def specflat(flat,rows=True,indiv=False,wid=100) :
"""
Removes spectral signature from a flat by dividing by smoothed version
Args:
flat: input flat fields
Keyword args:
rows (bool) : specifies if smoothing is along rows (default), otherwise columns
indiv (bool) : specifies if smoothing is done row-by-row, or single for whole image (default)
wid (int) : boxcar kernel width (default=100)
Returns:
flat with spectral signature removed
"""
boxcar = Box1DKernel(wid)
smooth=flat
if rows :
if indiv :
for row in range(flat.shape[0]) :
smooth[row,:] /= convolve(flat[row,:],boxcar,boundary='extend')
else :
c=convolve(flat.sum(axis=0),boxcar,boundary='extend')
for row in range(flat.shape[0]) :
smooth[row,:] /= c
else :
print('smoothing by columns not yet implemented!')
pdb.set_trace()
return smooth
def calculate_gradient(self,P,G,Z,smooth=False):
"""Calculate the gradient of any property. Deals with grid points that have no mass (that should'nt contribute to the gradient).
:param P: A param.Param instance.
.. warning::
This returns the gradient of the input field ... kind of ... sometimes
"""
Z = ma.masked_where(G.m<P.M_tol,Z).reshape(P.G.ny,P.G.nx)
# if smooth: # For details of astropy convolution process, see here: http://docs.astropy.org/en/stable/convolution/using.html
# kernel = Box2DKernel(smooth) # smallest possible square kernel is 3
# kernel = Gaussian2DKernel(x_stddev=1,y_stddev=1)
# Z = convolve(Z, kernel, boundary='extend')
dZdy,dZdx = gradient(Z,G.dy,G.dx)
if smooth: # For details of astropy convolution process, see here: http://docs.astropy.org/en/stable/convolution/using.html
# # kernel = Box2DKernel(smooth) # smallest possible square kernel is 3
kernel = Gaussian2DKernel(x_stddev=1,y_stddev=1)
dZdy = convolve(dZdy, kernel, boundary='extend')
dZdx = convolve(dZdx, kernel, boundary='extend')
grad = array([dZdx.flatten(),
dZdy.flatten(),
zeros_like(G.m)]).T
return grad
def process_image(medianed_image,vgridsize,hgridsize,lg,filter_width=13):
# define your function for leastsq to obtain initial value estimates:
def func(x):
return k() + np.multiply(n(),np.absolute(x))
IV = np.zeros((vgridsize,2))
k = Parameter(5)
n = Parameter(-1.5)
for i in range(0,vgridsize):
IV[i] = fit(func,[k,n],np.log10(medianed_image[i,:]),np.log10(np.abs(lg)))[0]
#extrapolate power law data
pl_image = np.zeros((vgridsize,hgridsize))
for i in range(0,vgridsize):
k = Parameter(IV[i,0])
n = Parameter(IV[i,1])
pl_image[i] = (10**IV[i,0])*np.power(np.abs(lg),IV[i,1])
#detrending data and power law extrapolation using boxcar filtering
data_filtered = np.zeros((vgridsize,hgridsize))
for i in range(0,vgridsize):
data_filtered[i] = convolve(medianed_image[i,:],Box1DKernel(filter_width))
smooth_pl = np.zeros((vgridsize,hgridsize))
for i in range(0,vgridsize):
smooth_pl[i] = convolve(pl_image[i,:],Box1DKernel(filter_width))
del i
return data_filtered,pl_image,smooth_pl,IV
def convolve_with_psf(self, add_psf=True):
if add_psf:
if self.telescope.psf_fits_file != None:
#first, rebin to psf pixel scale
#n_pixel_orig = self.sunrise_image.n_pixels
n_pixel_new = self.sunrise_image.n_pixels*self.sunrise_image.pixel_in_arcsec/self.telescope.psf_pixsize_arcsec
new_image = congrid(self.sunrise_image.image, (n_pixel_new,n_pixel_new))
#second, convolve with PSF
if CONVOLVE_TYPE=='astropy':
#astropy.convolution.convolve()
print "convolving with astropy"
conv_im = convolve(new_image,self.telescope.psf_kernel/np.sum(self.telescope.psf_kernel),boundary='fill',fill_value=0.0) #boundary option?
