def test_line_plot():
"""check the line plot function."""
plots = Imexamine()
plots.set_data(test_data)
plots.plot_line(50, 50)
f = plt.gca()
xplot, yplot = f.lines[0].get_xydata().T
assert_equal(yplot, test_data[:, 50])
plt.close()
def test_column_plot():
"""Check the column plot function."""
plots = Imexamine()
plots.set_data(test_data)
plots.plot_column(50, 50)
f = plt.gca()
xplot, yplot = f.lines[0].get_xydata().T
assert_equal(yplot, test_data[50, :])
plt.close()
def test_column_fit():
"""Fit a Gaussian1D column to the data."""
plots = Imexamine()
plots.set_data(test_data)
fit = plots.column_fit(50, 50, form='Gaussian1D', genplot=False)
amp = 2.8285560281694115
mean = 49.42625526973088
stddev = 12.791137635400535
assert_allclose(amp, fit.amplitude, 1e-6)
assert_allclose(mean, fit.mean, 1e-6)
assert_allclose(stddev, fit.stddev, 1e-6)
def test_line_fit():
"""Fit a Gaussian1D line to the data."""
plots = Imexamine()
plots.set_data(test_data)
fit = plots.line_fit(50, 50, form='Gaussian1D', genplot=False)
amp = 2.8152269683542137
mean = 49.45671107821953
stddev = 13.051081779478146
assert_allclose(amp, fit.amplitude, 1e-6)
assert_allclose(mean, fit.mean, 1e-6)
assert_allclose(stddev, fit.stddev, 1e-6)
def test_line_fit():
"""Fit a Gaussian1D line to the data."""
plots = Imexamine()
plots.set_data(test_data)
fit = plots.line_fit(50, 50, form='Gaussian1D', genplot=False)
amp = 2.8152269683542137
mean = 49.45671107821953
stddev = 13.051081779478146
assert_allclose(amp, fit.amplitude, 1e-6)
assert_allclose(mean, fit.mean, 1e-6)
assert_allclose(stddev, fit.stddev, 1e-6)
def test_column_fit():
"""Fit a Gaussian1D column to the data."""
plots = Imexamine()
plots.set_data(test_data)
fit = plots.column_fit(50, 50, form='Gaussian1D', genplot=False)
amp = 2.8285560281694115
mean = 49.42625526973088
stddev = 12.791137635400535
assert_allclose(amp, fit.amplitude, 1e-6)
assert_allclose(mean, fit.mean, 1e-6)
assert_allclose(stddev, fit.stddev, 1e-6)
def test_column_fit():
"""Fit a Gaussian1D column to the data."""
plots = Imexamine()
in_amp = 3.
in_mean = 50.
in_stddev = 2.
in_const = 20.
# Set all the columns to be Gaussians
col_gauss = in_const + in_amp * np.exp(-0.5 * ((yy - in_mean) / in_stddev)**2)
plots.set_data(col_gauss)
fit = plots.column_fit(50, 50, form='Gaussian1D', genplot=False)
assert_allclose(in_amp, fit.amplitude_0, 1e-6)
assert_allclose(in_mean, fit.mean_0, 1e-6)
assert_allclose(in_stddev, fit.stddev_0, 1e-6)
assert_allclose(in_const, fit.c0_1, 1e-6)
def test_column_fit():
"""Fit a Gaussian1D column to the data."""
plots = Imexamine()
in_amp = 3.
in_mean = 50.
in_stddev = 2.
in_const = 20.
# Set all the columns to be Gaussians
col_gauss = in_const + in_amp * np.exp(-0.5 * ((yy - in_mean) / in_stddev)**2)
plots.set_data(col_gauss)
fit = plots.column_fit(50, 50, form='Gaussian1D', genplot=False)
assert_allclose(in_amp, fit.amplitude_0, 1e-6)
assert_allclose(in_mean, fit.mean_0, 1e-6)
assert_allclose(in_stddev, fit.stddev_0, 1e-6)
assert_allclose(in_const, fit.c0_1, 1e-6)
def __init__(self, fv, fitsimage):
# superclass defines some variables for us, like logger
super(Imexam, self).__init__(fv, fitsimage)
# get Imexam preferences
prefs = self.fv.get_preferences()
self.settings = prefs.createCategory('plugin_Imexam')
self.settings.addDefaults(font='Courier',
fontsize=12,
plots_in_workspace=False)
self.settings.load(onError='silent')
self.layertag = 'imexam-canvas'
self.imexam_active = False
# this is our imexamine object
self.imex = Imexamine()
self.out_f = StringIO()
self.dc = fv.get_draw_classes()
canvas = self.dc.DrawingCanvas()
canvas.set_callback('key-press', self.key_press_cb)
canvas.set_surface(self.fitsimage)
canvas.register_for_cursor_drawing(self.fitsimage)
canvas.enable_draw(False)
canvas.name = 'Imexam-canvas'
self.canvas = canvas
self._plot = None
self._plot_w = None
self._plot_idx = 0
self._plots_in_ws = self.settings.get('plots_in_workspace', False)
self.w = Bunch.Bunch()
def test_radial_profile_background():
"""Test the radial profile function
With background subtraction
Cummulative pixels """
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
xx, yy = np.meshgrid(np.arange(25), np.arange(25))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
rad_in = rad_in.ravel()
flux_in = data.array.ravel()
