def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid=None, exponent=2):
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
vmin = vmin_auto
if vmax is None:
vmax = vmax_auto
normalizer = simple_norm(image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid=None, exponent=2):
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
log.info("vmin = %10.3e (auto)" % vmin_auto)
vmin = vmin_auto
else:
log.info("vmin = %10.3e" % vmin)
if vmax is None:
log.info("vmax = %10.3e (auto)" % vmax_auto)
vmax = vmax_auto
else:
log.info("vmax = %10.3e" % vmax)
if stretch == 'arcsinh':
stretch = 'asinh'
normalizer = simple_norm(image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
def build_image(id_,
set_,
bands=['EUC_VIS', 'EUC_H', 'EUC_J', 'EUC_Y'],
img_size=200,
scale=100,
clip=True):
tables = []
data = np.empty((img_size, img_size, len(bands)))
for i, band in enumerate(bands):
fname = get_image_filename_from_id(id_, band, set_)
try:
tables.append(fits.open(fname))
except FileNotFoundError as fe:
raise
if band != 'EUC_VIS':
band_data, data_footprint = reproject_interp(
tables[i][0], tables[0][0].header)
else:
band_data = tables[0][0].data
band_data[np.isnan(band_data)] = 0.
if clip:
interval = AsymmetricPercentileInterval(0.25,
99.75,
n_samples=100000)
vmin, vmax = interval.get_limits(band_data)
stretch = MinMaxInterval() + LogStretch()
data[:, :, i] = stretch(
((np.clip(band_data, -vmin * 0.7, vmax)) / (vmax)))
else:
stretch = LogStretch() + MinMaxInterval()
data[:, :, i] = stretch(band_data)
for t in tables:
t.close()
return data.astype(np.float32)
def stretchImage(self, image=None):
"""
stretchImage take the actual image and calculated norm based on the min, max
derived from interval which is calculated with AsymmetricPercentileInterval.
:param image: image
:return: norm for plot
"""
if image is None:
return None
values = self.stretchValues[self.ui.stretch.currentText()]
interval = AsymmetricPercentileInterval(*values)
vmin, vmax = interval.get_limits(image)
# cutout the noise
delta = vmax - vmin
vmin = min(vmin + delta * 0.01, vmax)
norm = ImageNormalize(image,
vmin=vmin,
vmax=vmax,
stretch=SqrtStretch(),
)
return norm, vmin, vmax
def make_thumbnail(a, ttype, ztftype):
cutout_data = a[f"cutout{ztftype}"]["stampData"]
with gzip.open(io.BytesIO(cutout_data), "rb") as f:
with fits.open(io.BytesIO(f.read())) as hdu:
# header = hdu[0].header
data_flipped_y = np.flipud(hdu[0].data)
# fixme: png, switch to fits eventually
buff = io.BytesIO()
plt.close("all")
fig = plt.figure()
fig.set_size_inches(4, 4, forward=False)
ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
ax.set_axis_off()
fig.add_axes(ax)
# replace nans with median:
img = np.array(data_flipped_y)
# replace dubiously large values
xl = np.greater(np.abs(img), 1e20, where=~np.isnan(img))
if img[xl].any():
img[xl] = np.nan
if np.isnan(img).any():
median = float(np.nanmean(img.flatten()))
img = np.nan_to_num(img, nan=median)
norm = ImageNormalize(
img,
stretch=LinearStretch() if ztftype == "Difference" else LogStretch())
img_norm = norm(img)
normalizer = AsymmetricPercentileInterval(lower_percentile=1,
upper_percentile=100)
vmin, vmax = normalizer.get_limits(img_norm)
ax.imshow(img_norm, cmap="bone", origin="lower", vmin=vmin, vmax=vmax)
plt.savefig(buff, dpi=42)
buff.seek(0)
plt.close("all")
thumb = {
"obj_id": a["objectId"],
"data": base64.b64encode(buff.read()).decode("utf-8"),
"ttype": ttype,
}
return thumb
def preprocess_band(image, clip=True):
"""Do clip preprocessing of a single band.
param: image (ndarray): 2D array containing a single band's data.
param: clip (bool): Whether or not to do clip preprocessing. if False log-stretches the image.
Defaults to True.
returns: newimage (ndarray): Preprocessed 2D array."""
image[np.isnan(image)] = 0.
if clip:
interval = AsymmetricPercentileInterval(0.25, 99.75, n_samples=100000)
vmin, vmax = interval.get_limits(image)
stretch = MinMaxInterval() + LogStretch()
newimage = stretch(((np.clip(image, -vmin * 0.7, vmax)) / (vmax)))
else:
stretch = LogStretch() + MinMaxInterval()
newimage = stretch(image)
return newimage
def _data_stretch(
image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid: float = 10, exponent=2):
""" Hacked from aplpy
"""
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
#log.info("vmin = %10.3e (auto)" % vmin_auto)
vmin = vmin_auto
else:
pass
#log.info("vmin = %10.3e" % vmin)
if vmax is None:
#log.info("vmax = %10.3e (auto)" % vmax_auto)
vmax = vmax_auto
else:
pass
#log.info("vmax = %10.3e" % vmax)
if stretch == 'arcsinh':
stretch = 'asinh'
normalizer = simple_norm(
image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