else:
#scipy.signal.convolve2d()
conv_im = convolve(new_image,self.telescope.psf_kernel/np.sum(self.telescope.psf_kernel),boundary='fill',fillvalue=0.0,mode='same') #boundary option?
self.psf_image.init_image(conv_im,self)
del new_image, conv_im
else:
self.add_gaussian_psf(add_psf=add_psf)
else:
self.psf_image.init_image(self.sunrise_image.image, self)
gc.collect()
def match_psf(img, psf, psfto): """ match the psf of the image from psf to psfto Params ------ img (np 2d array) psf (np 2d array) psfto (np 2d array) Return ------ img_cnvled (np 2d array): the psf matched image psf_cnvled (np 2d array): the matched psf (psf convolved with kernel_cnvl) kernel_cnvl (np 2d array): the moffat kernel that can smear psf into psfto """ # get convolving kernel kernel_cnvl = get_convolving_moffat_kernel(psf, psfto) # make psf matched img img_cnvled = ac.convolve(img, kernel_cnvl) # make psf matched psf psf_cnvled = ac.convolve(psf, kernel_cnvl) return img_cnvled, psf_cnvled, kernel_cnvl
def test_mask_convolve():
# Numpy is fundamentally incompatible with the objects we have created.
# np.ma.is_masked(array) checks specifically for the array's _mask
# attribute. We would have to refactor deeply to correct this, and I
# really don't want to do that because 'None' is a much more reasonable
# and less dangerous default for a mask.
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
assert spec.mask is False
from astropy.convolution import convolve,Box1DKernel
convolve(spec, Box1DKernel(3))
def smoothing(parameters,gridvals):
cellsize = parameters.boxsize/parameters.Ng
smoothing_radius_cells = parameters.smoothing_radius/cellsize
kernel_res = 3
kernel = make_kernel([kernel_res,kernel_res,kernel_res],smoothing_radius_cells)
smoothed_vals = copy(gridvals)
smoothed_vals = convolve(smoothed_vals,kernel,boundary='wrap')
n = 0
while sp.any(sp.isnan(smoothed_vals)):
smoothed_vals = convolve(smoothed_vals,kernel,boundary='wrap')
print "Went through the convolving-loop", n, "times"
return smoothed_vals
def convolveGaussian(self, image, gaussSigma, **kwargs):
if (type(gaussSigma) is int) or (type(gaussSigma) is float):
gaussSigma = np.array([gaussSigma, gaussSigma])
gRow = conv.Gaussian1DKernel(gaussSigma[0])
gCol = conv.Gaussian1DKernel(gaussSigma[1])
convImage = np.copy(image)
for rowNum in range(0, len(image)):
convImage[rowNum] = conv.convolve(convImage[rowNum], gRow, **kwargs)
for col in range(0, len(image.T)):
convImage[:,col] = conv.convolve(convImage[:,col], gCol, **kwargs)
return convImage
def get_inflection_point(x, w=2, threshold=0.01):
from astropy.convolution import convolve, Gaussian1DKernel
sm = convolve(x, Gaussian1DKernel(w), boundary="extend")
pos = np.diff(sm) > threshold
return np.argmax(pos)
return np.argmin(np.abs(np.diff(x)))
def timeseries(ax, telemetry, select=None, **kwargs):
"""Plot a telemetry timeseries."""
time, data = prepare_timeseries_data(telemetry)
kwargs.setdefault('alpha', max([0.01, 5.0/data.shape[1]]))
if data.shape[1] > 20:
kwargs.setdefault('color', 'k')
if select is not None:
data = data[:,select]
lines = ax.plot(time, data, **kwargs)
if select is not None:
sdata = np.apply_along_axis(lambda a: convolve(a, Gaussian1DKernel(stddev=50), boundary='extend'), 0, data)
kwargs['alpha'] = 1.0
for i, line in enumerate(lines):
kwargs['color'] = line.get_color()
ax.plot(time, sdata[:,i], **kwargs)
ax.set_xlabel("Time (s)")
ax.set_ylabel("{0:s}".format(telemetry.kind.name))
ax.set_title("Timeseries of {0:s} from {1:s}".format(
telemetry.kind.name, telemetry.dataset.title()))
ax.set_xlim(np.min(time), np.max(time))
def smoothing(combo, targres, origfwhm, pixscale):
"""
Smooth the image to the targeted final angular resolution.
