def simple_norm(data, stretch='linear', power=1.0, asinh_a=0.1, log_a=1000,
min_cut=None, max_cut=None, min_percent=None, max_percent=None,
percent=None, clip=True):
if percent is not None:
interval = PercentileInterval(percent)
elif min_percent is not None or max_percent is not None:
interval = AsymmetricPercentileInterval(min_percent or 0.,
max_percent or 100.)
elif min_cut is not None or max_cut is not None:
interval = ManualInterval(min_cut, max_cut)
else:
interval = MinMaxInterval()
if stretch == 'linear':
stretch = LinearStretch()
elif stretch == 'sqrt':
stretch = SqrtStretch()
elif stretch == 'power':
stretch = PowerStretch(power)
elif stretch == 'log':
stretch = LogStretch(log_a)
elif stretch == 'asinh':
stretch = AsinhStretch(asinh_a)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
vmin, vmax = interval.get_limits(data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip)
def test_invalid_data(self):
data = np.arange(25.).reshape((5, 5))
data[2, 2] = np.nan
data[1, 2] = np.inf
percent = 85.0
interval = PercentileInterval(percent)
# initialized without data
norm = ImageNormalize(interval=interval)
norm(data) # sets vmin/vmax
assert_equal((norm.vmin, norm.vmax), (1.65, 22.35))
# initialized with data
norm2 = ImageNormalize(data, interval=interval)
assert_equal((norm2.vmin, norm2.vmax), (norm.vmin, norm.vmax))
norm3 = simple_norm(data, 'linear', percent=percent)
assert_equal((norm3.vmin, norm3.vmax), (norm.vmin, norm.vmax))
assert_allclose(norm(data), norm2(data))
assert_allclose(norm(data), norm3(data))
norm4 = ImageNormalize()
norm4(data) # sets vmin/vmax
assert_equal((norm4.vmin, norm4.vmax), (0, 24))
norm5 = ImageNormalize(data)
assert_equal((norm5.vmin, norm5.vmax), (norm4.vmin, norm4.vmax))
def showNorm(imaOrCcd, **kwargs):
from astropy.visualization import imshow_norm, SqrtStretch
from astropy.visualization.mpl_normalize import PercentileInterval
from astropy.nddata import CCDData
plt.clf()
fig = plt.gcf()
if isinstance(imaOrCcd, CCDData):
arr = imaOrCcd.data
wcs = imaOrCcd.wcs
if wcs is None:
ax = plt.subplot()
else:
ax = plt.subplot(projection=wcs)
ax.coords.grid(True, color='white', ls='solid')
else:
arr = imaOrCcd
ax = plt.subplot()
if 'interval' not in kwargs:
kwargs['interval'] = PercentileInterval(99.7)
if 'stretch' not in kwargs:
kwargs['stretch'] = SqrtStretch()
if 'origin' not in kwargs:
kwargs['origin'] = 'lower'
im, _ = imshow_norm(arr, ax=ax, **kwargs)
cb = fig.colorbar(im)
cb.ax.tick_params(labelsize=11)
def showStarsFromIRAFTable(ima,
table,
color,
perc_interval=98,
aperture_radius=7):
from photutils.aperture.circle import CircularAperture
positions = (table['xcentroid'], table['ycentroid'])
apertures = CircularAperture(positions, r=aperture_radius)
showNorm(ima, interval=PercentileInterval(perc_interval))
apertures.plot(color=color)
def simple_norm(data,
stretch='linear',
power=1.0,
asinh_a=0.1,
log_a=1000,
min_cut=None,
max_cut=None,
min_percent=None,
max_percent=None,
percent=None,
clip=True):
if percent is not None:
interval = PercentileInterval(percent)
elif min_percent is not None or max_percent is not None:
interval = AsymmetricPercentileInterval(min_percent or 0., max_percent
or 100.)
elif min_cut is not None or max_cut is not None:
interval = ManualInterval(min_cut, max_cut)
else:
interval = MinMaxInterval()
if stretch == 'linear':
stretch = LinearStretch()
elif stretch == 'sqrt':
stretch = SqrtStretch()
elif stretch == 'power':
stretch = PowerStretch(power)
elif stretch == 'log':
stretch = LogStretch(log_a)
elif stretch == 'asinh':
stretch = AsinhStretch(asinh_a)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
vmin, vmax = interval.get_limits(data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip)
def test_percentile(self):
interval = PercentileInterval(62.2)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -4.88)
np.testing.assert_allclose(vmax, 44.88)
def test_percentile(self):
interval = PercentileInterval(62.2)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -4.88)
np.testing.assert_allclose(vmax, 44.88)