, b01[b01[:, 0] > xcent], axis=0)
posbase = np.append(posbase, b23[b23[:, 0] < xcent], axis=0)
print(posbase)
#Sort positions by matching with reference grid
positions_sort, ref_positions = sort_positions_regular(
pos_full, posbase, ref_shape)
text = np.array([
positions_sort[:, 0], positions_sort[:, 1], ref_positions[:, 0],
ref_positions[:, 1]
])
text_trans = np.zeros((len(positions_sort[:, 0]), 4))
#Transpose text matrix
for k in range(0, 4):
for l in range(0, len(positions_sort[:, 0])):
text_trans[l][k] = text[k][l]
#Save data as txt file
np.savetxt(
sname + '.txt',
text_trans,
fmt='%1.9E',
delimiter='\t',
header=
' x-measured y-measured x-reference y-reference',
comments='')
return positions_sort, ref_positions
def get_sources(detection_frame,
mask=False,
sigma=5.0,
mode='DAO',
fwhm=2.5,
threshold=None,
npix=4,
return_segm_image=False):
"""
Main method used to identify sources in a detection frame and estimate their position.
Different modes are available, accesible through the ``mode`` keyword :
* DAO : uses the :class:`photutils:photutils.DAOStarFinder` method, adapted from DAOPHOT.
* IRAF : uses the :class:`photutils:photutils.IRAFStarFinder` method, adapted from IRAF.
* PEAK : uses the :func:`photutils:photutils.find_peaks` method, looking for local peaks above a given threshold.
* ORB : uses the :func:`ORB:orb.utils.astrometry.detect_stars` method, fitting stars in the frame
* SEGM : uses the :func:`photutils:photutils.detect_sources` method, segmenting the image.
The most reliable is SEGM.
Parameters
----------
detection_frame : 2D :class:`~numpy:numpy.ndarray`
Map on which the sources should be visible.
mask : 2D :class:`~numpy:numpy.ndarray` or bool, Default = False
(Optional) If passed, only sources inside the mask are detected.
sigma : float
(Optional) Signal to Noise of the detections we want to keep. Only used if threshold is None. In this case, the signal and the noise are computed with sigma-clipping on the deteciton frame. Default = 5
threshold : float or 2D :class:`~numpy:numpy.ndarray` of floats
(Optional) Threshold above which we consider having a detection. Default is None
mode : str
(Optional) One of the detection mode listed above. Dafault = 'DAO'
fwhm : float
(Optional) Expected FWHM of the sources. Default : 2.5
npix : int
(Optional) Only used by the 'SEGM' method : minimum number of connected pixels with flux above the threshold to make a credible source. Default = 4
return_segm_image : bool, Default = False
(Optional) Only used in the 'SEGM' mode. If True, returns the obtained segmentation image.
Returns
-------
sources : :class:`~pandas:pandas.DataFrame`
A DataFrame where each row represents a detection, with at least the positions named as ``xcentroid``, ``ycentroid`` (WARNING : using astropy convention). The other columns depend on the mode used.
"""
if mask is False:
mask = np.ones_like(detection_frame)
if threshold is None:
mean, median, std = sigma_clipped_stats(
detection_frame, sigma=3.0, iters=5,
mask=~mask.astype(bool)) #On masque la region hors de l'anneau
threshold = median + sigma * std
#On detecte sur toute la frame, mais on garde que ce qui est effectivement dans l'anneau
if mode == 'DAO':
daofind = DAOStarFinder(fwhm=fwhm, threshold=threshold)
sources = daofind(detection_frame)
elif mode == 'IRAF':
irafind = IRAFStarFinder(threshold=threshold, fwhm=fwhm)
sources = irafind(detection_frame)
elif mode == 'PEAK':
sources = find_peaks(detection_frame, threshold=threshold)
sources.rename_column('x_peak', 'xcentroid')
sources.rename_column('y_peak', 'ycentroid')
elif mode == 'ORB':
astro = Astrometry(detection_frame, instrument='sitelle')
path, fwhm_arc = astro.detect_stars(min_star_number=5000,
r_max_coeff=1.,
filter_image=False)
star_list = astro.load_star_list(path)
sources = Table([star_list[:, 0], star_list[:, 1]],
names=('ycentroid', 'xcentroid'))
elif mode == 'SEGM':
logging.info('Detecting')
segm = detect_sources(detection_frame, threshold, npixels=npix)
deblend = True
labels = segm.labels
if deblend:
# while labels.shape != (0,):
# try:
# #logging.info('Deblending')
# # fwhm = 3.
# # s = fwhm / (2.0 * np.sqrt(2.0 * np.log(2.0)))
# # kernel = Gaussian2DKernel(s, x_size = 3, y_size = 3)
# # kernel = Box2DKernel(3, mode='integrate')
# deblended = deblend_sources(detection_frame, segm, npixels=npix, labels=labels)#, filter_kernel=kernel)
# success = True
# except ValueError as e:
# #warnings.warn('Deblend was not possible.\n %s'%e)
# source_id = int(e.args[0].split('"')[1])
# id = np.argwhere(labels == source_id)[0,0]
# labels = np.concatenate((labels[:id], labels[id+1:]))
# success = False
# if success is True:
# break
try:
logging.info('Deblending')