order = np.argsort(rad_in)
rad_in = rad_in[order]
flux_in = flux_in[order]
plots = Imexamine()
plots.set_data(data.array)
# check the binned results
plots.radial_profile_pars['pixels'][0] = False
plots.radial_profile_pars['background'][0] = True
rad_out, flux_out = plots.radial_profile(x0, y0, genplot=False)
good = np.where(rad_in <= np.max(rad_out))
rad_in = rad_in[good]
flux_in = flux_in[good]
flux_in = np.bincount(rad_in.astype(np.int), flux_in)
assert_array_equal(np.arange(flux_in.size), rad_out)
assert_allclose(flux_in - flux_out, flux_out * 0, atol=1e-5)
def test_radial_profile():
"""Test the radial profile function without background subtraction"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
# check the binned results
plots.radial_profile_pars['pixels'][0] = False
x, y = plots.radial_profile(12, 12, genplot=False)
rad = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
flux = [4.53542348e-02, 3.00703719e-01, 3.54889792e-01,
1.95806071e-01, 7.56662018e-02, 2.46976310e-02,
2.54073324e-03, 1.51802506e-04, 1.08323362e-06,
3.65945812e-10]
assert_array_equal(rad, x)
assert_allclose(flux, y, 1e-7)
def test_curve_of_growth():
"""Test the cog functionality."""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
x, y = plots.curve_of_growth(12, 12, genplot=False)
rad = [1, 2, 3, 4, 5, 6, 7, 8]
flux = [0.04535423476987057,
0.34605795394960859,
0.70094774639729907,
0.89675381769455764,
0.97242001951216395,
0.99711765053819645,
0.99965838382174854,
0.99998044756724924]
assert_array_equal(rad, x)
assert_allclose(flux, y, 1e-6)
def test_gauss_center():
"""Check the gaussian center fitting."""
# make a 2d dataset with a gaussian at the center
from astropy.convolution import Gaussian2DKernel
gaussian_2D_kernel = Gaussian2DKernel(10)
plots = Imexamine()
plots.set_data(gaussian_2D_kernel.array)
a, xx, yy, xs, ys = plots.gauss_center(37, 37)
amp = 0.0015915494309189533
xc = 40.0
yc = 40.0
xsig = 10.0
ysig = 10.0
assert_allclose(amp, a, 1e-6)
assert_allclose(xc, xx, 1e-4)
assert_allclose(yc, yy, 1e-4)
assert_allclose(xsig, xs, 0.01)
assert_allclose(ysig, ys, 0.01)
def test_gauss_center():
"""Check the gaussian center fitting."""
# make a 2d dataset with a gaussian at the center
from astropy.convolution import Gaussian2DKernel
gaussian_2D_kernel = Gaussian2DKernel(10)
plots = Imexamine()
plots.set_data(gaussian_2D_kernel.array)
a, xx, yy, xs, ys = plots.gauss_center(37, 37)
amp = 0.0015915494309189533
xc = 40.0
yc = 40.0
xsig = 10.0
ysig = 10.0
assert_allclose(amp, a, 1e-6)
assert_allclose(xc, xx, 1e-4)
assert_allclose(yc, yy, 1e-4)
assert_allclose(xsig, xs, 0.01)
assert_allclose(ysig, ys, 0.01)
def test_curve_of_growth():
"""Test the cog functionality."""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
x, y = plots.curve_of_growth(12, 12, genplot=False)
rad = [1, 2, 3, 4, 5, 6, 7, 8]
flux = [0.04535423476987057,
0.34605795394960859,
0.70094774639729907,
0.89675381769455764,
0.97242001951216395,
0.99711765053819645,
0.99965838382174854,
0.99998044756724924]
assert_array_equal(rad, x)
assert_allclose(flux, y, 1e-6)
def test_gauss_center():
"""Check the gaussian center fitting."""
from astropy.convolution import Gaussian2DKernel
# This creates a 2D normalized gaussian kernal with
# a set amplitude. Guess off-center
amp = 0.0015915494309189533
size = 81.0
sigma = 10.0
gaussian_2D_kernel = Gaussian2DKernel(sigma, x_size=size, y_size=size)
plots = Imexamine()
plots.set_data(gaussian_2D_kernel.array)
a, xx, yy, xs, ys = plots.gauss_center(37, 37)
assert_allclose(amp, a, 1e-6)
assert_allclose(size // 2, xx, 1e-6)
assert_allclose(size // 2, yy, 1e-6)
assert_allclose(sigma, xs, 0.01)
assert_allclose(sigma, ys, 0.01)
def test_curve_of_growth():
"""Test the cog functionality."""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
rad_out, flux_out = plots.curve_of_growth(12, 12, genplot=False)
rads = [1, 2, 3, 4, 5, 6, 7, 8]
flux = []
# Run the aperture phot on this to build up the expected fluxes
plots.aper_phot_pars['genplot'][0] = False
plots.aper_phot_pars['subsky'][0] = False
for rad in rads:
plots.aper_phot_pars['radius'][0] = rad
plots.aper_phot(12, 12)
flux.append(plots.total_flux)
assert_array_equal(rads, rad_out)
assert_allclose(flux, flux_out, 1e-6)
def test_gauss_center():
"""Check the gaussian center fitting."""