Parameters
----------
combo : float array
Combined image
targres : float
The HPBW of the smoothed image (in units of arcsecond)
origfwhm : float
The original HPBW of input image (in units of arcsecond)
pixscale : float
The pixscale of the input image (in units of arcsecond)
Returns
-------
combo : float array
Smoothed image
"""
fwhm = np.sqrt(8*np.log(2))
kernel_size = ((targres/fwhm)**2-(hiresfwhm/fwhm)**2)**0.5
pixel_n = kernel_size/pixscale
#smooth the image using gaussian 2d kernel
gauss_kernel = Gaussian2DKernel(pixel_n)
combo = convolve(combo, gauss_kernel,normalize_kernel=True)
return combo
def gaussian(x,par,kernel=None):
"""
gaussian(x,par,kernel=None)
An 1D Gaussian function, structured in a way that it can be given an
lmfit Parameters instance as input.
Parameters
----------
x : numpy.ndarray
The x axis data of function.
par : lmfit.parameter.Parameters
The lmfit Parameters instance, which should contain the following:
* peak - the peak height of the Gaussian
* fwhm - the full width at half maximum of the Gaussian
* pos - the peak position of the Gaussian
kernel : Nonetype, numpy.ndarray or astropy.convolution.Kernel
If set, the result will be convolved using this kernel.
Returns
-------
The function calculated over the range in x, and convolved with the
kernel if one was given.
"""
peak=par['peak'].value
fwhm=par['fwhm'].value
pos=par['pos'].value
out_y=peak*np.exp(-2.35*(x-pos)**2./fwhm**2.)
if not(np.any(kernel)):
return out_y
else:
return convolution.convolve(out_y,kernel)
def diffusion(self, wave, im):
"""Simulate UVIS's charge diffusion.
Parameters
----------
wave : float
The effective wavelength of the image. [micron]
im : ndarray
The image to process.
Returns
-------
newim : ndarray
The processed image.
Notes
-----
Based on diffusion parameters from STScI Instrument Science Report
2008-014.
"""
from astropy.convolution import convolve
w0 = [0.250, 0.810] # micron
k0 = np.array([[[0.027, 0.111, 0.027],
[0.111, 0.432, 0.111],
[0.027, 0.111, 0.027]],
[[0.002, 0.037, 0.002],
[0.037, 0.844, 0.037],
[0.002, 0.037, 0.002]]])
dk = (k0[1] - k0[0]) / (w0[1] - w0[0])
k = k0[0] + dk * (wave - w0[0])
k /= k.sum()
return convolve(im, k, boundary='extend')
def conv(img):
kernel_file=pyfits.open(root+kfile)
kernel=kernel_file[0].data
new=convolve(img,kernel)
return new
def convolve(self, target_beam):
"""
Convolve *to* this rsolution
beam = [bmaj, bmin, bpa]
"""
from lib_beamdeconv import deconvolve_ell, EllipticalGaussian2DKernel
from astropy import convolution
# if difference between beam is negligible <1%, skip - it mostly happens when beams are exactly the same
beam = self.get_beam()
if (np.abs((target_beam[0]/beam[0])-1) < 1e-2) and (np.abs((target_beam[1]/beam[1])-1) < 1e-2) and (np.abs(target_beam[2] - beam[2]) < 1):
logging.debug('%s: do not convolve. Same beam.' % self.imagefile)
return
# first find beam to convolve with
convolve_beam = deconvolve_ell(target_beam[0], target_beam[1], target_beam[2], beam[0], beam[1], beam[2])
if convolve_beam[0] is None:
logging.error('Cannot deconvolve this beam.')
sys.exit(1)
logging.debug('%s: Convolve beam: %.3f" %.3f" (pa %.1f deg)' \
% (self.imagefile, convolve_beam[0]*3600, convolve_beam[1]*3600, convolve_beam[2]))
# do convolution on data
bmaj, bmin, bpa = convolve_beam
assert abs(self.img_hdr['CDELT1']) == abs(self.img_hdr['CDELT2'])
pixsize = abs(self.img_hdr['CDELT1'])
fwhm2sigma = 1./np.sqrt(8.*np.log(2.))