# fwhm = 3.
# s = fwhm / (2.0 * np.sqrt(2.0 * np.log(2.0)))
# kernel = Gaussian2DKernel(s, x_size = 3, y_size = 3)
# kernel = Box2DKernel(3, mode='integrate')
deblended = deblend_sources(
detection_frame, segm,
npixels=npix) #, filter_kernel=kernel)
except ValueError as e:
warnings.warn('Deblend was not possible.\n %s' % e)
deblended = segm
logging.info('Retieving properties')
sources = source_properties(detection_frame, deblended).to_table()
else:
deblended = segm
logging.info('Retieving properties')
sources = source_properties(detection_frame, deblended).to_table()
logging.info('Filtering Quantity columns')
for col in sources.colnames:
if type(sources[col]) is Quantity:
sources[col] = sources[col].value
sources = mask_sources(sources, mask) # On filtre
df = sources.to_pandas()
if return_segm_image:
return deblended.array, df
else:
return df
def center(s_post, gal_cat, image_wcs, search_box_width):
# centroid of the gal_cat
# search box width = width of the search box around the signpost in arcmin. (5' x 5' BOX)
# image_wcs = WCS of the image where objects are detected.
# Search box
PIX_SCALE = 0.000239631 * 3600 # arcsec/pixel ; 0.000239631 degrees/pixel for IRAC-ch2 (SDWFS Survey)
wid_arcsec = search_box_width * 60 # arcsec
wid_pixels = int(wid_arcsec / PIX_SCALE) # in pixels
init_x, init_y = image_wcs.wcs_world2pix(
s_post[0], s_post[1], 0) # initial center estimate to draw box
xmin, xmax = int(init_x - (wid_pixels / 2.0)), int(
init_x + (wid_pixels / 2.0)) # boundaries
ymin, ymax = int(init_y - (wid_pixels / 2.0)), int(
init_y + (wid_pixels / 2.0)) # boundaries
# Galaxy catalog in image pixel coordinates
galpix = convpix(gal_cat, image_wcs) # objects in pixel coordinates
x_pixels, y_pixels = galpix[:, 0], galpix[:, 1]
# 2D-histogram on the grid in the search box
binstep = 5.0 # pixels, grid resolution
wght = np.asarray(gal_cat['ch2']) # weights: irac ch2[4.5 um] flux, uJy
xedges, yedges = np.arange(xmin, xmax + binstep,
binstep), np.arange(ymin, ymax + binstep,
binstep)
h, xedges, yedges = np.histogram2d(x_pixels,
y_pixels,
bins=(xedges, yedges),
weights=wght)
# Smoothing the 2D histogram of the galaxies in the search box
size = int(round(
(np.shape(h)[0]) / 4.0)) # kernel size : at least ~ 4 times smaller
stdev_ker = size / 4.0 # size = 4-sigma
gauss_kernel = Gaussian2DKernel(x_stddev=stdev_ker,
y_stddev=stdev_ker,
x_size=size,
y_size=size) # kernel
smoothed_data_gauss = convolve_fft(h.T,
gauss_kernel) # convolution/smoothing
# Peak of the histogram
x_org, y_org = (xedges[0] + xedges[1]) / 2.0, (
yedges[0] + yedges[1]) / 2.0 # origin of bin-coordinate
fp_m = find_peaks(smoothed_data_gauss, 0,
npeaks=1) # 1 peak with max value
xbin_peak, ybin_peak = fp_m['x_peak'][0], fp_m['y_peak'][
0] # x, y positions of the peak
x_p, y_p = (x_org + xbin_peak * binstep), (
y_org + ybin_peak * binstep) # conversion to pixel coordinates
peak_fk5 = image_wcs.wcs_pix2world(x_p, y_p, 0)
# Centroid of the image moments
cen_pos = centroid_com(smoothed_data_gauss)
cenx, ceny = (x_org + cen_pos[0] * binstep), (y_org + cen_pos[1] * binstep)
centre = image_wcs.wcs_pix2world(cenx, ceny, 0)
return centre, peak_fk5
def centroid_fit(x,
y,
data,
reference=None,
rssframe=None,
galaxyid=None,
microns=True,
circular=True): #** reference,rssframe,galaxyid added
"""Fit the x,y,data values, regardless of what they are and return some useful stuff. Data is an array of spectra"""
working_dir = rssframe.strip('sci.fits')
# Smooth the data spectrally to get rid of cosmics
data_smooth = np.zeros_like(data)
for q in range(np.shape(data)[0]):
# data_smooth[q,:]=utils.smooth(data[q,:], 11) #default hanning smooth
data_smooth[q, :] = median_filter(data[q, :], 15)
# Now sum the data over a large range to get broad band "image"
data_sum = np.nansum(data_smooth[:, 200:1800], axis=1)
data_med = np.nanmedian(data_smooth[:, 200:1800], axis=1)
#** New masking method starts ————————————————————————————————————————————————
from scipy.ndimage.filters import gaussian_filter
from astropy.stats import sigma_clipped_stats
from photutils import find_peaks
# Parameter initializations
x0, y0 = x - np.min(x), y - np.min(y) # image x,y
xc, yc = (np.max(x) - np.min(x)) / 2. + np.min(
x), (np.max(y) - np.min(y)) / 2. + np.min(y) # central pixel
width = 85. # default gaussian filtering size
checkind = 'None' # for check list
img = np.zeros((np.max(x0) + 1, np.max(y0) + 1)) # rss image
x_good, y_good, data_sum_good = x, y, data_sum # good fibres to use
tx, ty, trad = xc, yc, 1000 #target x,y centre and masking radius (1000 means no masking)
if not os.path.exists(
working_dir + '_centroid_fit_reference/'
): # path to save centre of reference frame & checklist
os.makedirs(working_dir + '_centroid_fit_reference')
# Load fibre flux to image
for i in range(len(x0)):
img[x0[i], y0[i]] = data_sum[i]
# Gaussian filtering
img1 = gaussian_filter(
img, sigma=(width, width), order=0,
mode='constant') # width = diameter of a core in degrees/microns
# Find peaks
mean, median, std = sigma_clipped_stats(img1, sigma=3.0)
threshold = median + std
tbl = find_peaks(img1, threshold, box_size=105)
# Case1: If no peaks are found, masking is not applied. Actually I don't find any.
if tbl == None:
checkind = 'nopeak'
elif (len(tbl) < 1):
checkind = 'nopeak'
# Case2: A single peak is found
elif (len(tbl) == 1):
checkind = 'single'
dist = (tbl['y_peak'] + np.min(x) -
xc)**2 + (tbl['x_peak'] + np.min(y) -
yc)**2 # separation between a peak and centre
if (dist < (310)**2): # Single peak near the centre
tx, ty, trad = tbl['y_peak'] + np.min(x), tbl['x_peak'] + np.min(
y), 105 * 2 # y_peak is x. yes. it's right.
