def test_1d_mean(self):
"""Test 1D array with func=np.mean."""
data = np.arange(4)
block_size = 2.
expected = block_reduce(data, block_size, func=np.sum) / block_size
result_mean = block_reduce(data, block_size, func=np.mean)
assert np.all(result_mean == expected)
def test_1d_mean(self):
"""Test 1D array with func=np.mean."""
data = np.arange(4)
block_size = 2.
expected = block_reduce(data, block_size, func=np.sum) / block_size
result_mean = block_reduce(data, block_size, func=np.mean)
assert np.all(result_mean == expected)
def test_2d_mean(self):
"""Test 2D array with func=np.mean."""
data = np.arange(4).reshape(2, 2)
block_size = 2.
expected = (block_reduce(data, block_size, func=np.sum) /
block_size**2)
result = block_reduce(data, block_size, func=np.mean)
assert np.all(result == expected)
def test_2d_mean(self):
"""Test 2D array with func=np.mean."""
data = np.arange(4).reshape(2, 2)
block_size = 2.
expected = (block_reduce(data, block_size, func=np.sum) /
block_size**2)
result = block_reduce(data, block_size, func=np.mean)
assert np.all(result == expected)
def test_2d_trim(self):
"""
Test trimming of 2D array when size is not perfectly divisible
by block_size.
"""
data1 = np.arange(15).reshape(5, 3)
result1 = block_reduce(data1, 2)
data2 = data1[0:4, 0:2]
result2 = block_reduce(data2, 2)
assert np.all(result1 == result2)
def test_2d_trim(self):
"""
Test trimming of 2D array when size is not perfectly divisible
by block_size.
"""
data1 = np.arange(15).reshape(5, 3)
result1 = block_reduce(data1, 2)
data2 = data1[0:4, 0:2]
result2 = block_reduce(data2, 2)
assert np.all(result1 == result2)
def Percentile_resolution(mapa, L, rho, PP, weights='None'):
N = len(mapa)
dx = L / N
res = list((10**np.linspace(0, np.log10(N), 100) + 1e-5))
res.sort()
res = np.array(res).astype(int)
res = np.array(list(set(res)))
res.sort()
res = np.array(list(set((N / res).astype(int))))
res.sort()
Per = np.zeros_like(res, dtype=float)
if weights == 'None':
for k, R in enumerate(res):
fake = block_reduce(mapa, R, func=np.sum)
N1 = len(fake)
radial = radial_map(fake)
fake = fake.ravel()
radial = radial.ravel() * L / N1
try:
fake = fake[radial < rho]
Per[k] = np.percentile(fake, PP)
except:
Per[k] = 0
else:
for k, R in enumerate(res):
fake = block_reduce(mapa, R, func=np.sum)
fake2 = block_reduce(mapa, R, func=np.sum)
N1 = len(fake)
radial = radial_map(fake)
fake = fake.ravel()
fake2 = fake2.ravel()
radial = radial.ravel() * L / N1
try:
field = fake[radial < rho]
mass = fake2[radial < rho]
mass = mass[field.argsort()]
field = field[field.argsort()]
field = np.insert(field, 0, field[-1])
mass = np.insert(mass, 0, 0)
mass = np.cumsum(mass)
mass /= mass[-1]
fm = interp1d(mass, field)
Per[k] = fm(PP * 0.01)
except:
Per[k] = 0
return res, Per, dx
def Circulation_block_example(name):
print(name)
temp=name.split('/')[-1]
temp=temp.split('.npy')[0]
mapa=np.load(name)
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
for R in [res[0]]:
if os.path.isfile('Difussion/Block-'+temp+'-%05d'%R):
print('File already exists') #Skip code if file already exists
else:
original=np.load(name)
fake=block_reduce(original, R, func=np.sum)
del original
Nf=len(fake)
tabla=Table()
tabla['circulation'] = Column(np.array(fake.ravel()), description='vorticity')
tabla.write('Difussion/Block-'+temp+'-%05d' %R,path='data',format='hdf5')
del tabla
return True
def Circulation_sigma(mapa):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
Negative=np.zeros_like(res,dtype=float)
L=np.zeros_like(res,dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
N1=len(fake)
L[k]=N1
radial=radial_map(fake)
fake=fake.ravel()
radial=radial.ravel()*4.0e4/N1
try:
fake=fake[radial<1.5e4]
Negative[k]=np.percentile(fake,84)-np.percentile(fake,16)
except:
Negative[k]=0
Negative[np.isnan(Negative)]=0
return res, Negative, L
def Circulation_Negative(mapa):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
Negative=np.zeros_like(res,dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
N1=len(fake)
radial=radial_map(fake)
fake=fake.ravel()
radial=radial.ravel()*4.0e4/N1
try:
fake=fake[radial<1.5e4]
Negative[k]=len(fake[fake<0])/len(fake)
except:
Negative[k]=0
return res, Negative
def Circulation_Percentiles(mapa):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
P16=np.zeros_like(res,dtype=float)
P25=np.zeros_like(res,dtype=float)
P50=np.zeros_like(res,dtype=float)
P75=np.zeros_like(res,dtype=float)
P84=np.zeros_like(res,dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
fake=fake.ravel()/R**2
P16[k]=np.percentile(fake,16)
P25[k]=np.percentile(fake,25)
P50[k]=np.percentile(fake,50)
P75[k]=np.percentile(fake,75)
P84[k]=np.percentile(fake,84)
return res,P16,P25,P50,P75,P84
def set_data(self, label, data, inplace_ok=False):
if 'clim' not in self.volumes[label]:
raise ValueError("set_clim should be called before set_data")
# Avoid adding the same data again
if 'data' in self.volumes[
label] and self.volumes[label]['data'] is data:
return
# Since outside this class we sometimes need to already copy the data
# before passing it here, we allow the caller to specify inplace_ok=True
# which means that it's ok to do the limits scaling in-place to avoid
# another copy.
if inplace_ok and data.dtype != np.float32:
raise TypeError('data should be float32 if inplace_ok is set')
# VisPy can't handle dimensions larger than 2048 so we need to reduce
# the array on-the-fly if needed
shape = np.asarray(data.shape)
if np.any(shape > 2048):
if self._initial_shape:
self._block_size = np.ceil(shape / 2048).astype(int)
data = block_reduce(data, self._block_size, func=np.mean)
self.volumes[label]['data'] = data
self.volumes[label]['inplace_ok'] = inplace_ok
self._update_scaled_data(label)
def Percentile_profiles(mapa,PP):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
N1=len(res)
N2=len(PP)
Per=np.zeros((N1,N2),dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)/R**2
N1=len(fake)
fake=fake.ravel()
for j in range(N2):
try:
Per[k][j]=np.percentile(fake,PP[j])
except:
Per[k][j]=0
return res, Per
def ndarray_to_png(x, min_percent=20, max_percent=99.5):
shape = np.array(x.shape)
# Reverse order for reasons I do not understand...
shape = shape[::-1]
if len(shape) != 2:
return
width = 600 # pixels
downsample = (shape[0] // width) + 1
if downsample > 1:
x = block_reduce(x,
block_size=(downsample, downsample))
norm = simple_norm(x,
min_percent=min_percent,
max_percent=max_percent,
clip=True)
x = norm(x)
# Replace NaNs with black pixels
x = np.nan_to_num(x)
img_buffer = BytesIO()
mimg.imsave(img_buffer, x, format='png', cmap='gray')
return img_buffer.getvalue()
def Negative_profile(mapa):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
Negative=np.zeros_like(res,dtype=float)
Total=np.zeros_like(res,dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
N1=len(fake)
fake=fake.ravel()
try:
Negative[k]=len(fake[fake<0])/len(fake)
except:
Negative[k]=0
Total[k]=len(fake)
return res, Negative,Total
def Percentile_profile(mapa,PP):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
Per=np.zeros_like(res,dtype=float)
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
N1=len(fake)
fake=fake.ravel()
try:
Per[k]=np.percentile(fake,PP)
except:
Per[k]=0
return res, Per
def Sum_resolution(mapa, L, rho):
N = len(mapa)
res = list((10**np.linspace(0, np.log10(N), 100) + 1e-5))
res.sort()
res = np.array(res).astype(int)
res = np.array(list(set(res)))
res.sort()
dx = L / N
res = np.array(list(set((N / res).astype(int))))
res.sort()
Sum = np.zeros_like(res, dtype=float)
for k, R in enumerate(res):
fake = block_reduce(mapa, R, func=np.sum)
N1 = len(fake)
radial = radial_map(fake)
fake = fake.ravel()
radial = radial.ravel() * L / N1
try:
fake = fake[radial < rho]
Sum[k] = np.abs(fake).sum()
except:
Sum[k] = 0
return res, Sum
def set_data(self, label, data):
if 'clim' not in self.volumes[label]:
raise ValueError("set_clim should be called before set_data")
# Get rid of NaN values
data = np.nan_to_num(data)
# VisPy can't handle dimensions larger than 2048 so we need to reduce
# the array on-the-fly if needed
shape = np.asarray(data.shape)
if np.any(shape > 2048):
if self._initial_shape:
self._block_size = np.ceil(shape / 2048).astype(int)
data = block_reduce(data, self._block_size, func=np.mean)
self.volumes[label]['data'] = data
self._update_scaled_data(label)
def set_data(self, label, data):
if 'clim' not in self.volumes[label]:
raise ValueError("set_clim should be called before set_data")
# Get rid of NaN values
data = np.nan_to_num(data)
# VisPy can't handle dimensions larger than 2048 so we need to reduce
# the array on-the-fly if needed
shape = np.asarray(data.shape)
if np.any(shape > 2048):
if self._initial_shape:
self._block_size = np.ceil(shape / 2048).astype(int)
data = block_reduce(data, self._block_size, func=np.mean)
self.volumes[label]['data'] = data
self._update_scaled_data(label)
def ndarray_to_png(x, min_percent=20, max_percent=99.5):
shape = np.array(x.shape)
# Reverse order for reasons I do not understand...
shape = shape[::-1]
if len(shape) != 2:
return
width = 600 # pixels
downsample = (shape[0] // width) + 1
scaled_data = scale_image(x,
min_percent=min_percent,
max_percent=max_percent)
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
img_buffer = BytesIO()
mimg.imsave(img_buffer, scaled_data, format='png', cmap='gray')
return img_buffer.getvalue()
def ndarray_to_png(x, min_percent=20, max_percent=99.5):
shape = np.array(x.shape)
# Reverse order for reasons I do not understand...
shape = shape[::-1]
if len(shape) != 2:
return
width = 600 # pixels
downsample = (shape[0] // width) + 1
if downsample > 1:
x = block_reduce(x,
block_size=(downsample, downsample))
norm = simple_norm(x,
min_percent=min_percent,
max_percent=max_percent)
img_buffer = BytesIO()
mimg.imsave(img_buffer, norm(x), format='png', cmap='gray')
return img_buffer.getvalue()
def Block_Negative_radii(mapa, Nbins):
N = len(mapa)
res = list((10**np.linspace(0, np.log10(N), 100) + 1e-5))
res.sort()
res = np.array(res).astype(int)
res = np.array(list(set(res)))
res.sort()
res = np.array(list(set((N / res).astype(int))))
res.sort()
radial = radial_map(mapa)
radial = radial.ravel()
if Nbins > 1:
#redges = np.linspace(0,0.5*N,Nbins)
#redges = np.insert(redges,len(redges),radial.max())
redges = np.linspace(0, 0.5 * N * 0.75, Nbins + 1)
else:
#redges=np.array([0,radial.max()])
redges = np.array([0, 0.5 * N * 0.75])
rcen = 0.5 * (redges[:-1] + redges[1:])
Negative = np.zeros((Nbins, len(res)))
for k, R in enumerate(res):
fake = block_reduce(mapa, R, func=np.sum)
N1 = len(fake)
radial = radial_map(fake)
fake = fake.ravel()
radial = radial.ravel() * N / N1
for j in range(Nbins):
ring = (redges[j] < radial) & (redges[j + 1] > radial)
temp = fake[ring]
try:
Negative[j][k] = len(temp[temp < 0]) / len(temp)
except:
Negative[j][k] = 0.0
return res, rcen, Negative, redges
def sigma_resolution(mapa):
N=len(mapa)
res=list((10**np.linspace(0,np.log10(N),100)+1e-5))
res.sort()
res=np.array(res).astype(int)
res=np.array(list(set(res)))
res.sort()
res=np.array(list(set((N/res).astype(int))))
res.sort()
Sig=np.zeros_like(res,dtype=float)
eta=2*0.6744897501
for k,R in enumerate(res):
fake=block_reduce(mapa, R, func=np.sum)
fake=fake.ravel()
try:
#Sig[k]=(np.percentile(fake,75)-np.percentile(fake,25))/eta
Sig[k]=np.std(fake)
except:
Sig[k]=0
return res, Sig
def test_2d(self):
"""Test 2D array."""
data = np.arange(4).reshape(2, 2)
expected = np.array([[6]])
result = block_reduce(data, 2)
assert np.all(result == expected)
def test_block_size_len(self):
"""Test block_size length."""
data = np.ones((2, 2))
with pytest.raises(ValueError):
block_reduce(data, (2, 2, 2))
def add_noise(infile,
rms,
mean=0,
nu=0,
outfile='',
bin_img=1,
bin_spec=1,
flip_lr=False,
flip_ud=False,
log_scale=False,
overwrite=False,
verbose=False):
"""
Function used to add gaussian noise to an image.
rms must be given in K
Noise can be added either to single images or cubes.