from astropy.convolution import Gaussian2DKernel
# This creates a 2D normalized gaussian kernal with
# a set amplitude. Guess off-center
amp = 0.0015915494309189533
size = 81.0
sigma = 10.0
gaussian_2D_kernel = Gaussian2DKernel(sigma, x_size=size, y_size=size)
plots = Imexamine()
plots.set_data(gaussian_2D_kernel.array)
a, xx, yy, xs, ys = plots.gauss_center(37, 37)
assert_allclose(amp, a, 1e-6)
assert_allclose(size // 2, xx, 1e-6)
assert_allclose(size // 2, yy, 1e-6)
assert_allclose(sigma, xs, 0.01)
assert_allclose(sigma, ys, 0.01)
def test_xy_coords(capsys):
"""Make sure xy coords are printed with the correct location and value."""
plots = Imexamine()
plots.set_data(test_data)
out_text = """50 50 3.0"""
plots.show_xy_coords(50, 50)
out, err = capsys.readouterr()
assert (out.strip() == out_text)
def test_column_plot():
"""Check the column plot function."""
plots = Imexamine()
plots.set_data(test_data)
plots.plot_column(50, 50)
f = plt.gca()
xplot, yplot = f.lines[0].get_xydata().T
assert_equal(yplot, test_data[50, :])
plt.close()
def test_line_plot():
"""check the line plot function."""
plots = Imexamine()
plots.set_data(test_data)
plots.plot_line(50, 50)
f = plt.gca()
xplot, yplot = f.lines[0].get_xydata().T
assert_equal(yplot, test_data[:, 50])
plt.close()
def test_curve_of_growth():
"""Test the curve of growth functionality."""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
rad_out, flux_out = plots.curve_of_growth(12, 12, genplot=False)
rads = [1, 2, 3, 4, 5, 6, 7, 8]
flux = []
# Run the aperture phot on this to build up the expected fluxes
plots.aper_phot_pars['genplot'][0] = False
plots.aper_phot_pars['subsky'][0] = False
for rad in rads:
plots.aper_phot_pars['radius'][0] = rad
plots.aper_phot(12, 12)
flux.append(plots.total_flux)
assert_array_equal(rads, rad_out)
assert_allclose(flux, flux_out, 1e-6)
def test_aper_phot(capsys):
"""Make sure aper phot executes and returns expected text."""
out_text = """xc=49.500000 yc=49.500000
x y radius flux mag(zpt=25.00) sky fwhm
49.50 49.50 5 134.73 19.68 1.34 27.54
"""
plots = Imexamine()
plots.set_data(test_data)
plots.aper_phot(50, 50)
out, err = capsys.readouterr()
print(out)
print(out_text)
assert out == out_text
def test_radial_profile_background():
"""Test the radial profile function with background subtraction"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
# check the binned results
plots.radial_profile_pars['pixels'][0] = False
plots.radial_profile_pars['background'][0] = True
x, y = plots.radial_profile(12, 12, genplot=False)
rad = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
flux = [4.535423e-02, 3.007037e-01, 3.548898e-01, 1.958061e-01,
7.566620e-02, 2.469763e-02, 2.540733e-03, 1.518025e-04,
1.083221e-06, 3.605551e-10]
assert_array_equal(rad, x)
assert_allclose(flux, y, 1e-6)
def test_radial_profile_cumulative():
"""Test the radial profile function
without background subtraction
with each pixel integer binned
"""
from astropy.convolution import Gaussian2DKernel
ksize = 25
data = Gaussian2DKernel(1.5, x_size=ksize, y_size=ksize)
xx, yy = np.meshgrid(np.arange(ksize), np.arange(ksize))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
rad_in = rad_in.ravel()
flux_in = data.array.ravel()
indices = np.argsort(rad_in)
rad_in = rad_in[indices]
flux_in = flux_in[indices]
# now bin the radflux like we expect
rad_in = rad_in.astype(np.int)
flux_in = np.bincount(rad_in, flux_in) / np.bincount(rad_in)
rad_in = np.arange(len(flux_in))
assert (data.array[x0, y0] == flux_in[0])
# check the binned results
plots = Imexamine()
plots.set_data(data.array)
plots.radial_profile_pars['pixels'][0] = False
plots.radial_profile_pars['background'][0] = False
plots.radial_profile_pars['clip'][0] = False
rad_out, flux_out = plots.radial_profile(x0, y0, genplot=False)
# The default measurement size is not equal
assert (len(rad_in) >= len(rad_out))
good = [rad_in[i] for i in rad_out if rad_out[i] == rad_in[i]]
assert_array_equal(rad_in[good], rad_out[good])
assert_allclose(flux_in[good], flux_out[good], atol=1e-7)
def test_radial_profile():
"""Test the radial profile function
No background subtraction
individual pixel results used
"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
xx, yy = np.meshgrid(np.arange(25), np.arange(25))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
rad_in = rad_in.ravel()
flux_in = data.array.ravel()
order = np.argsort(rad_in)
rad_in = rad_in[order]
flux_in = flux_in[order]
plots = Imexamine()
plots.set_data(data.array)
plots.radial_profile_pars['pixels'][0] = True
plots.radial_profile_pars['background'][0] = False
plots.radial_profile_pars['clip'][0] = False
rad_out, flux_out = plots.radial_profile(x0, y0, genplot=False)
order2 = np.argsort(rad_out)
rad_out = rad_out[order2]
flux_out = flux_out[order2]
# the radial profile is done on a smaller cutout by default
# and may have a fractional center radius calculation. This
# looks at the first few hundred data points in both arrays
assert (len(rad_out) < len(rad_in))
good = 150
assert_allclose(rad_in[:good], rad_out[:good], atol=1e-14)
assert_allclose(flux_in[:good], flux_out[:good], atol=1e-14)
def __init__(self,
target=None,
path=None,
viewer="ds9",
wait_time=10,
quit_window=True,
port=None):
"""Initialize the imexam control object."""