else: # When a peak is near the edge. High possibility that our target is not detected due to low brightness
for k in range(
1, 100
): # repeat until it finds multiple peaks with reduced filtering box
width = width * 0.98
img3 = gaussian_filter(
img,
sigma=(width, width),
order=0,
mode='constant',
cval=np.min(
img)) # width = diameter of a core in degrees/microns
mean, median, std = sigma_clipped_stats(img3, sigma=3.0)
threshold = median + std * 0.1
tbl = find_peaks(img3, threshold, box_size=width) #find peaks
if tbl == None:
continue
if (
len(tbl) == 1
): # only a single peak is found until maximum iteration (=100)
tx, ty, trad = tbl['y_peak'] + np.min(
x), tbl['x_peak'] + np.min(
y
), 1000 # fibre masking is not applied (trad = 1000)
checkind = 'single_edge'
if (len(tbl) > 1
): # multiple peaks are found, go to Case3: multiple peaks
checkind = 'multi_faint'
break
# Case3: When there are multiple peaks
elif (len(tbl) > 1):
if checkind is not 'multi_faint':
checkind = 'multi'
xx, yy = tbl['y_peak'] + np.min(x), tbl['x_peak'] + np.min(
y) # y_peak is x. yes. it's right.
# The assumption is that dithering is relatively small, and our target is near the target centre from the (1st) reference frame
if reference is not None and rssframe != reference and os.path.exists(
working_dir + '_centroid_fit_reference/centre_' + galaxyid +
'_ref.txt') != False:
fileref = open(
working_dir + '_centroid_fit_reference/centre_' + galaxyid +
'_ref.txt', 'r')
rx, ry = np.loadtxt(fileref, usecols=(0, 1))
coff = (xx - rx)**2 + (
yy - ry
)**2 # If not reference frame, the closest object from the reference
else:
coff = (xx - xc)**2 + (
yy - yc
)**2 # If reference frame, the closest object from the centre
tx, ty = xx[np.where(coff == np.min(coff))[0][0]], yy[np.where(
coff == np.min(coff))[0][0]] # target centre
xx, yy = xx[np.where(xx * yy != tx * ty)], yy[np.where(
xx * yy != tx * ty)]
osub = np.where(
((xx - tx)**2 + (yy - ty)**2 - np.min((xx - tx)**2 + (yy - ty)**2))
< 0.1) # the 2nd closest object
trad = np.sqrt(
(xx[osub] - tx)**2 + (yy[osub] - ty)**2
) / 2. # masking radius = (a separation btw the target and 2nd closest object)/2.
if (trad > 105 * 2): # when masking radius is too big
trad = 105 * 2
if (trad < 105 * 1.5): # when masking radius is too small
trad = 105 * 1.5
# Use fibres only within masking radius
gsub = np.where(np.sqrt((x - tx)**2 + (y - ty)**2) < trad)
if len(gsub) < 5:
tdist = np.sqrt((x - tx)**2 + (y - ty)**2)
inds = np.argsort(tdist)
gsub = inds[:5]
x_good, y_good, data_sum_good = x[gsub], y[gsub], data_sum[gsub]
# Save the target centre of reference frame
if reference is not None and rssframe == reference:
ref = open(
working_dir + '_centroid_fit_reference/centre_' + galaxyid +
'_ref.txt', 'w')
try:
ref.write(str(tx.data[0]) + ' ' + str(ty.data[0]))
except:
ref.write(str(tx) + ' ' + str(ty))
ref.close()
#** New masking method ends ————————————————————————————————————————————————
# Use the crude distributed centre-of-mass to get the rough centre of mass
com = utils.comxyz(x_good, y_good,
data_sum_good) #**use good data within masking
# Peak height guess could be closest fibre to com position.
dist = (x - com[0])**2 + (
y - com[1])**2 # distance between com and all fibres.
# First guess at width of Gaussian - diameter of a core in degrees/microns.
if microns == True:
sigx = 105.0
core_diam = 105.0
else:
sigx = 4.44e-4
core_diam = 4.44e-4
# First guess Gaussian parameters.
if circular == True:
p0 = [
data_sum[np.sum(np.where(dist == np.min(dist)))], com[0], com[1],
sigx, 0.0
]
#print "Guess Parameters:", p0 #here
elif circular == False:
p0 = [
data_sum[np.sum(np.where(dist == np.min(dist)))], com[0], com[1],
sigx, sigx, 45.0, 0.0
]
#print "Guess Parameters:", p0
# Fit two circular 2D Gaussians.
gf = fitting.TwoDGaussFitter(
p0, x_good, y_good, data_sum_good) #** use good data within masking
amplitude_mic, xout_mic, yout_mic, sig_mic, bias_mic = gf.p
fitting.fibre_integrator(gf, core_diam) # fibre integrator
gf.fit() #### gaussian fitting
# Make a linear grid to reconstruct the fitted Gaussian over.
x_0 = np.min(x)
y_0 = np.min(y)
# dx should be 1/10th the fibre diameter (in whatever units)
dx = sigx / 10.0
xlin = x_0 + np.arange(100) * dx # x axis
ylin = y_0 + np.arange(100) * dx # y axis
# Reconstruct the model
model = np.zeros((len(xlin), len(ylin)))
# Reconstructing the Gaussian over the proper grid.
for ii in range(len(xlin)):
xval = xlin[ii]
for jj in range(len(ylin)):
yval = ylin[jj]
model[ii, jj] = gf.fitfunc(gf.p, xval, yval)
amplitude_mic, xout_mic, yout_mic, sig_mic, bias_mic = gf.p
# print('gx,gy final',xout_mic,yout_mic) #test
return gf.p, data_sum, xlin, ylin, model
def find_peaks(imagename, ext=1, outname='default', threshold=40.0,
box_size=24.0, border_width=5.0):
#, footprint=footprint, mask=mask,
# border_width=border_width, npeaks=npeaks, subpixel=subpixel,
# error=error, wcs=wcs):
""" Extends `photutils.find_peaks`.