"""
from astropy.nddata.utils import block_reduce
fname = sys._getframe().f_code.co_name
start_time = time.time()
# Detects whether input data comes from a file or an array
if type(infile) == str:
input_from_file = True
header = fits.getheader(infile)
data = fits.getdata(infile)
elif isinstance(infile, (list, np.ndarray)):
input_from_file = False
data = np.array(infile)
# Ensure data has at least one dimension
data = np.array(data, ndmin=1)
if bin_img < 1:
bin_img = 1
if bin_spec < 1:
bin_spec = 1
# Rescale header elements by binning factors
if input_from_file:
header['CDELT1'] *= bin_img
header['CRPIX1'] /= bin_img
header['CDELT2'] *= bin_img
header['CRPIX2'] /= bin_img
if 'CDELT3' in header:
header['CDELT3'] *= bin_spec
header['CRPIX3'] /= bin_spec
# Bin the spectrum and image if required
if bin_spec > 1 or bin_img > 1:
print_(f"Original cube shape: {np.shape(data)}",
fname,
verbose=verbose)
if data.ndim == 3:
# bin spec
if bin_spec > 1:
data = block_reduce(data, [bin_spec, 1, 1], func=np.nanmean)
if bin_img > 1:
data = block_reduce(data, [1, bin_img, bin_img],
func=np.nanmean)
else:
raise Exception(
"[!] Input data does not have three dimensions, impossible to detect spectral axis."
)
print_(f"Binned cube shape: {np.shape(data)}", fname, verbose=verbose)
# Validate rms parameter type
if not isinstance(rms, (tuple)):
raise ValueError(
'Not a valid type for rms. Must be a tuple (val, "unit").')
if rms[1].upper() != 'K':
raise ValueError("rms must be in kelvin. Example: rms=(1,'K').")
print_("Adding noise level of: %e [K] " % (rms[0]), fname, verbose=verbose)
noisy_data = []
print_("Adding Gaussian noise ...", fname, verbose=verbose)
# Loop over the cube
for idx, channel in enumerate(data):
# Add gaussian noise to the image
noise = np.random.normal(mean, rms[0], np.shape(channel))
c = channel + noise
# Flip image
if flip_lr:
c = np.fliplr(c)
if flip_ud:
c = np.flipud(c)
if log_scale:
c = np.log10(c)
noisy_data.append(c)
# Write data to fits file if required
write_fits(outfile, noisy_data, header, overwrite, fname, verbose)
# Print the time taken by the function
elapsed_time(time.time() - start_time, fname, verbose)
return np.array(noisy_data)
def lacosmic(data,
contrast,
cr_threshold,
neighbor_threshold,
error=None,
mask=None,
background=None,
effective_gain=None,
readnoise=None,
maxiter=4,
border_mode='mirror'):
"""
Remove cosmic rays from an astronomical image using the `L.A.Cosmic
<http://www.astro.yale.edu/dokkum/lacosmic/>`_ algorithm. The
algorithm is based on Laplacian edge detection and is described in
`PASP 113, 1420 (2001)`_.
.. _PASP 113, 1420 (2001):
http://adsabs.harvard.edu/abs/2001PASP..113.1420V
Parameters
----------
data : array_like
The 2D array of the image.
contrast : float
Contrast threshold between the Laplacian image and the
fine-structure image. If your image is critically sampled, use
a value around 2. If your image is undersampled (e.g. HST
data), a value of 4 or 5 (or more) is more appropriate. If your
image is oversampled, use a value between 1 and 2. For details,
please see `PASP 113, 1420 (2001)`_, which calls this parameter
:math:`f_{\mbox{lim}}`. In particular, Figure 4 shows the
approximate relationship between the ``contrast`` parameter and
the pixel full-width half-maximum of stars in your image.
cr_threshold : float
The Laplacian signal-to-noise ratio threshold for cosmic-ray
detection.
neighbor_threshold : float
The Laplacian signal-to-noise ratio threshold for detection of
cosmic rays in pixels neighboring the initially-identified
cosmic rays.
error : array_like, optional
The pixel-wise Gaussian 1-sigma errors of the input ``data``.
If ``error`` is not input, then ``effective_gain`` and
``readnoise`` will be used to construct an approximate model of
the ``error``. If ``error`` is input, it will override the
``effective_gain`` and ``readnoise`` parameters. ``error`` must
have the same shape as ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when identifying cosmic rays. It is
highly recommended that saturated stars be included in ``mask``.
background : float or array_like, optional
The background level previously subtracted from the input
``data``. ``background`` may either be a scalar value or a 2D
image with the same shape as the input ``data``. If the input
``data`` has not been background-subtracted, then set
``background=None`` (default).
effective_gain : float, array-like, optional
Ratio of counts (e.g., electrons or photons) to the units of
``data``. For example, if your input ``data`` are in units of
ADU, then ``effective_gain`` should represent electrons/ADU. If
your input ``data`` are in units of electrons/s then
``effective_gain`` should be the exposure time (or an exposure
time map). ``effective_gain`` and ``readnoise`` must be
specified if ``error`` is not input.
readnoise : float, optional
The read noise (in electrons) in the input ``data``.
``effective_gain`` and ``readnoise`` must be specified if
``error`` is not input.
maxiter : float, optional
The maximum number of iterations. The default is 4. The
routine will automatically exit if no additional cosmic rays are
identified. If the routine is still identifying cosmic rays
after four iterations, then you are likely digging into sources
(e.g. saturated stars) and/or the noise. In that case, try
inputing a ``mask`` or increasing the value of ``cr_threshold``.
border_mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
The mode in which the array borders are handled during
convolution and median filtering. For 'constant', the value is
0. The default is 'mirror', which matches the original
L.A.Cosmic algorithm.