_possible_viewers = []
if have_xpa:
_possible_viewers.append("ds9")
self._viewer = viewer.lower()
if (self._viewer not in _possible_viewers
or len(_possible_viewers) == 0):
warnings.warn("**Unsupported viewer, check your installed "
"packages**\n")
raise NotImplementedError
# init sets empty data array until we can load or check viewer
self.exam = Imexamine()
if 'ds9' in self._viewer:
self.window = ds9(target=target,
path=path,
wait_time=wait_time,
quit_ds9_on_del=quit_window)
self._event_driven_exam = False # use the imexam loop
# alter the exam.imexam_option_funcs{} here through the viewer code
# if you want to change key+function associations
# self.window._reassign_keys(imexam_dict)
self.logfile = 'imexam_log.txt' # default logfile name
self.log = set_logging() # points to the package logger
self._current_slice = None
self._current_frame = None
def test_radial_profile_cumulative():
"""Test the radial profile function
without background subtraction
with each pixel integer binned
"""
from astropy.convolution import Gaussian2DKernel
ksize = 25
data = Gaussian2DKernel(1.5, x_size=ksize, y_size=ksize)
xx, yy = np.meshgrid(np.arange(ksize), np.arange(ksize))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
rad_in = rad_in.ravel()
flux_in = data.array.ravel()
indices = np.argsort(rad_in)
rad_in = rad_in[indices]
flux_in = flux_in[indices]
# now bin the radflux like we expect
rad_in = rad_in.astype(np.int)
flux_in = np.bincount(rad_in, flux_in) / np.bincount(rad_in)
rad_in = np.arange(len(flux_in))
assert (data.array[x0, y0] == flux_in[0])
# check the binned results
plots = Imexamine()
plots.set_data(data.array)
plots.radial_profile_pars['pixels'][0] = False
plots.radial_profile_pars['background'][0] = False
plots.radial_profile_pars['clip'][0] = False
rad_out, flux_out = plots.radial_profile(x0, y0, genplot=False)
# The default measurement size is not equal
assert (len(rad_in) >= len(rad_out))
good = [rad_in[i] for i in rad_out if rad_out[i] == rad_in[i]]
assert_array_equal(rad_in[good], rad_out[good])
assert_allclose(flux_in[good], flux_out[good], atol=1e-7)
def test_radial_profile():
"""Test the radial profile function
No background subtraction
individual pixel results used
"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
xx, yy = np.meshgrid(np.arange(25), np.arange(25))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
rad_in = rad_in.ravel()
flux_in = data.array.ravel()
order = np.argsort(rad_in)
rad_in = rad_in[order]
flux_in = flux_in[order]
plots = Imexamine()
plots.set_data(data.array)
plots.radial_profile_pars['pixels'][0] = True
plots.radial_profile_pars['background'][0] = False
plots.radial_profile_pars['clip'][0] = False
rad_out, flux_out = plots.radial_profile(x0, y0, genplot=False)
order2 = np.argsort(rad_out)
rad_out = rad_out[order2]
flux_out = flux_out[order2]
# the radial profile is done on a smaller cutout by default
# and may have a fractional center radius calculation. This
# looks at the first few hundred data points in both arrays
assert (len(rad_out) < len(rad_in))
good = 150
assert_allclose(rad_in[:good], rad_out[:good], atol=1e-14)
assert_allclose(flux_in[:good], flux_out[:good], atol=1e-14)
def test_radial_profile_pixels():
"""Test the radial profile function
without background subtraction
with each pixel unsummed
"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
xx, yy = np.meshgrid(np.arange(25), np.arange(25))
x0, y0 = np.where(data.array == data.array.max())
rad_in = np.sqrt((xx - x0)**2 + (yy - y0)**2)
