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()
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)
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)
results[x] = mfwhm(alpha=moff.alpha_0, gamma=moff.gamma_0)
yresults[y] = mfwhm(alpha=ymoff.alpha_0, gamma=ymoff.gamma_0)
# gresults[x] = gfwhm(moff.stddev_0)[0]
# gyresults[y] = gfwhm(ymoff.stddev_0)[1]
print(names, np.median(list(results.values())),
np.median(list(yresults.values())))
# plt.figure()
# plt.subplot(121)
# plt.hist(yresults.values(), bins='auto', alpha=.7, range=(3.,7.5))
# plt.hist(results.values(), bins='auto', alpha=.7, range=(3.,7.5))
# plt.subplot(122)
# plt.hist(gyresults.values(), bins='auto', alpha=.7, range=(3.,7.5))
# plt.hist(gresults.values(), bins='auto', alpha=.7, range=(3.,7.5))