gauss_kern = EllipticalGaussian2DKernel((bmaj*fwhm2sigma)/pixsize, (bmin*fwhm2sigma)/pixsize, (90+bpa)*np.pi/180.) # bmaj and bmin are in pixels
self.img_data = convolution.convolve(self.img_data, gauss_kern, boundary=None)
self.img_data *= (target_beam[0]*target_beam[1])/(beam[0]*beam[1]) # since we are in Jt/b we need to renormalise
self.set_beam(target_beam) # update beam
def test__cross_correlation(self):
self.wc.lamp = self.ccd.copy()
self.wc.serial_binning = 1
x_axis = np.arange(0, 4060, 1)
reference = np.zeros(4060)
gaussian = models.Gaussian1D(stddev=2)
for i in sorted(np.random.choice(x_axis, 30)):
gaussian.mean.value = i
reference += gaussian(x_axis)
offset = np.random.choice(range(1, 15), 1)[0]
for slit in [1, 2, 3, 4, 5]:
new_array = np.append(reference[offset:], np.zeros(offset))
if slit > 3:
box_kernel = Box1DKernel(width=slit / 0.15)
new_array = convolve(new_array, box_kernel)
self.assertEqual(len(reference), len(new_array))
self.wc.lamp.header['SLIT'] = '{:d}.0" long slit'.format(slit)
correlation_value = self.wc._cross_correlation(reference=reference,
new_array=new_array)
self.assertEqual(correlation_value, offset)
def gsmooth_cube(cube, kernelsize, use_fft=True, psf_pad=False, fft_pad=False,
kernelsize_mult=8, **kwargs):
"""
Smooth a cube with a gaussian in 3d
Because even a tiny cube can become enormous if you have, say, a 1024x32x32
cube, padding is off by default
"""
if cube.ndim != 3:
raise ValueError("Wrong number of dimensions for a data cube")
#z,y,x = np.indices(cube.shape)
# use an odd kernel size for non-fft, even kernel size for fft
ks = (np.array(kernelsize)*kernelsize_mult).astype('int')
if np.any(ks % 2 == 0) and not use_fft:
ks[ks % 2 == 0] += 1
z,y,x = np.indices(ks)
kernel = np.exp(-((x-x.max()/2.)**2 / (2.*(kernelsize[2])**2) +
(y-y.max()/2.)**2 / (2.*(kernelsize[1])**2) +
(z-z.max()/2.)**2 / (2.*(kernelsize[0])**2)))
if use_fft:
return convolve_fft(cube, kernel, normalize_kernel=True,
psf_pad=psf_pad, fft_pad=fft_pad, **kwargs)
else:
return convolve(cube, kernel, normalize_kernel=True, **kwargs)
def voigt_abs(pars, t):
"""
f_obs = f_source * e ^-tau
"""
N, sigma, z, osc, l0, gamma, resolution_fwhm = pars
# Move wavelength array to rest
t = t.copy() / (1 + z)
# Center wavlength array at line
t_rest = t - l0
# Convert to km/s
t = c * t_rest / t
# Get optical depth
tau = voigt_tau(t, N, osc, l0, sigma, gamma)
# Absorb flux
flux = np.exp(-tau)
# Correct for instrumental resolution
if resolution_fwhm > 0.:
# Get transformation from km/s to pixels for convolution
mask = (t > - 5000) & (t < 5000)
transform = np.median(np.diff(t)[mask[:-1]])
if np.isnan(transform):
transform = 1e3
# print(transform)
# Convolve with linespread-function
flux = convolve(flux, Gaussian1DKernel(stddev=resolution_fwhm/(sigfwhm*transform), mode="oversample"))
return flux
def convolve_image(self,stddev,scale=True):
# """The inputs are as follows:
# stddev = standard deviation of the Gaussian the step function will be convolved with
# scale = True -- will scale the flux by the redshift unless set to False
#
# The output variables are as follows:
# Nothing is returned but the object property, img, is updated to hold the convolved image
# """
##CURRENTLY BUILDING IMAGE AS LAMBA,X,Y
##Because writing the fits file transposes the x and z axes for some reason
self.img = np.zeros((len(self.wavelengths),len(self.frb[0,:]),len(self.frb[1,:])))
gauss = Gaussian1DKernel(stddev=stddev,x_size=len(self.wavelengths))