Parameters
----------
imagename : string
Name of the FITS file.
ext : int
The extension of the FITS file to read.
outname : string
Name of the output coordinate file. If "default", becomes,
"<imagename>.coo."
threshold : float
Default of "40.0."
box_size : float
Default of "24.0."
border_width : float
Default of "5.0."
Returns
-------
coo_tab : astropy.Table
Table containing the coordinates.
References
----------
http://photutils.readthedocs.org/en/latest/api/photutils.detection.find_peaks.html#photutils.detection.find_peaks
"""
# Fetch function metadata.
current_params = locals()
func_name = sys._getframe().f_code.co_name
# Read in FITS file.
hdulist = fits.open(imagename)
data = hdulist[ext].data
hdulist.close()
coo_tab = photutils.find_peaks(data=data, \
threshold=threshold, \
box_size=box_size, \
border_width=border_width)
#footprint=footprint, \
#mask=mask, \
#npeaks=npeaks, \
#subpixel=subpixel, \
#error=error, \
#wcs=wcs)
# Create basename for out file.
if outname == 'default':
baseoutname = imagename + '.coo'
else:
baseoutname = outname
# Check whether file already exists. If yes, append number.
fileoutname = baseoutname
i=1
while os.path.isfile(fileoutname):
fileoutname = baseoutname
fileoutname += ('.' + str(i))
i += 1
write_out_photfile(fileoutname, coo_tab, current_params, func_name)
return coo_tab
def build_ap_list(data,
start_aps = None,
radius = 3.0,
automatic_mode=True,
interactive_mode=False,
lam = None,
save_plot = None,
):
''' Detects local maxima in an image, and allows the manual inspection of the result.
This function finds local maxima in 2-D array, and then offers the user the ability
to check/modify the found peaks manually. It associate a fixed circular aperture to
each peak (individually modifiable manually), used later to extract the integrated s
pectra of these areas.
:Args:
data: 2-D numpy array
The raw data from which to find maxima.
start_aps: list of list [default: None]
A pre-existing list of apertures.
radius: int [default: 3.0]
The default radius of the apertures associated with the local maxima detected
automatically.
automatic_mode: bool [default: True]
Whether to detect peaks and assign apertures automatically or not.
interactive_mode: bool [default:True]
Whether to inspect the apertures manually or not.
lam: float [default: False]
The wavelength of the data, to be added to the plot title if set.
save_plot: string [default: None]
If set, the name of the file used to save the final aperture selection.
:Returns:
out: 2-D numpy array
The array of apertures with shape (n,3), where n is the total number of
apertures (1 per row), defined by (x,y,r) its center and radius (in pixels).
:Notes:
If start_aps are provided, then automatic_mode is False.
'''
mean, median, std = sigma_clipped_stats(data, sigma=3.0, iters=5)
# I tried to play with daofind at some point, with some limited success.
# It is very sensitive to varying background, and was not ideal to find HII regions
# at the core of galaxies.
# The next lines is left here for possible future usage, and remind me that I tried.
# sources = daofind(data, fwhm=5.0, threshold=1.*std)
# For now, these parameters are fixed. Until I see whether playing with them at the
# user level can help or not.
threshold = (1.0 * std)
#myf = np.array([[0,1,1,1,0],
# [1,1,1,1,1],
# [1,1,1,1,1],
# [1,1,1,1,1],
# [0,1,1,1,0]])
if not(start_aps) and automatic_mode:
# Find the local peak in the data.
tbl = photutils.find_peaks(data, threshold, box_size=5, subpixel=False)
xs = tbl['x_peak']
ys = tbl['y_peak']
rs = np.zeros(len(xs))+radius
elif start_aps:
# User provided us with apertures already. No need to start from scratch
xs,ys,rs = zip(*start_aps)
else:
xs = np.zeros(0)
ys = np.zeros(0)
rs = np.ones(0)
# Start the plotting
plt.close(90)
fig = plt.figure(90, figsize=(10,8))
gs = gridspec.GridSpec(1,1, height_ratios=[1], width_ratios=[1])
gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)
ax = plt.subplot(gs[0,0])
# Note, I will here be working in pixel space for simplicity. No need to bother
# with WCS coefficient for now.
ax.imshow(data, cmap='Greys', origin='lower',
norm=LogNorm(), vmin=1,
interpolation='nearest')
# Plot the peaks, and make them pickable !
line, = ax.plot(xs, ys, ls='none', color='tomato',
marker='+', ms=10, lw=1.5, picker=4)