# It's going to crop things down apparently.
plots = Imexamine()
datasize = int(plots.radial_profile_pars["rplot"][0])
icentery = 12
icenterx = 12
rad_in = rad_in[icentery - datasize:icentery + datasize,
icenterx - datasize:icenterx + datasize]
flux_in = data.array[icentery - datasize:icentery + datasize,
icenterx - datasize:icenterx + datasize]
rad_in = rad_in.ravel()
flux_in = flux_in.ravel()
order = np.argsort(rad_in)
rad_in = rad_in[order]
flux_in = flux_in[order]
plots.set_data(data.array)
# check the unbinned results
plots.radial_profile_pars['pixels'][0] = True
out_radius, out_flux = plots.radial_profile(x0, y0, genplot=False)
good = np.where(rad_in <= np.max(out_radius))
rad_in = rad_in[good]
flux_in = flux_in[good]
assert_allclose(rad_in, out_radius, 1e-7)
assert_allclose(flux_in, out_flux, 1e-7)
import pyregion
from pathlib import Path
from imexam.imexamine import Imexamine
from imexam.math_helper import mfwhm, gfwhm
from astropy.io import ascii
test = 0
while test == 0:
try:
filename = Path(input("Enter path to file: "))
rfile = Path(input("enter path to region file: "))
except (KeyError, TypeError, FileNotFoundError):
filename = str(input("perhaps a typo, try again"))
images = sorted(filename.glob('indiir1_20*.fits'))
region_name = sorted(rfile.glob('indiir1_*.coo'))
plots = Imexamine()
for names, stars in zip(images, region_name):
data = fits.getdata(names)
plots.set_data(data)
starlist = ascii.read(stars, data_start=3)
plots.line_fit_pars['func'] = ['Moffat1D']
plots.column_fit_pars['func'] = ['Moffat1D']
results = dict()
yresults = dict()
for star in starlist:
sid, x, y, a4, a5, a6, a7 = star
moff = plots.line_fit(x, y, genplot=False)
ymoff = plots.column_fit(x, y, genplot=False)
# print(moff)
def test_radial_profile_pixels():
"""Test the radial profile function without background subtraction"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
# check the unbinned results
plots.radial_profile_pars['pixels'][0] = True
x, y = plots.radial_profile(12, 12, genplot=False)
rad = [1.00485917e-14, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.41421356e+00,
1.41421356e+00, 1.41421356e+00, 1.41421356e+00,
2.00000000e+00, 2.00000000e+00, 2.00000000e+00,
2.00000000e+00, 2.23606798e+00, 2.23606798e+00,
2.23606798e+00, 2.23606798e+00, 2.23606798e+00,
2.23606798e+00, 2.23606798e+00, 2.23606798e+00,
2.82842712e+00, 2.82842712e+00, 2.82842712e+00,
2.82842712e+00, 3.00000000e+00, 3.00000000e+00,
3.00000000e+00, 3.00000000e+00, 3.16227766e+00,
3.16227766e+00, 3.16227766e+00, 3.16227766e+00,
3.16227766e+00, 3.16227766e+00, 3.16227766e+00,
3.16227766e+00, 3.60555128e+00, 3.60555128e+00,
3.60555128e+00, 3.60555128e+00, 3.60555128e+00,
3.60555128e+00, 3.60555128e+00, 3.60555128e+00,
4.00000000e+00, 4.00000000e+00, 4.00000000e+00,
4.00000000e+00, 4.12310563e+00, 4.12310563e+00,
4.12310563e+00, 4.12310563e+00, 4.12310563e+00,
4.12310563e+00, 4.12310563e+00, 4.12310563e+00,
4.24264069e+00, 4.24264069e+00, 4.24264069e+00,
4.24264069e+00, 4.47213595e+00, 4.47213595e+00,
4.47213595e+00, 4.47213595e+00, 4.47213595e+00,
4.47213595e+00, 4.47213595e+00, 4.47213595e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.09901951e+00, 5.09901951e+00, 5.09901951e+00,
5.09901951e+00, 5.09901951e+00, 5.09901951e+00,
5.09901951e+00, 5.09901951e+00, 5.38516481e+00,
5.38516481e+00, 5.38516481e+00, 5.38516481e+00,
5.38516481e+00, 5.38516481e+00, 5.38516481e+00,
5.38516481e+00, 5.65685425e+00, 5.65685425e+00,
5.65685425e+00, 5.65685425e+00, 5.83095189e+00,
5.83095189e+00, 5.83095189e+00, 5.83095189e+00,
5.83095189e+00, 5.83095189e+00, 5.83095189e+00,
5.83095189e+00, 6.00000000e+00, 6.00000000e+00,
6.00000000e+00, 6.00000000e+00, 6.08276253e+00,