Returns
-------
cleaned_image : `~numpy.ndarray`
The cosmic-ray cleaned image.
crmask : `~numpy.ndarray` (bool)
A mask image of the identified cosmic rays. Cosmic-ray pixels
have a value of `True`.
"""
from scipy import ndimage
block_size = 2.0
kernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
clean_data = data.copy()
if background is not None:
clean_data += background
final_crmask = np.zeros(data.shape, dtype=bool)
if error is not None:
if data.shape != error.shape:
raise ValueError('error and data must have the same shape')
clean_error_image = error
ncosmics, ncosmics_tot = 0, 0
for iteration in range(maxiter):
sampled_img = block_replicate(clean_data, block_size)
convolved_img = ndimage.convolve(sampled_img, kernel,
mode=border_mode).clip(min=0.0)
laplacian_img = block_reduce(convolved_img, block_size)
if clean_error_image is None:
if effective_gain is None or readnoise is None:
raise AssertionError('effective_gain and readnoise must be '
'input if error is not input')
med5_img = ndimage.median_filter(clean_data,
size=5,
mode=border_mode).clip(min=1.e-5)
error_image = (np.sqrt(effective_gain * med5_img + readnoise**2) /
effective_gain)
else:
error_image = clean_error_image
snr_img = laplacian_img / (block_size * error_image)
# this is used to remove extended structures (larger than ~5x5)
snr_img -= ndimage.median_filter(snr_img, size=5, mode=border_mode)
# used to remove compact bright objects
med3_img = ndimage.median_filter(clean_data, size=3, mode=border_mode)
med7_img = ndimage.median_filter(med3_img, size=7, mode=border_mode)
finestruct_img = ((med3_img - med7_img) / error_image).clip(min=0.01)
cr_mask1 = snr_img > cr_threshold
# NOTE: to follow the paper exactly, this condition should be
# "> contrast * block_size". "lacos_im.cl" uses simply "> contrast"
cr_mask2 = (snr_img / finestruct_img) > contrast
cr_mask = cr_mask1 * cr_mask2
if mask is not None:
cr_mask = np.logical_and(cr_mask, ~mask)
# grow cosmic rays by one pixel and check in snr_img
selem = np.ones((3, 3))
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = cr_mask1 * neigh_mask
# now grow one more pixel and lower the detection threshold
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = (snr_img > neighbor_threshold) * neigh_mask
# previously unknown cosmic rays found in this iteration
crmask_new = np.logical_and(~final_crmask, cr_mask)
ncosmics = np.count_nonzero(crmask_new)
final_crmask = np.logical_or(final_crmask, cr_mask)
ncosmics_tot += ncosmics
log.info('Iteration {0}: Found {1} cosmic-ray pixels, '
'Total: {2}'.format(iteration + 1, ncosmics, ncosmics_tot))
if ncosmics == 0:
if background is not None:
clean_data -= background
return clean_data, final_crmask
clean_data = _clean_masked_pixels(clean_data,
final_crmask,
size=5,
exclude_mask=mask)
if background is not None:
clean_data -= background
return clean_data, final_crmask
def make_lens_sys(lensmodel, src_camera_kwargs, source_info, kwargs_band_src,
lens_info):
#Model
kwargs_model_postit = {
'lens_model_list': ['SIE'],
'source_light_model_list': ['INTERPOL']
}
kwargs_lens = lensmodel['kwargs_lens_list'] # SIE model
#data
numpix = len(source_info['image']) * source_info['HR_factor']
kwargs_source_mag = [{
'magnitude':
source_info['magnitude'],
'image':
source_info['image'],
'scale':
source_info['deltapix'] / source_info['HR_factor'],
'phi_G':
0,
'center_x':
lensmodel['source_shift'][0],
'center_y':
lensmodel['source_shift'][1]
}] #phi_G is to rotate, centers are to shift in arcsecs
sim = SimAPI(numpix=numpix,
kwargs_single_band=kwargs_band_src,
kwargs_model=kwargs_model_postit)
kwargs_numerics = {'supersampling_factor': source_info['HR_factor']}
imSim = sim.image_model_class(kwargs_numerics)
_, kwargs_source, _ = sim.magnitude2amplitude(
kwargs_source_mag=kwargs_source_mag)
#simulation
image_HD = imSim.image(kwargs_lens=kwargs_lens,
kwargs_source=kwargs_source)
mag_LS = -2.5 * np.log10(sum(sum(image_HD))) + source_info['zero_point']
magnification = source_info[
'magnitude'] - mag_LS #rough estimation in the magnitude change
#DES - PSF convolution
npsf = inter_psf(lens_info['psf'], lens_info['deltapix'],
source_info['deltapix'])
source_conv = signal.fftconvolve(image_HD, npsf, mode='same')
#resize in deltapix
source_lensed_res = block_reduce(
source_conv, source_info['HR_factor']) #in case an HR_factor was used
eq_pa = int(
lens_info['deltapix'] * len(lens_info['image']) *
len(source_lensed_res) /
(source_info['deltapix'] * len(source_info['image']))
) #this value is to consider a source image of the same size in arcseconds as the lens
bs = np.zeros([eq_pa, eq_pa])
val = int((len(bs) - len(source_lensed_res)) / 2)
bs[val:val + len(source_lensed_res),
val:val + len(source_lensed_res)] = source_lensed_res
source_size_scale = block_reduce(bs,
int(len(bs) / len(lens_info['image'])))
#flux rescale
flux_img = sum(image_HD.flatten()) * (10**(
0.4 * (lens_info['zero_point'] - source_info['zero_point'])))
sc = sum(source_conv.flatten()) / flux_img
source_scaled = source_size_scale / sc
#cut right pixels size
lens_size = min(np.shape(lens_info['image']))
source_size = min(np.shape(source_scaled))
if lens_size > source_size:
xin = yin = int((lens_size - source_size) / 2)
lens_final = lens_info['image'][xin:xin + source_size,
yin:yin + source_size]
source_final = source_scaled
if lens_size < source_size:
xin = yin = math.ceil((source_size - lens_size) / 2)