# Plot the associated apertures, but DON'T make them pickable.
# Construct the size of the apertures associated with each peak, using the
# default value from the user.
# TODO: be smarter, and find the best aperture size for each peak.
# E.G. 2D gaussian fitting ?
# Also for now, just use a Circle
my_ap_scatter(ax, xs, ys, rs, facecolor='none',edgecolor='tomato')
# Now, the 1-line black magic command that deals with the user interaction
if interactive_mode:
apmanager = ApManager(fig, ax, line, rs)
ax.grid(True)
ax.set_xlim((-10,np.shape(data)[1]+10))
ax.set_xlabel(r'x [pixels]', labelpad = 10)
ax.set_ylim((-10,np.shape(data)[0]+10))
ax.set_ylabel(r'y [pixels]', labelpad = 10)
ax.set_title(r'$\lambda$:%.2f' % lam)
plt.show()
if interactive_mode:
print ' Starting interactive mode:'
print ' Aperture radius: 3 pixels'
print ' [left-click] : add an aperture'
print ' [right-click] : remove an aperture'
print ' [u]+cursor on plot: larger aperture '
print ' [d]+cursor on plot: smaller aperture '
# A little trick to wait for the user to be done before proceeding
while True:
letter = raw_input(' [m] to save and continue, [zzz] to NOT save and '+
'crash (maybe). \n')
if letter in ['m','zzz']:
break
else:
print ' [%s] unrecognized !' % letter
line.remove()
plt.savefig(save_plot+np.str(lam)+'.pdf',bbox_inches='tight')
plt.close()
if letter =='m':
return zip(apmanager.xs, apmanager.ys, apmanager.rs)
else:
return False
else:
line.remove()
plt.savefig(save_plot+np.str(lam)+'.pdf',bbox_inches='tight')
plt.close()
return zip(xs,ys,rs)
precision_patch = np.zeros(254, np.float32)
recall_patch = np.zeros(254, np.float32)
retina_img = Image.open(gt_base_dir + config_type +
'/img_%02d_patch_%02d_img.png' %
(idx, idx_patch))
pred = np.load(results_base_dir + config_type +
'/epoch_1800/img_%02d_patch_%02d.npy' %
(idx, idx_patch))
axes[0, config_id].imshow(retina_img)
mean, median, std = sigma_clipped_stats(pred, sigma=3.0)
threshold = median + (10.0 * std)
sources = find_peaks(pred, threshold, box_size=3)
positions = (sources['x_peak'], sources['y_peak'])
axes[2, config_id].imshow(pred, interpolation='nearest')
axes[2, config_id].plot(sources['x_peak'],
sources['y_peak'],
ls='none',
color='red',
marker='+',
ms=10,
lw=1.5)
gt_img = Image.open(gt_base_dir + config_type +
'/img_%02d_patch_%02d_gt.png' % (idx, idx_patch))
mean_gt, median_gt, std_gt = sigma_clipped_stats(gt_img, sigma=3.0)
from astropy.visualization import simple_norm
from photutils import datasets
hdu = datasets.load_simulated_hst_star_image()
data = hdu.data
from photutils.datasets import make_noise_image
data += make_noise_image(data.shape, type='gaussian', mean=10.,
stddev=5., random_state=12345)
from photutils import find_peaks
peaks_tbl = find_peaks(data, threshold=500.)
from astropy.table import Table
stars_tbl = Table()
stars_tbl['x'] = peaks_tbl['x_peak']
stars_tbl['y'] = peaks_tbl['y_peak']
from astropy.stats import sigma_clipped_stats
mean_val, median_val, std_val = sigma_clipped_stats(data, sigma=2.,
iters=None)
data -= median_val
from astropy.nddata import NDData
nddata = NDData(data=data)
from photutils.psf import extract_stars
stars = extract_stars(nddata, stars_tbl, size=25)
import matplotlib.pyplot as plt
nrows = 5
ncols = 5
def center(iter_no, s_post, member_indices, apr, plotmap):
mem = gcat[
member_indices] # gcat = The galaxy catalog of which the overdensities are being computed.
""" Member Galaxy Locations in Pixel Space """
galpix = convpix(mem, w)
x = galpix[:, 0]
y = galpix[:, 1]
""" Configuring Pixel Space"""
init_x, init_y = w.wcs_world2pix(s_post[0], s_post[1],
0) # initial estimate to draw box
PIX_SCALE = imagefile[0].header[
'CDELT2'] * 3600.0 # "/pixel ---> 0.000239 deg/pixel, IRAC/bootes
if apr <= 2.0:
wid_arcsec = 302 # ~5'x5' region around the signpost
elif 2.0 < apr <= 5.0:
wid_arcsec = 716.5 # ~12'x12'
else:
wid_arcsec = 898 # ~15'x15'
wid_pixels = int(wid_arcsec /
PIX_SCALE) # ~350x350 pixels/ 830x830 /1040x1040
xmin, xmax = int(init_x - (wid_pixels / 2.0)), int(
init_x + (wid_pixels / 2.0)) # boundaries
ymin, ymax = int(init_y - (wid_pixels / 2.0)), int(init_y +
(wid_pixels / 2.0))
""" 2D-Histogram & Smoothing """
binstep = 5.0 # 10 pixel ~ 8.62 "
wght = np.asarray(
gcat[member_indices]['ch2_flux']) # weights: irac ch2[4.5 um] flux
xedges = np.arange(xmin, xmax + binstep, binstep)
yedges = np.arange(ymin, ymax + binstep, binstep)
h, xedges, yedges = np.histogram2d(x,
y,
bins=(xedges, yedges),
weights=wght)
xmid = [(xedges[ik] + xedges[ik + 1]) / 2.0
for ik in range(len(xedges) - 1)]
ymid = [(yedges[ij] + yedges[ij + 1]) / 2.0
for ij in range(len(yedges) - 1)]
size = int(round((np.shape(h)[0]) /
4.0)) # kernel size : at least ~ 4 times smaller
stdev_ker = size / 4.0 # size = 4-sigma
gauss_kernel = Gaussian2DKernel(x_stddev=stdev_ker,
y_stddev=stdev_ker,
x_size=size,
y_size=size) # kernel
smoothed_data_gauss = convolve_fft(
h.T, gauss_kernel) # convolution/smoothing
max_sm_val = max(smoothed_data_gauss.flatten())
""" Finds Peak """
x_org, y_org = (xedges[0] + xedges[1]) / 2.0, (
yedges[0] + yedges[1]) / 2.0 # origin of bin-coordinate
fp_m = find_peaks(smoothed_data_gauss, 0,
npeaks=1) # 1 peak with max value
xbin_peak, ybin_peak = fp_m['x_peak'][0], fp_m['y_peak'][
0] # x, y positions of the peak
x_p, y_p = (x_org + xbin_peak * binstep), (
y_org + ybin_peak * binstep) # conversion to pixel coordinates
peak_pix = (x_p, y_p)
ra_p, dec_p = w.wcs_pix2world(peak_pix[0], peak_pix[1], 0)