6.08276253e+00, 6.08276253e+00, 6.08276253e+00,
6.08276253e+00, 6.08276253e+00, 6.08276253e+00,
6.08276253e+00, 6.32455532e+00, 6.32455532e+00,
6.32455532e+00, 6.32455532e+00, 6.32455532e+00,
6.32455532e+00, 6.32455532e+00, 6.32455532e+00,
6.40312424e+00, 6.40312424e+00, 6.40312424e+00,
6.40312424e+00, 6.40312424e+00, 6.40312424e+00,
6.40312424e+00, 6.40312424e+00, 6.70820393e+00,
6.70820393e+00, 6.70820393e+00, 6.70820393e+00,
6.70820393e+00, 6.70820393e+00, 6.70820393e+00,
6.70820393e+00, 7.00000000e+00, 7.00000000e+00,
7.07106781e+00, 7.07106781e+00, 7.07106781e+00,
7.07106781e+00, 7.07106781e+00, 7.07106781e+00,
7.07106781e+00, 7.07106781e+00, 7.21110255e+00,
7.21110255e+00, 7.21110255e+00, 7.21110255e+00,
7.21110255e+00, 7.21110255e+00, 7.21110255e+00,
7.21110255e+00, 7.28010989e+00, 7.28010989e+00,
7.28010989e+00, 7.28010989e+00, 7.61577311e+00,
7.61577311e+00, 7.61577311e+00, 7.61577311e+00,
7.81024968e+00, 7.81024968e+00, 7.81024968e+00,
7.81024968e+00, 7.81024968e+00, 7.81024968e+00,
7.81024968e+00, 7.81024968e+00, 8.06225775e+00,
8.06225775e+00, 8.06225775e+00, 8.06225775e+00,
8.48528137e+00, 8.48528137e+00, 8.48528137e+00,
8.48528137e+00, 8.60232527e+00, 8.60232527e+00,
8.60232527e+00, 8.60232527e+00, 9.21954446e+00,
9.21954446e+00, 9.21954446e+00, 9.21954446e+00,
9.89949494e+00]
flux = [1.19552465e-02, 2.32856406e-02, 2.32856406e-02,
3.93558331e-03, 3.93558331e-03, 4.53542348e-02,
7.66546959e-03, 7.66546959e-03, 1.29556643e-03,
2.90802459e-02, 2.90802459e-02, 8.30691786e-04,
8.30691786e-04, 5.66405848e-02, 5.66405848e-02,
9.57301302e-03, 9.57301302e-03, 1.61796667e-03,
1.61796667e-03, 2.73457911e-04, 2.73457911e-04,
7.07355303e-02, 2.02059585e-03, 2.02059585e-03,
5.77193322e-05, 2.32856406e-02, 2.32856406e-02,
1.12421908e-04, 1.12421908e-04, 4.53542348e-02,
4.53542348e-02, 7.66546959e-03, 7.66546959e-03,
2.18967977e-04, 2.18967977e-04, 3.70085038e-05,
3.70085038e-05, 5.66405848e-02, 5.66405848e-02,
1.61796667e-03, 1.61796667e-03, 2.73457911e-04,
2.73457911e-04, 7.81146217e-06, 7.81146217e-06,
1.19552465e-02, 1.19552465e-02, 9.75533570e-06,
9.75533570e-06, 2.32856406e-02, 2.32856406e-02,
3.93558331e-03, 3.93558331e-03, 1.90007994e-05,
1.90007994e-05, 3.21138811e-06, 3.21138811e-06,
4.53542348e-02, 2.18967977e-04, 2.18967977e-04,
1.05716645e-06, 2.90802459e-02, 2.90802459e-02,
8.30691786e-04, 8.30691786e-04, 2.37291269e-05,
2.37291269e-05, 6.77834392e-07, 6.77834392e-07,
2.32856406e-02, 2.32856406e-02, 3.93558331e-03,
3.93558331e-03, 1.12421908e-04, 1.12421908e-04,
1.90007994e-05, 1.90007994e-05, 5.42767351e-07,
5.42767351e-07, 9.17349095e-08, 9.17349095e-08,
7.66546959e-03, 7.66546959e-03, 1.29556643e-03,
1.29556643e-03, 1.05716645e-06, 1.05716645e-06,
1.78675206e-07, 1.78675206e-07, 9.57301302e-03,
9.57301302e-03, 2.73457911e-04, 2.73457911e-04,
1.32024112e-06, 1.32024112e-06, 3.77133487e-08,
3.77133487e-08, 1.19552465e-02, 9.75533570e-06,
9.75533570e-06, 7.96023526e-09, 7.66546959e-03,
7.66546959e-03, 3.70085038e-05, 3.70085038e-05,
1.05716645e-06, 1.05716645e-06, 5.10394673e-09,
5.10394673e-09, 8.30691786e-04, 8.30691786e-04,
1.93626789e-08, 1.93626789e-08, 1.61796667e-03,
1.61796667e-03, 2.73457911e-04, 2.73457911e-04,
3.77133487e-08, 3.77133487e-08, 6.37405811e-09,
6.37405811e-09, 2.02059585e-03, 2.02059585e-03,
5.77193322e-05, 5.77193322e-05, 4.70982729e-08,
4.70982729e-08, 1.34538575e-09, 1.34538575e-09,
3.93558331e-03, 3.93558331e-03, 3.21138811e-06,
3.21138811e-06, 5.42767351e-07, 5.42767351e-07,
4.42891556e-10, 4.42891556e-10, 1.61796667e-03,
1.61796667e-03, 7.81146217e-06, 7.81146217e-06,
3.77133487e-08, 3.77133487e-08, 1.82078162e-10,
1.82078162e-10, 1.12421908e-04, 1.12421908e-04,