source_final = source_scaled[xin:xin + lens_size, yin:yin + lens_size]
lens_final = lens_info['image']
else:
lens_final = lens_info['image']
source_final = source_scaled
final = lens_final + source_final
phot_ap = 2
_, mag_sim = ap_phot(
len(final) / 2,
len(final) / 2, final, phot_ap / (lens_info['deltapix']),
phot_ap / (lens_info['deltapix']), np.pi / 2., lens_info['zero_point'])
#in the old code some images cause problems with some inf pixels... this will discard that system
if magnification == np.float('-inf') or magnification == np.float('inf'):
print('INFINITE MAGNIFICATION')
magnification = 10000
return {
'simulation': final,
'src_image': source_info['image'],
'mag_sim': mag_sim,
'mag_lensed_src': mag_LS,
'image_HD': image_HD,
'resize': source_size_scale,
'conv': source_conv,
'magnification': magnification
}
plt.savefig('/scratch/dw1519/galex/data/star_photon/' + outdir +
'/flat_in.pdf',
dpi=190)
plt.clf()
scans = [
'0023', '0212', '0302', '0464', '0500', '0689', '0806', '0941', '1247',
'1508', '1778', '2120', '2291', '2453', '2759', '2930', '3155', '3227',
'3326', '3497'
]
date = '08-21-2017' #'08-21-2017'
data = np.zeros((size, size))
flat = np.load('/scratch/dw1519/galex/data/star_photon/' + outdir +
'/flat0.npy')
data = np.load('/scratch/dw1519/galex/data/star_photon/' + outdir +
'/data.npy')
data = block_reduce(data, resample)
N = 10
for i in range(1, N):
model = np.zeros((size, size))
A_list = []
for scan in scans:
with open('../name_scan/%s' % scan) as f:
name_list = f.read().splitlines()
print name_list
if i == 0:
data += np.load(
'/scratch/dw1519/galex/data/star_photon/back/' + scan +
'_data.npy')
exp = np.load('/scratch/dw1519/galex/data/star_photon/back/' +
scan + '_exp.npy')
if len(exp.shape) < 2:
def lacosmic(data, contrast, cr_threshold, neighbor_threshold,
error=None, mask=None, background=None, effective_gain=None,
readnoise=None, maxiter=4, border_mode='mirror'):
"""
Remove cosmic rays from an astronomical image using the `L.A.Cosmic
<http://www.astro.yale.edu/dokkum/lacosmic/>`_ algorithm. The
algorithm is based on Laplacian edge detection and is described in
`PASP 113, 1420 (2001)`_.
.. _PASP 113, 1420 (2001):
http://adsabs.harvard.edu/abs/2001PASP..113.1420V
Parameters
----------
data : array_like
The 2D array of the image.
contrast : float
Contrast threshold between the Laplacian image and the
fine-structure image. If your image is critically sampled, use
a value around 2. If your image is undersampled (e.g. HST
data), a value of 4 or 5 (or more) is more appropriate. If your
image is oversampled, use a value between 1 and 2. For details,
please see `PASP 113, 1420 (2001)`_, which calls this parameter
:math:`f_{\\mbox{lim}}`. In particular, Figure 4 shows the
approximate relationship between the ``contrast`` parameter and
the pixel full-width half-maximum of stars in your image.
cr_threshold : float
The Laplacian signal-to-noise ratio threshold for cosmic-ray
detection.
neighbor_threshold : float
The Laplacian signal-to-noise ratio threshold for detection of
cosmic rays in pixels neighboring the initially-identified
cosmic rays.
error : array_like, optional
The pixel-wise Gaussian 1-sigma errors of the input ``data``.
If ``error`` is not input, then ``effective_gain`` and
``readnoise`` will be used to construct an approximate model of
the ``error``. If ``error`` is input, it will override the
``effective_gain`` and ``readnoise`` parameters. ``error`` must
have the same shape as ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when identifying cosmic rays. It is
highly recommended that saturated stars be included in ``mask``.
background : float or array_like, optional
The background level previously subtracted from the input
``data``. ``background`` may either be a scalar value or a 2D
image with the same shape as the input ``data``. If the input
``data`` has not been background-subtracted, then set
``background=None`` (default).
effective_gain : float, array-like, optional
Ratio of counts (e.g., electrons or photons) to the units of
``data``. For example, if your input ``data`` are in units of
ADU, then ``effective_gain`` should represent electrons/ADU. If
your input ``data`` are in units of electrons/s then
``effective_gain`` should be the exposure time (or an exposure
time map). ``effective_gain`` and ``readnoise`` must be
specified if ``error`` is not input.
readnoise : float, optional
The read noise (in electrons) in the input ``data``.
``effective_gain`` and ``readnoise`` must be specified if
``error`` is not input.
maxiter : float, optional
The maximum number of iterations. The default is 4. The
routine will automatically exit if no additional cosmic rays are
identified. If the routine is still identifying cosmic rays
after four iterations, then you are likely digging into sources
(e.g. saturated stars) and/or the noise. In that case, try
inputing a ``mask`` or increasing the value of ``cr_threshold``.
border_mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
The mode in which the array borders are handled during
convolution and median filtering. For 'constant', the value is
0. The default is 'mirror', which matches the original
L.A.Cosmic algorithm.
Returns
-------
cleaned_image : `~numpy.ndarray`
The cosmic-ray cleaned image.
crmask : `~numpy.ndarray` (bool)
A mask image of the identified cosmic rays. Cosmic-ray pixels
have a value of `True`.