peak_fk5 = (ra_p, dec_p)
""" Centroid from Image Moments """
cen_pos = centroid_com(smoothed_data_gauss)
cenx, ceny = (x_org + cen_pos[0] * binstep), (y_org +
cen_pos[1] * binstep)
ra_c, dec_c = w.wcs_pix2world(cenx, ceny, 0)
centre = (ra_c, dec_c)
""" Plots """
if plotmap == 'ON':
fgm = plt.figure()
ax = fgm.add_subplot(111)
# ax_b.imshow(smoothed_data_gauss, extent=[xmin, xmax, ymin, ymax], cmap='jet')
cntf = ax.contourf(
xmid,
ymid,
smoothed_data_gauss,
cmap='Blues',
levels=[lt for lt in np.arange(0.10, max_sm_val + 0.02, 0.02)])
ax.scatter(x, y, marker='o', color='red', s=5)
ax.scatter(cenx, ceny, marker='s')
ax.scatter(x_p, y_p, marker='^')
ax.scatter(init_x, init_y, marker='o', color='lime')
fgm.colorbar(cntf)
fgm.savefig(catdir + 'clump_plots/' + str(iter_no) + '_map.png',
format='png',
dpi=100)
plt.close(fgm)
return centre, peak_fk5
def get_most_confident_outputs(img_id, patch_center_row, patch_center_col,
confident_th, gpu_id, connected_same_vessel):
patch_size = 64
center = (patch_center_col, patch_center_row)
x_tmp = int(center[0] - patch_size / 2)
y_tmp = int(center[1] - patch_size / 2)
confident_connections = {}
confident_connections['x_peak'] = []
confident_connections['y_peak'] = []
confident_connections['peak_value'] = []
root_dir = './gt_dbs/DRIVE/'
img = Image.open(
os.path.join(root_dir, 'test', 'images', '%02d_test.tif' % img_id))
img = np.array(img, dtype=np.float32)
h, w = img.shape[:2]
if x_tmp > 0 and y_tmp > 0 and x_tmp + patch_size < w and y_tmp + patch_size < h:
img_crop = img[y_tmp:y_tmp + patch_size, x_tmp:x_tmp + patch_size, :]
img_crop = img_crop.transpose((2, 0, 1))
img_crop = torch.from_numpy(img_crop)
img_crop = img_crop.unsqueeze(0)
inputs = img_crop / 255 - 0.5
# Forward pass of the mini-batch
inputs = Variable(inputs)
if gpu_id >= 0:
inputs = inputs.cuda()
p = {}
p['useRandom'] = 1 # Shuffle Images
p['useAug'] = 0 # Use Random rotations in [-30, 30] and scaling in [.75, 1.25]
p['inputRes'] = (64, 64) # Input Resolution
p['outputRes'] = (64, 64) # Output Resolution (same as input)
p['g_size'] = 64 # Higher means narrower Gaussian
p['trainBatch'] = 1 # Number of Images in each mini-batch
p['numHG'] = 2 # Number of Stacked Hourglasses
p['Block'] = 'ConvBlock' # Select: 'ConvBlock', 'BasicBlock', 'BottleNeck'
p['GTmasks'] = 0 # Use GT Vessel Segmentations as input instead of Retinal Images
model_dir = './results_dir_vessels/'
if connected_same_vessel:
modelName = tb.construct_name(p, "HourGlass-connected-same-vessel")
else:
modelName = tb.construct_name(p, "HourGlass-connected")
numHGScales = 4 # How many times to downsample inside each HourGlass
net = nt.Net_SHG(p['numHG'], numHGScales, p['Block'], 128, 1)
epoch = 1800
net.load_state_dict(
torch.load(os.path.join(
model_dir,
os.path.join(model_dir,
modelName + '_epoch-' + str(epoch) + '.pth')),
map_location=lambda storage, loc: storage))
if gpu_id >= 0:
net = net.cuda()
output = net.forward(inputs)
pred = np.squeeze(
np.transpose(
output[len(output) - 1].cpu().data.numpy()[0, :, :, :],
(1, 2, 0)))
mean, median, std = sigma_clipped_stats(pred, sigma=3.0)
threshold = median + (10.0 * std)
sources = find_peaks(pred, threshold, box_size=3)
indxs = np.argsort(sources['peak_value'])
for ii in range(0, len(indxs)):
idx = indxs[len(indxs) - 1 - ii]
if sources['peak_value'][idx] > confident_th:
confident_connections['x_peak'].append(sources['x_peak'][idx])
confident_connections['y_peak'].append(sources['y_peak'][idx])
confident_connections['peak_value'].append(
sources['peak_value'][idx])
else:
break
confident_connections = Table([
confident_connections['x_peak'], confident_connections['y_peak'],
confident_connections['peak_value']
],
names=('x_peak', 'y_peak', 'peak_value'))
return confident_connections
image_fits=pyfits.open(filepath+filename) image=image_fits[0].data image_or=image ## smoothing kernel=convolution.Gaussian2DKernel(5) image=convolution.convolve(image,kernel) # get image size img_size=image.shape[0] print img_size mean,median,std=sigma_clipped_stats(image,sigma=3.0) threshold=median+(20*std) peaks=find_peaks(image,threshold,box_size=5) print peaks ''' ## throw out source peak # by distance to center center=img_size/2 dist_center=np.sqrt((peaks['x_peak']-center)**2+(peaks['y_peak']-center)**2) print dist_center idx=list(dist_center).index(np.min(dist_center)) threshold=np.min(peaks['peak_value'][idx]) peaks=find_peaks(image,threshold,box_size=5) print peaks #print len(peaks['x_peak'])
def get_photometry(image, mask=None, gain=4., pos=(dx_stamp, dx_stamp),
radii=10., sigma1=None, alpha=None, beta=None, iter=0):
print iter
sigma_clip = SigmaClip(sigma=3., iters=2)
bkg_estimator = MedianBackground()
bkg = Background2D(image, (10, 10), sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
print '\tBackground stats: %f, %f' % (bkg.background_median,
bkg.background_rms_median)
data = image - bkg.background
# bkg_err = np.random.normal(bkg.background_median,
# bkg.background_rms_median, image.shape)
if False:
plt.imshow(data, cmap='viridis', interpolation='nearest')
plt.colorbar()
# plt.show()
# error = calc_total_error(image, bkg_err, gain)
if True:
back_mean, back_median, back_std = sigma_clipped_stats(data, mask,
sigma=3,
iters=3,
cenfunc=np.median)
print '\tBackground stats: %f, %f' % (back_median, back_std)
tbl = find_peaks(data,
np.minimum(back_std, bkg.background_rms_median) * 3,
box_size=5, subpixel=True)
if len(tbl) == 0:
print '\tNo detection...'