zconvolve = convolve(self.step_function,gauss.array,boundary='extend')
for i in range(len(self.frb[0,:])):
for j in range(len(self.frb[1,:])):
if scale:
self.img[:,j,i] = zconvolve*(self.frb[i,j]/((1.+self.z_observed)**4.0))
else:
self.img[:,j,i] = zconvolve*self.frb[i,j]
return
def run(self): while not self.kill_received: # get a task try: x_range, y_range = self.work_queue.get_nowait() except Queue.Empty: break # the actual processing print 'Worker running: ', multiprocessing.current_process() # print "Convolving image section - XRANGE=", y_range, " - YRANGE=", x_range im_size=np.asarray(shared_im.shape) kernel_size=np.asarray(shared_kernel.shape) im_pad=(kernel_size-1)/2+10 x_pad=[0 if x_range[0]==0 else -im_pad[0], 0 if x_range[1]==(im_size[0]-1) else im_pad[0]] y_pad=[0 if y_range[0]==0 else -im_pad[1], 0 if y_range[1]==(im_size[1]-1) else im_pad[1]] shared_nim[x_range[0]:x_range[1], y_range[0]:y_range[1]]=convolve(shared_im[x_range[0]+x_pad[0]:x_range[1]+x_pad[1], y_range[0]+y_pad[0]:y_range[1]+y_pad[1]], shared_kernel, normalize_kernel=True)[-x_pad[0]:x_range[1]-x_range[0]-x_pad[0], -y_pad[0]:y_range[1]-y_range[0]-y_pad[0]] print 'Worker done: ', multiprocessing.current_process() self.result_queue.put(id)
def calc_line_profile(self, phase, inclination, nbins=100,
v_macro=2*u.km/u.s, v_inst=4*u.km/u.s,
v_min=-40*u.km/u.s, v_max=40*u.km/u.s):
# get CartesianRepresentation pointing to earth at these phases
earth = set_earth(inclination, phase)
# get CartesianRepresentation of projected velocities
vproj = dot(earth, self.tile_velocities).to(u.km/u.s)
# which tiles fall in which bin?
bins = np.linspace(v_min, v_max, nbins)
indices = np.digitize(vproj, bins)
lum = self._luminosity_array(phase, inclination)
phase = np.asarray(phase)
trailed_spectrum = np.zeros((phase.size, nbins))
for i in range(nbins):
mask = (indices == i)
trailed_spectrum[:, i] = np.sum(lum*mask, axis=1)
# convolve with instrumental and local line profiles
# TODO: realistic Line Profile Treatment
# For now we assume every element has same intrinsic
# line profile
bin_width = (v_max-v_min)/(nbins-1)
profile_width_in_bins = np.sqrt(v_macro**2 + v_inst**2) / bin_width
gauss_kernel = Gaussian1DKernel(stddev=profile_width_in_bins, mode='linear_interp')
for i in range(phase.size):
trailed_spectrum[i, :] = convolve(trailed_spectrum[i, :], gauss_kernel, boundary='extend')
return bins, trailed_spectrum
def get_extrema(x, w=2, threshold=0.01):
"""
Return the valid range in pixels.
This function tries to find the first local maximum and minimum and select
the range between those. It can run into trouble if the first/last pixel
are the min/max; most of the code is dedicated to these corner cases
"""
from astropy.convolution import convolve, Gaussian1DKernel
sm = convolve(x, Gaussian1DKernel(w), boundary='extend')
pos = np.diff(sm) > threshold
# force first point to match second point
# smoothing can affect edges
if pos[0] != pos[1]:
pos[0] = pos[1]
if pos[0] != pos[2]:
pos[:2] = pos[2]
# Find the first point at which the slope is positive
# (this is very near the 0/0 point)
pospt = np.argmax(pos)
if np.count_nonzero(pos) < 5+4:
# implies there were no extrema found
return 0,x.size
elif pospt == 0:
# find first *negative* extremum
negpt = np.argmin(pos)
pos[:negpt] = False
pospt = np.argmax(pos)
if pospt < negpt:
pospt = pos.size - 1
return negpt,pospt
else:
return 0,pospt
def moment_masking(cube, kernel_size, clip=5, dilations=1):
'''
'''
if not signal_id_flag:
raise ImportError("signal-id is not installed."