"""
block_size = 2.0
kernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
clean_data = data.copy()
if background is not None:
clean_data += background
final_crmask = np.zeros(data.shape, dtype=bool)
if error is not None:
if data.shape != error.shape:
raise ValueError('error and data must have the same shape')
clean_error_image = error
ncosmics, ncosmics_tot = 0, 0
for iteration in range(maxiter):
sampled_img = block_replicate(clean_data, block_size)
convolved_img = ndimage.convolve(sampled_img, kernel,
mode=border_mode).clip(min=0.0)
laplacian_img = block_reduce(convolved_img, block_size)
if clean_error_image is None:
if effective_gain is None or readnoise is None:
raise ValueError('effective_gain and readnoise must be '
'input if error is not input')
med5_img = ndimage.median_filter(clean_data, size=5,
mode=border_mode).clip(min=1.e-5)
error_image = (np.sqrt(effective_gain*med5_img + readnoise**2) /
effective_gain)
else:
error_image = clean_error_image
snr_img = laplacian_img / (block_size * error_image)
# this is used to remove extended structures (larger than ~5x5)
snr_img -= ndimage.median_filter(snr_img, size=5, mode=border_mode)
# used to remove compact bright objects
med3_img = ndimage.median_filter(clean_data, size=3, mode=border_mode)
med7_img = ndimage.median_filter(med3_img, size=7, mode=border_mode)
finestruct_img = ((med3_img - med7_img) / error_image).clip(min=0.01)
cr_mask1 = snr_img > cr_threshold
# NOTE: to follow the paper exactly, this condition should be
# "> contrast * block_size". "lacos_im.cl" uses simply "> contrast"
cr_mask2 = (snr_img / finestruct_img) > contrast
cr_mask = cr_mask1 * cr_mask2
if mask is not None:
cr_mask = np.logical_and(cr_mask, ~mask)
# grow cosmic rays by one pixel and check in snr_img
selem = np.ones((3, 3))
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = cr_mask1 * neigh_mask
# now grow one more pixel and lower the detection threshold
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = (snr_img > neighbor_threshold) * neigh_mask
# previously unknown cosmic rays found in this iteration
crmask_new = np.logical_and(~final_crmask, cr_mask)
ncosmics = np.count_nonzero(crmask_new)
final_crmask = np.logical_or(final_crmask, cr_mask)
ncosmics_tot += ncosmics
log.info('Iteration {0}: Found {1} cosmic-ray pixels, '
'Total: {2}'.format(iteration + 1, ncosmics, ncosmics_tot))
if ncosmics == 0:
if background is not None:
clean_data -= background
return clean_data, final_crmask
clean_data = _clean_masked_pixels(clean_data, final_crmask, size=5,
exclude_mask=mask)
if background is not None:
clean_data -= background
return clean_data, final_crmask
def test_block_size_broadcasting(self):
"""Test scalar block_size broadcasting."""
data = np.arange(16).reshape(4, 4)
result1 = block_reduce(data, 2)
result2 = block_reduce(data, (2, 2))
assert np.all(result1 == result2)
def show_image(image,
percl=99,
percu=None,
is_mask=False,
figsize=(6, 10),
cmap='viridis',
log=False,
show_colorbar=True,
show_ticks=True,
fig=None,
ax=None,
input_ratio=None):
"""
Show an image in matplotlib with some basic astronomically-appropriat stretching.
Parameters
----------
image
The image to show
percl : number
The percentile for the lower edge of the stretch (or both edges if ``percu`` is None)
percu : number or None
The percentile for the upper edge of the stretch (or None to use ``percl`` for both)
figsize : 2-tuple
The size of the matplotlib figure in inches
"""
if percu is None:
percu = percl
percl = 100 - percl
if (fig is None and ax is not None) or (fig is not None and ax is None):
raise ValueError('Must provide both "fig" and "ax" '
'if you provide one of them')
elif fig is None and ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize)
if figsize is not None:
# Rescale the fig size to match the image dimensions, roughly
image_aspect_ratio = image.shape[0] / image.shape[1]
figsize = (max(figsize) * image_aspect_ratio, max(figsize))
print(figsize)
# To preserve details we should *really* downsample correctly and
# not rely on matplotlib to do it correctly for us (it won't).
# So, calculate the size of the figure in pixels, block_reduce to
# roughly that,and display the block reduced image.
# Thanks, https://stackoverflow.com/questions/29702424/how-to-get-matplotlib-figure-size
fig_size_pix = fig.get_size_inches() * fig.dpi
ratio = (image.shape // fig_size_pix).max()
if ratio < 1:
ratio = 1
ratio = input_ratio or ratio
# Divide by the square of the ratio to keep the flux the same in the
# reduced image
reduced_data = block_reduce(image, ratio) / ratio**2
# Of course, now that we have downsampled, the axis limits are changed to
# match the smaller image size. Setting the extent will do the trick to
# change the axis display back to showing the actual extent of the image.
extent = [0, image.shape[1], 0, image.shape[0]]
if log:
stretch = aviz.LogStretch()
else:
stretch = aviz.LinearStretch()
norm = aviz.ImageNormalize(reduced_data,
interval=aviz.AsymmetricPercentileInterval(
percl, percu),
stretch=stretch)
if is_mask:
# The image is a mask in which pixels are zero or one. Set the image scale
# limits appropriately.
scale_args = dict(vmin=0, vmax=1)
else:
scale_args = dict(norm=norm)
im = ax.imshow(reduced_data,
origin='lower',
cmap=cmap,
extent=extent,
aspect='equal',
**scale_args)
if show_colorbar:
# I haven't a clue why the fraction and pad arguments below work to make
# the colorbar the same height as the image, but they do....unless the image
# is wider than it is tall. Sticking with this for now anyway...
# Thanks: https://stackoverflow.com/a/26720422/3486425
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, format='%2.0f')
# In case someone in the future wants to improve this:
# https://joseph-long.com/writing/colorbars/
# https://stackoverflow.com/a/33505522/3486425
# https://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html#colorbar-whose-height-or-width-in-sync-with-the-master-axes
if not show_ticks:
ax.tick_params(labelbottom=False,
labelleft=False,
labelright=False,
labeltop=False)
def test_block_size_len(self):
"""Test block_size length."""
data = np.ones((2, 2))
with pytest.raises(ValueError):
block_reduce(data, (2, 2, 2))
sigma_v = (p75 - p25) / eta
"""
Creating the resolution array which are integers factors
"""
res = list((10**np.linspace(0, np.log10(N), 100) + 1e-5))
res.sort()
res = np.array(res).astype(int)
res = np.array(list(set(res)))
res.sort()
res = np.array(list(set((N / res).astype(int))))
res.sort()
stds = np.zeros_like(res, dtype=float)
serr = np.zeros_like(res, dtype=float)
for k, R in enumerate(res):
fake = block_reduce(difference_map, R, func=np.sum) / R**2
lf = len(fake.ravel())
stds[k] = np.std(fake.ravel())
stds[-1] = 4 * stds[-2]
serr = 1.5 * stds * res / res.max()
sve = 0.25 * kms_to_pcmyr * 2 * DX / res**1.5
stot = np.sqrt(sve**2 + serr**2)
stot = np.maximum(stot, 0.2 * stds)
resolutions = ''
sigma_array = ''
error_array = ''
for sa, re, se in zip(stds, res * DX, stot):
sigma_array += ' ' + str(sa)
resolutions += ' ' + str(re)
error_array += ' ' + str(se)
frac = cut/full
frac[np.isnan(frac)] = 0.