return None
tree_XY = cKDTree(np.array([tbl['x_centroid'], tbl['y_centroid']]).T)
if iter == 0:
d = 9
else:
d = 5
dist, indx = tree_XY.query(pos, k=2, distance_upper_bound=d)
print tbl
print dist, indx
if np.isinf(dist).all():
print '\tNo source found in the asked position... ',
print 'using given position...'
position = pos
# return None
else:
if len(tbl) >= 2 and not np.isinf(dist[1]):
if tbl[indx[1]]['fit_peak_value'] > \
tbl[indx[0]]['fit_peak_value']:
indx = indx[1]
else:
indx = indx[0]
else:
indx = indx[0]
position = [tbl[indx]['x_centroid'], tbl[indx]['y_centroid']]
else:
position = pos
print '\tObject position: ', position
apertures = [CircularAperture(position, r=r) for r in radii]
try:
phot_table = aperture_photometry(data, apertures, mask=mask,
method='subpixel', subpixels=5)
except IndexError:
phot_table = aperture_photometry(data, apertures,
method='subpixel', subpixels=5)
for k, r in enumerate(radii):
area = np.pi * r ** 2
phot_table['aperture_flx_err_%i' %
k] = np.sqrt(sigma1**2 * alpha**2 * area**beta +
phot_table['aperture_sum_%i' % k][0] / gain)
phot_table.remove_columns(['xcenter', 'ycenter'])
phot_table['xcenter'] = position[0]
phot_table['ycenter'] = position[1]
return phot_table
if star == 'V0473_Lyr':
x_1 = 2100
x_2 = 2450
y_1 = 1700
y_2 = 2300
if star == 'RR_Lyr':
x_1 = 0
x_2 = 4090
y_1 = 0
y_2 = 4090
for image_name in files[0:1]:
image = fits.getdata(star + "/" + image_name, ext=0)
data = image[x_1:x_2, y_1:y_2]
tbl = find_peaks(data, 300, box_size=50)
pe = np.array(tbl['peak_value'])
pe_x = np.array(tbl['x_peak'])
pe_y = np.array(tbl['y_peak'])
peaks = np.array((pe_x, pe_y, pe)).T
peaks = peaks.tolist()
peaks = sorted(peaks, key=lambda t: t[2], reverse=True)
positions = (peaks[0][0], peaks[0][1]
) #[peaks[0][0], peaks[0][1]], [peaks[1][0], peaks[1][1]]
apertures = lambda r: CircularAperture(positions, r=r)
phot_table = lambda r: aperture_photometry(data, apertures(r))
rad = [0.0]
numcounts = [0.0]
noise = 10000
i = 0
while (noise >= 5000):
def find_pinholes_irregular(fname,
freference,
sname,
fdarkff,
fdark,
fff,
files,
size,
threshold,
fwhm,
fitshape,
MAX_CONTROL_POINTS,
PIXEL_TOL,
range_psf,
sigma=2.,
oversampling=4,
maxiters=3,
MIN_MATCHES_FRACTION=0.8,
NUM_NEAREST_NEIGHBORS=5):
"""Finds and fits irregularly spread pinhole positions with a ePSF in a FITS image. Then matches them to the reference positions.
Parameters
----------
fname : str
Folder name of the input fits files.
freference : str
File name of the reference positions (txt file).
sname : str
Folder name of the returned found and matched pinhole positions (txt files).
fdarkff : string
Location of the dark images for the flat field images.
fdark : string
Location of the dark images for the raw images.
fff : string
Location of the flat field images.
files : (1, 2)-shaped int array
File range to create a median image
size : int
Rectangular size of the ePSF. Size must be an odd number.
threshold : float
The absolute image value above which to select sources.
fwhm : float
The full-width half-maximum (FWHM) of the major axis of the Gaussian kernel in units of pixels.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used to collect the data to do the fitting.
Can be an integer to be the same along both axes.
E.g., 5 is the same as (5, 5), which means to fit only at the following
relative pixel positions: [-2, -1, 0, 1, 2]. Each element of fitshape must be an odd number.
MAX_CONTROL_POINTS : int
The maximum control points (stars) to use to build the invariants.