1.29556643e-03, 2.18967977e-04, 2.18967977e-04,
3.70085038e-05, 3.70085038e-05, 1.78675206e-07,
1.78675206e-07, 2.46415996e-11, 8.30691786e-04,
8.30691786e-04, 6.77834392e-07, 6.77834392e-07,
1.93626789e-08, 1.93626789e-08, 1.57997104e-11,
1.57997104e-11, 2.73457911e-04, 2.73457911e-04,
7.81146217e-06, 7.81146217e-06, 2.18967977e-04,
2.18967977e-04, 1.05716645e-06, 1.05716645e-06,
2.73457911e-04, 2.73457911e-04, 3.77133487e-08,
3.77133487e-08, 6.37405811e-09, 6.37405811e-09,
8.79064260e-13, 8.79064260e-13, 1.12421908e-04,
1.12421908e-04, 9.17349095e-08, 9.17349095e-08,
5.77193322e-05, 1.34538575e-09, 1.34538575e-09,
3.13597326e-14, 3.70085038e-05, 3.70085038e-05,
5.10394673e-09, 5.10394673e-09, 7.81146217e-06,
7.81146217e-06, 1.82078162e-10, 1.82078162e-10,
1.05716645e-06]
assert_allclose(rad, x, 1e-7)
assert_allclose(flux, y, 1e-7)
def test_radial_profile_pixels():
"""Test the radial profile function without background subtraction"""
from astropy.convolution import Gaussian2DKernel
data = Gaussian2DKernel(1.5, x_size=25, y_size=25)
plots = Imexamine()
plots.set_data(data.array)
# check the unbinned results
plots.radial_profile_pars['pixels'][0] = True
x, y = plots.radial_profile(12, 12, genplot=False)
rad = [1.00485917e-14, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.41421356e+00,
1.41421356e+00, 1.41421356e+00, 1.41421356e+00,
2.00000000e+00, 2.00000000e+00, 2.00000000e+00,
2.00000000e+00, 2.23606798e+00, 2.23606798e+00,
2.23606798e+00, 2.23606798e+00, 2.23606798e+00,
2.23606798e+00, 2.23606798e+00, 2.23606798e+00,
2.82842712e+00, 2.82842712e+00, 2.82842712e+00,
2.82842712e+00, 3.00000000e+00, 3.00000000e+00,
3.00000000e+00, 3.00000000e+00, 3.16227766e+00,
3.16227766e+00, 3.16227766e+00, 3.16227766e+00,
3.16227766e+00, 3.16227766e+00, 3.16227766e+00,
3.16227766e+00, 3.60555128e+00, 3.60555128e+00,
3.60555128e+00, 3.60555128e+00, 3.60555128e+00,
3.60555128e+00, 3.60555128e+00, 3.60555128e+00,
4.00000000e+00, 4.00000000e+00, 4.00000000e+00,
4.00000000e+00, 4.12310563e+00, 4.12310563e+00,
4.12310563e+00, 4.12310563e+00, 4.12310563e+00,
4.12310563e+00, 4.12310563e+00, 4.12310563e+00,
4.24264069e+00, 4.24264069e+00, 4.24264069e+00,
4.24264069e+00, 4.47213595e+00, 4.47213595e+00,
4.47213595e+00, 4.47213595e+00, 4.47213595e+00,
4.47213595e+00, 4.47213595e+00, 4.47213595e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.09901951e+00, 5.09901951e+00, 5.09901951e+00,
5.09901951e+00, 5.09901951e+00, 5.09901951e+00,
5.09901951e+00, 5.09901951e+00, 5.38516481e+00,
5.38516481e+00, 5.38516481e+00, 5.38516481e+00,
5.38516481e+00, 5.38516481e+00, 5.38516481e+00,
5.38516481e+00, 5.65685425e+00, 5.65685425e+00,
5.65685425e+00, 5.65685425e+00, 5.83095189e+00,
5.83095189e+00, 5.83095189e+00, 5.83095189e+00,
5.83095189e+00, 5.83095189e+00, 5.83095189e+00,
5.83095189e+00, 6.00000000e+00, 6.00000000e+00,
6.00000000e+00, 6.00000000e+00, 6.08276253e+00,
6.08276253e+00, 6.08276253e+00, 6.08276253e+00,
6.08276253e+00, 6.08276253e+00, 6.08276253e+00,
6.08276253e+00, 6.32455532e+00, 6.32455532e+00,
6.32455532e+00, 6.32455532e+00, 6.32455532e+00,
6.32455532e+00, 6.32455532e+00, 6.32455532e+00,
6.40312424e+00, 6.40312424e+00, 6.40312424e+00,
6.40312424e+00, 6.40312424e+00, 6.40312424e+00,
6.40312424e+00, 6.40312424e+00, 6.70820393e+00,
6.70820393e+00, 6.70820393e+00, 6.70820393e+00,
6.70820393e+00, 6.70820393e+00, 6.70820393e+00,
6.70820393e+00, 7.00000000e+00, 7.00000000e+00,
7.07106781e+00, 7.07106781e+00, 7.07106781e+00,
7.07106781e+00, 7.07106781e+00, 7.07106781e+00,
7.07106781e+00, 7.07106781e+00, 7.21110255e+00,
7.21110255e+00, 7.21110255e+00, 7.21110255e+00,
7.21110255e+00, 7.21110255e+00, 7.21110255e+00,
7.21110255e+00, 7.28010989e+00, 7.28010989e+00,