flat = frac*flat
print flat.shape
np.save('/scratch/dw1519/galex/data/star_photon/'+outdir+'/flat_in.npy', flat)
plt.imshow(flat, vmin=0, vmax=1.5)
plt.colorbar()
plt.savefig('/scratch/dw1519/galex/data/star_photon/'+outdir+'/flat_in.pdf', dpi=190)
plt.clf()
scans = ['0023', '0212', '0302', '0464', '0500', '0689', '0806', '0941', '1247', '1508', '1778',
'2120', '2291', '2453','2759', '2930', '3155', '3227', '3326', '3497']
date = '08-21-2017'#'08-21-2017'
data = np.zeros((size,size))
flat = np.load('/scratch/dw1519/galex/data/star_photon/'+outdir+'/flat2.npy')
data = np.load('/scratch/dw1519/galex/data/star_photon/'+outdir+'/data.npy')
data = block_reduce(data, resample)
N=10
for i in range(3, N):
model = np.zeros((size,size))
A_list = []
for scan in scans:
with open('../name_scan/%s'%scan) as f:
name_list = f.read().splitlines()
print name_list
if i==0:
data += np.load('/scratch/dw1519/galex/data/star_photon/back/'+scan+'_data.npy')
exp = np.load('/scratch/dw1519/galex/data/star_photon/back/'+scan+'_exp.npy')
if len(exp.shape)<2:
exp = np.concatenate(exp,axis=0)
print exp.shape
star_list = np.load('/scratch/dw1519/galex/data/star_photon/back/'+scan+'_bstar.npy')
def lacosmic(data, contrast, cr_threshold, neighbor_threshold,
error=None, mask=None, background=None, effective_gain=None,
readnoise=None, maxiter=4, border_mode='mirror'):
block_size = 2.0
kernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
clean_data = data.copy()
if background is not None:
clean_data += background
final_crmask = np.zeros(data.shape, dtype=bool)
if error is not None:
if data.shape != error.shape:
raise ValueError('error and data must have the same shape')
clean_error_image = error
ncosmics, ncosmics_tot = 0, 0
for iteration in range(maxiter):
sampled_img = block_replicate(clean_data, block_size)
convolved_img = ndimage.convolve(sampled_img, kernel,
mode=border_mode).clip(min=0.0)
laplacian_img = block_reduce(convolved_img, block_size)
if clean_error_image is None:
if effective_gain is None or readnoise is None:
raise ValueError('effective_gain and readnoise must be '
'input if error is not input')
med5_img = ndimage.median_filter(clean_data, size=5,
mode=border_mode).clip(min=1.e-5)
error_image = (np.sqrt(effective_gain*med5_img + readnoise**2) /
effective_gain)
else:
error_image = clean_error_image
snr_img = laplacian_img / (block_size * error_image)
# this is used to remove extended structures (larger than ~5x5)
snr_img -= ndimage.median_filter(snr_img, size=5, mode=border_mode)
# used to remove compact bright objects
med3_img = ndimage.median_filter(clean_data, size=3, mode=border_mode)
med7_img = ndimage.median_filter(med3_img, size=7, mode=border_mode)
finestruct_img = ((med3_img - med7_img) / error_image).clip(min=0.01)
cr_mask1 = snr_img > cr_threshold
# NOTE: to follow the paper exactly, this condition should be
# "> contrast * block_size". "lacos_im.cl" uses simply "> contrast"
cr_mask2 = (snr_img / finestruct_img) > contrast
cr_mask = cr_mask1 * cr_mask2
if mask is not None:
cr_mask = np.logical_and(cr_mask, ~mask)
# grow cosmic rays by one pixel and check in snr_img
selem = np.ones((3, 3))
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = cr_mask1 * neigh_mask
# now grow one more pixel and lower the detection threshold
neigh_mask = ndimage.binary_dilation(cr_mask, selem)
cr_mask = (snr_img > neighbor_threshold) * neigh_mask
# previously unknown cosmic rays found in this iteration
crmask_new = np.logical_and(~final_crmask, cr_mask)
ncosmics = np.count_nonzero(crmask_new)
final_crmask = np.logical_or(final_crmask, cr_mask)
ncosmics_tot += ncosmics
if (ncosmics_tot>0):
log.info('Iteration {0}: Found {1} cosmic-ray pixels, '
'Total: {2}'.format(iteration + 1, ncosmics, ncosmics_tot))
cr_image = 1*final_crmask*255
return cr_image,ncosmics_tot
def apply_beam(infile,
fwhm,
pa=0,
nu=0,
outfile='',
bin_img=1,
bin_spec=1,
eff=1,
telescope='APEX',
pixel_size=None,
output_Tmb=False,
flip_lr=False,
flip_ud=False,
log_scale=False,
overwrite=False,
verbose=False):
"""
Function used to convolve images by single-beams.
This does not apply for interferometers, in such case, use simobserve instead.
Bin the image if required (i.e. bin_size!=0).
Perform convolution with a gaussian beam
Note: is better to implement convolve_fft() instead of
convolve() since it works faster for large kernels (n>500)
Also rescale intensities from Jy/px to Jy/beam by the (1.331*(fwhm**2)/(pixel_size**2)) factor
"""
from astropy.convolution import Gaussian2DKernel, convolve, convolve_fft
from astropy.nddata.utils import block_reduce
fname = sys._getframe().f_code.co_name
start_time = time.time()
# Detects whether input data comes from a file or an array
if type(infile) == str:
header = fits.getheader(infile)
data = fits.getdata(infile)
input_from_file = True
elif isinstance(infile, (list, np.ndarray)):
input_from_file = False
data = np.array(infile)
# Ensure data has at least one dimension
data = np.array(data, ndmin=1)
else:
input_from_file = False
# Assure binning factors are not negative
bin_img = 1 if bin_img < 1 else bin_img
bin_spec = 1 if bin_spec < 1 else bin_spec
# Rescale header elements by binning factors
if input_from_file:
header['CDELT1'] *= bin_img
header['CRPIX1'] /= bin_img
header['CDELT2'] *= bin_img
header['CRPIX2'] /= bin_img
if 'CDELT3' in header:
header['CDELT3'] *= bin_spec
header['CRPIX3'] /= bin_spec
# Bin the spectrum and image if required
if bin_spec > 1 or bin_img > 1:
print_(f"Original cube shape: {np.shape(data)}",
fname,
verbose=verbose)
if data.ndim == 3:
data = block_reduce(data, [bin_spec, 1, 1],
func=np.nanmean) if bin_spec > 1 else data
data = block_reduce(data, [1, bin_img, bin_img],
func=np.nanmean) if bin_img > 1 else data
else:
raise Exception(
"[!] Input data does not have three dimensions, impossible to detect spectral axis."