PIXEL_TOL : int
The pixel distance tolerance to assume two invariant points are the same.
range_psf : (1, 4)-shaped int array
Position range to compute epsf [xmin,xmax,ymin,ymax]
sigma : float
Number of standard deviations used to perform sigma clip with a astropy.stats.SigmaClip object.
oversampling : int or tuple of two int
The oversampling factor(s) of the ePSF relative to the input stars along the x and y axes.
The oversampling can either be a single float or a tuple of two floats of the form (x_oversamp, y_oversamp).
If oversampling is a scalar then the oversampling will be the same for both the x and y axes.
maxiters : int
The maximum number of iterations to perform.
MIN_MATCHES_FRACTION : float (0,1]
The minimum fraction of triangle matches to accept a transformation.
If the minimum fraction yields more than 10 triangles, 10 is used instead.
NUM_NEAREST_NEIGHBORS : int
The number of nearest neighbors of a given star (including itself) to construct the triangle invariants.
Returns
-------
s_list : (N,2)-shaped array
Found and matched positions of the pinholes.
t_list : (N,2)-shaped array
Matched reference grid positions.
"""
#Load the sample of fits images
entries = os.listdir(fname)
data_col = np.array([fits.getdata(fname + '/' + entries[files[0]], ext=0)])
for k in range(files[0] + 1, files[1] + 1):
data_col = np.append(data_col,
[fits.getdata(fname + '/' + entries[k], ext=0)],
axis=0)
#Data reduction: Darc current + Flatfield
data_col = data_correction(data_col, fdarkff, fdark, fff)
#Claculate median image
data_full = np.median(data_col, axis=0)
pos_full = np.array([[0, 0]])
data = data_full[range_psf[2]:range_psf[3], range_psf[0]:range_psf[1]]
#Find peaks in data
peaks_tbl = find_peaks(data, threshold=threshold)
peaks_tbl['peak_value'].info.format = '%.8g'
#Load data around found peaks
hsize = (size - 1) / 2
x = peaks_tbl['x_peak']
y = peaks_tbl['y_peak']
mask = ((x > hsize) & (x < (data.shape[1] - 1 - hsize)) & (y > hsize) &
(y < (data.shape[0] - 1 - hsize)))
stars_tbl = Table()
stars_tbl['x'] = x[mask]
stars_tbl['y'] = y[mask]
#Calculate mean, median, std
mean_val, median_val, std_val = sigma_clipped_stats(data, sigma=sigma)
data = data - median_val
#Find pinholes and create ePSF
nddata = NDData(data=data)
stars = extract_stars(nddata, stars_tbl, size=size)
epsf_builder = EPSFBuilder(oversampling=oversampling,
maxiters=maxiters,
progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
#Use ePSF to find precise locations of pinholes
daofind = DAOPhotPSFPhotometry(crit_separation=30,
threshold=threshold,
fwhm=fwhm,
psf_model=epsf,
fitshape=fitshape,
aperture_radius=12,
niters=1)
#Get positions
sources = daofind(data_full)
for col in sources.colnames:
sources[col].info.format = '%.8g'
pos = np.transpose((sources['x_fit'], sources['y_fit']))
pos_full = np.append(pos_full, pos, axis=0)
pos_full = pos_full[1:]
#Plot found pinholes
apertures = CircularAperture(pos_full, r=10)
norm = ImageNormalize(stretch=SqrtStretch())
#Plot found pinholes
fig, ax = plt.subplots()
ax.set_title('Pinhole Positions')
ax.set(xlabel='x [pixel]', ylabel='y [pixel]')
ax.imshow(data_full, cmap='Greys', origin='lower', norm=norm)
apertures.plot(color='blue', lw=1.5, alpha=0.5)
ax.legend(['#pinholes = ' + str(len(pos_full[:, 0]))],
loc='lower left',
prop={'size': 12})
plt.show()
#Sort positions by matching with reference grid
positions_sort = pos_full
ref_positions = np.genfromtxt(freference, skip_header=0)
transf, (s_list, t_list) = find_transform(positions_sort, ref_positions,
MAX_CONTROL_POINTS, PIXEL_TOL,
MIN_MATCHES_FRACTION,
NUM_NEAREST_NEIGHBORS)
text = np.array([s_list[:, 0], s_list[:, 1], t_list[:, 0], t_list[:, 1]])
text_trans = np.zeros((len(s_list[:, 0]), 4))
#Transpose text matrix
for k in range(0, 4):
for l in range(0, len(s_list[:, 0])):
text_trans[l][k] = text[k][l]
#Save data as txt file
np.savetxt(sname + '.txt',
text_trans,
fmt='%1.9E',
delimiter='\t',
comments='',
header='x-measured y-measured x-reference y-reference')
return s_list, t_list
gt_base_dir = './results_dir/gt_test'
results_base_dir = './results_dir/results'
idx_patch = 2
for idx in range(start_img,start_img+num_images):
print(idx)
config_type = '_connected'
retina_img = Image.open(gt_base_dir + config_type + '/img_%02d_patch_%02d_img.png' %(idx, idx_patch))
plt.imshow(retina_img)
pred = np.load(results_base_dir + config_type + '/epoch_1800/img_%02d_patch_%02d.npy' %(idx, idx_patch))
mean, median, std = sigma_clipped_stats(pred, sigma=3.0)
threshold = median + (10.0 * std)
sources = find_peaks(pred, threshold, box_size=3)
positions = (sources['x_peak'], sources['y_peak'])
pos_x_vector = []
pos_y_vector = []
for ii in range(0,len(sources['peak_value'])):
if sources['peak_value'][ii] > 20:
pos_x_vector.append(sources['x_peak'][ii])
pos_y_vector.append(sources['y_peak'][ii])
plt.scatter(pos_x_vector, pos_y_vector, marker='+', color='blue', s=100, linewidths=10)
plt.axis('off')
plt.show()