7.28010989e+00, 7.28010989e+00, 7.61577311e+00,
7.61577311e+00, 7.61577311e+00, 7.61577311e+00,
7.81024968e+00, 7.81024968e+00, 7.81024968e+00,
7.81024968e+00, 7.81024968e+00, 7.81024968e+00,
7.81024968e+00, 7.81024968e+00, 8.06225775e+00,
8.06225775e+00, 8.06225775e+00, 8.06225775e+00,
8.48528137e+00, 8.48528137e+00, 8.48528137e+00,
8.48528137e+00, 8.60232527e+00, 8.60232527e+00,
8.60232527e+00, 8.60232527e+00, 9.21954446e+00,
9.21954446e+00, 9.21954446e+00, 9.21954446e+00,
9.89949494e+00]
flux = [1.19552465e-02, 2.32856406e-02, 2.32856406e-02,
3.93558331e-03, 3.93558331e-03, 4.53542348e-02,
7.66546959e-03, 7.66546959e-03, 1.29556643e-03,
2.90802459e-02, 2.90802459e-02, 8.30691786e-04,
8.30691786e-04, 5.66405848e-02, 5.66405848e-02,
9.57301302e-03, 9.57301302e-03, 1.61796667e-03,
1.61796667e-03, 2.73457911e-04, 2.73457911e-04,
7.07355303e-02, 2.02059585e-03, 2.02059585e-03,
5.77193322e-05, 2.32856406e-02, 2.32856406e-02,
1.12421908e-04, 1.12421908e-04, 4.53542348e-02,
4.53542348e-02, 7.66546959e-03, 7.66546959e-03,
2.18967977e-04, 2.18967977e-04, 3.70085038e-05,
3.70085038e-05, 5.66405848e-02, 5.66405848e-02,
1.61796667e-03, 1.61796667e-03, 2.73457911e-04,
2.73457911e-04, 7.81146217e-06, 7.81146217e-06,
1.19552465e-02, 1.19552465e-02, 9.75533570e-06,
9.75533570e-06, 2.32856406e-02, 2.32856406e-02,
3.93558331e-03, 3.93558331e-03, 1.90007994e-05,
1.90007994e-05, 3.21138811e-06, 3.21138811e-06,
4.53542348e-02, 2.18967977e-04, 2.18967977e-04,
1.05716645e-06, 2.90802459e-02, 2.90802459e-02,
8.30691786e-04, 8.30691786e-04, 2.37291269e-05,
2.37291269e-05, 6.77834392e-07, 6.77834392e-07,
2.32856406e-02, 2.32856406e-02, 3.93558331e-03,
3.93558331e-03, 1.12421908e-04, 1.12421908e-04,
1.90007994e-05, 1.90007994e-05, 5.42767351e-07,
5.42767351e-07, 9.17349095e-08, 9.17349095e-08,
7.66546959e-03, 7.66546959e-03, 1.29556643e-03,
1.29556643e-03, 1.05716645e-06, 1.05716645e-06,
1.78675206e-07, 1.78675206e-07, 9.57301302e-03,
9.57301302e-03, 2.73457911e-04, 2.73457911e-04,
1.32024112e-06, 1.32024112e-06, 3.77133487e-08,
3.77133487e-08, 1.19552465e-02, 9.75533570e-06,
9.75533570e-06, 7.96023526e-09, 7.66546959e-03,
7.66546959e-03, 3.70085038e-05, 3.70085038e-05,
1.05716645e-06, 1.05716645e-06, 5.10394673e-09,
5.10394673e-09, 8.30691786e-04, 8.30691786e-04,
1.93626789e-08, 1.93626789e-08, 1.61796667e-03,
1.61796667e-03, 2.73457911e-04, 2.73457911e-04,
3.77133487e-08, 3.77133487e-08, 6.37405811e-09,
6.37405811e-09, 2.02059585e-03, 2.02059585e-03,
5.77193322e-05, 5.77193322e-05, 4.70982729e-08,
4.70982729e-08, 1.34538575e-09, 1.34538575e-09,
3.93558331e-03, 3.93558331e-03, 3.21138811e-06,
3.21138811e-06, 5.42767351e-07, 5.42767351e-07,
4.42891556e-10, 4.42891556e-10, 1.61796667e-03,
1.61796667e-03, 7.81146217e-06, 7.81146217e-06,
3.77133487e-08, 3.77133487e-08, 1.82078162e-10,
1.82078162e-10, 1.12421908e-04, 1.12421908e-04,
1.29556643e-03, 2.18967977e-04, 2.18967977e-04,
3.70085038e-05, 3.70085038e-05, 1.78675206e-07,
1.78675206e-07, 2.46415996e-11, 8.30691786e-04,
8.30691786e-04, 6.77834392e-07, 6.77834392e-07,
1.93626789e-08, 1.93626789e-08, 1.57997104e-11,
1.57997104e-11, 2.73457911e-04, 2.73457911e-04,
7.81146217e-06, 7.81146217e-06, 2.18967977e-04,
2.18967977e-04, 1.05716645e-06, 1.05716645e-06,
2.73457911e-04, 2.73457911e-04, 3.77133487e-08,
3.77133487e-08, 6.37405811e-09, 6.37405811e-09,
8.79064260e-13, 8.79064260e-13, 1.12421908e-04,
1.12421908e-04, 9.17349095e-08, 9.17349095e-08,
5.77193322e-05, 1.34538575e-09, 1.34538575e-09,
3.13597326e-14, 3.70085038e-05, 3.70085038e-05,
5.10394673e-09, 5.10394673e-09, 7.81146217e-06,
7.81146217e-06, 1.82078162e-10, 1.82078162e-10,
1.05716645e-06]
assert_allclose(rad, x, 1e-7)
assert_allclose(flux, y, 1e-7)