)
print_(f"Binned cube shape: {np.shape(data)}", fname, verbose=verbose)
# Obtain the pixel size in arcseconds. Function argument pixel_size (in arcsec) overrides all other options.
if pixel_size in [0, None]:
# Read from the header
if input_from_file:
pixel_size = np.float64(np.abs(header['CDELT1'])) * (u.deg).to(
u.arcsec)
# Ask the user to enter it
else:
pixel_size = float(input("Enter pixel size in arcsec: "))
# Convert fwhm to numpy array. If a scalar, use it both as bmin and bmaj.
fwhm = np.array(fwhm) if np.size(fwhm) > 1 else np.array([fwhm, fwhm])
# Convert FWHM["] --> FWHM[px]
fwhm_pix = fwhm / pixel_size
fwhm_to_std = np.sqrt(8 * np.log(2))
sigma = fwhm_pix / fwhm_to_std
# Create the Gaussian Kernel
kernel = Gaussian2DKernel(x_stddev=sigma[0],
y_stddev=sigma[1],
theta=pa * u.deg.to(u.rad)).array
print_(f'Data shape: {np.shape(data)}', fname, verbose=verbose)
print_(f'Kernel shape: {np.shape(kernel)}', fname, verbose=verbose)
print_(f'Convolving ...', fname, verbose=verbose)
convolved_data = []
# Loop over the cube
for idx, channel in enumerate(data):
# Use Fast Fourier Transform only for arrays of side-length larger than 500.
convolver_string = "Using FFT ..." if len(
channel) < 500 else "Not using FFT ..."
if idx == 0: print_(convolver_string, fname, verbose=verbose)
# Convolve de the image.
c = convolve(channel, kernel) if len(channel) < 500 else convolve_fft(
channel, kernel)
# Rescale intensity (Jy/px) --> (Jy/beam)
# Rescaling factor: Output_beam_area / Input_beam_area
# Output_beam area = Area of a Gassian beam
# Area of a Gaussian beam: 2*pi/(8*ln(2)) * (FWHM_maj*FWHM_min) = 1.133 * FWHM**2
rescaling_factor = 1.1331 * (fwhm[0] * fwhm[1]) / (pixel_size**2)
c = c * rescaling_factor
# Convert intensities (Jy/beam) to brightness temperature (K)
Tmb = brightness_temperature(c, unit='Jy/beam', nu=nu,
fwhm=fwhm) if output_Tmb else c
# Rescale by the telescope efficiency if indicated ()
if eff != 1: Tmb = (1 / eff) * Tmb
# Flip lef-right if indicated
if flip_lr: Tmb = np.fliplr(Tmb)
# Flip upside-down if indicated
if flip_ud: Tmb = np.flipud(Tmb)
# Convert to logscale if indicated
if log_scale: Tmb = np.log10(Tmb)
convolved_data.append(Tmb)
# Write additional keywords to header
if input_from_file:
header['BTYPE'] = 'Tmb' if output_Tmb else 'intensity'
header['BTYPE'] = 'K' if output_Tmb else 'Jy/beam'
header['BMIN'] = fwhm[0] * (u.arcsec).to(u.deg)
header['BMAJ'] = fwhm[1] * (u.arcsec).to(u.deg)
header['BPA'] = pa
header['TELESCOP'] = str(telescope)
# Write data to fits file if required
write_fits(outfile, convolved_data, header, overwrite, fname, verbose)
# Print the time taken by the function
elapsed_time(time.time() - start_time, fname, verbose)
return np.array(convolved_data)
def test_1d(self):
"""Test 1D array."""
data = np.arange(4)
expected = np.array([1, 5])
result = block_reduce(data, 2)
assert np.all(result == expected)
def test_1d(self):
"""Test 1D array."""
data = np.arange(4)
expected = np.array([1, 5])
result = block_reduce(data, 2)
assert np.all(result == expected)
def test_block_size_broadcasting(self):
"""Test scalar block_size broadcasting."""
data = np.arange(16).reshape(4, 4)
result1 = block_reduce(data, 2)
result2 = block_reduce(data, (2, 2))
assert np.all(result1 == result2)
name_list = ['0023', '0032', '0203', '0239', '0446', '0464', '0473', '0806', '0815', '1301', '1310', '1319',\
'1616', '1634', '1679', '2174', '2183', '2192', '2714', '2750', '3236', '3245', '3281']
name_list = ['2561']
name_list = ['0050']
date='11-07-2017'
date1='08-21-2017/0001-0500'
factor = 4
for name in name_list:
#name = re.split('/', name)[3]
if name == 'name2444-2480':
continue
print name
count = pyfits.open('../fits/scan_map/'+date1+'/count_map_%s_count.fits'%name)
exp = pyfits.open('../fits/scan_map/'+date+'/count_map_%s_exp.fits'%name)
cm = block_reduce(count[0].data, factor)
em = block_reduce(exp[0].data, factor, np.mean)
w = WCS('../fits/scan_map/'+date1+'/count_map_%s_count.fits'%name)
wn = get_resampled_wcs(w,factor,True)
header = wn.to_header()
hdu = pyfits.PrimaryHDU(em)
hdu.header = count[0].header
hdu.header['NAXIS1'] = em.shape[1]#header['NAXIS1']
hdu.header['NAXIS2'] = em.shape[0]#header['NAXIS2']
hdu.header['CRPIX1'] = header['CRPIX1']
hdu.header['CRPIX2'] = header['CRPIX2']
hdu.header['CDELT1'] = header['CDELT1']
hdu.header['CDELT2'] = header['CDELT2']
hdulist = pyfits.HDUList([hdu])
def test_2d(self):
"""Test 2D array."""
data = np.arange(4).reshape(2, 2)
expected = np.array([[6]])
result = block_reduce(data, 2)
assert np.all(result == expected)