" This function is not available.")
smooth_data = convolve(cube.filled_data[:], gauss_kern(kernel_size))
fake_mask = LazyMask(np.isfinite, cube=cube)
smooth_cube = SpectralCube(data=smooth_data, wcs=cube.wcs, mask=fake_mask)
smooth_scale = Noise(smooth_cube).scale
mask = (smooth_cube > (clip * smooth_scale)).include()
# Now dilate the mask once
dilate_struct = nd.generate_binary_structure(3, 3)
mask = nd.binary_dilation(mask, structure=dilate_struct,
iterations=dilations)
return mask
def resample(spectrum, resampled_header_broadcast, label_col=None, convolve=False, normalize=True):
"""Resamples the spectrum so that the x-axis starts at low and ends at high, while
keeping the delta between the wavelengths"""
resampled_header = resampled_header_broadcast.value
if label_col is not None:
logger.debug(spectrum.columns)
without_label = spectrum.drop(label_col, axis=1)
without_label.columns = pd.to_numeric(without_label.columns, errors="ignore")
else:
without_label = spectrum
if convolve:
to_interpolate = convolution.convolve(without_label.iloc[0].values, convolution.Gaussian1DKernel(7),
boundary="extend")
else:
to_interpolate = without_label.iloc[0].values
logger.debug(without_label)
interpolated = np.interp(resampled_header, without_label.columns.values, to_interpolate)
interpolated = interpolated[3:-3] # remove some weird artefacts that might happen because of convo/interpolation
if normalize:
interpolated = prep.minmax_scale([interpolated], axis=1)
logger.debug("Interpolated:%s", interpolated)
interpolated_df = pd.DataFrame(data=interpolated, columns=resampled_header[3:-3], index=spectrum.index.values)
if label_col is not None:
interpolated_df[label_col] = spectrum[label_col]
return interpolated_df
def convolveSquaredGaussian(self, image, gaussSigma):
if (type(gaussSigma) is int) or (type(gaussSigma) is float):
gaussSigma = np.array([gaussSigma, gaussSigma])
gRow = conv.Gaussian1DKernel(gaussSigma[0])
gSqRow = conv.CustomKernel(np.power(gRow.array, 2))
gCol = conv.Gaussian1DKernel(gaussSigma[1])
gSqCol = conv.CustomKernel(np.power(gCol.array, 2))
convImage = np.copy(image)
for rowNum in range(0, len(image)):
convImage[rowNum] = conv.convolve(convImage[rowNum], gSqRow, boundary=None)
for col in range(0, len(image.T)):
convImage[:,col] = conv.convolve(convImage[:,col], gSqCol, boundary=None)
return convImage
def mk_pix_stau(self, spec, kbin=22.*u.km/u.s, debug=False, **kwargs):
""" Generate the smoothed tau array for kinematic tests
Parameters
----------
spec: Spectrum1D class
Input spectrum
velo is expected to have been filled already
fill: bool (True)
Fill the dictionary with some items that other kin programs may need
Returns
-------
out_kin : dict
Dictionary of kinematic measurements
JXP on 11 Dec 2014
"""
# Calcualte dv
imn = np.argmin( np.fabs(spec.velo) )
dv = np.abs( spec.velo[imn] - spec.velo[imn+1] )
# Test for bad pixels
pixmin = np.argmin( np.fabs( spec.velo-self.vmnx[0] ) )
pixmax = np.argmin( np.fabs( spec.velo-self.vmnx[1] ) )
pix = np.arange(pixmin, pixmax+1)
npix = len(pix)
badzero=np.where((spec.flux[pix] == 0) & (spec.sig[pix] <= 0))[0]
if len(badzero) > 0:
if np.max(badzero)-np.min(badzero) >= 5:
raise ValueError('orig_kin: too many or too large sections of bad data')
spec.flux[pix[badzero]] = np.mean(np.array([spec.flux[pix[np.min(badzero)-1]],
spec.flux[pix[np.max(badzero)+1]]]))
xdb.set_trace() # Should add sig too
# Generate the tau array
tau = np.zeros(npix)
gd = np.where((spec.flux[pix] > spec.sig[pix]/2.) &
(spec.sig[pix] > 0.) )
if len(gd) == 0:
raise ValueError('orig_kin: Profile too saturated.')
tau[gd] = np.log(1./spec.flux[pix[gd]])
sat = (pix == pix)
sat[gd] = False
tau[sat] = np.log(2./spec.sig[pix[sat]])
# Smooth
nbin = (np.round(kbin/dv)).value
kernel = Box1DKernel(nbin, mode='center')
stau = convolve(tau, kernel, boundary='fill', fill_value=0.)
if debug is True:
xdb.xplot(spec.velo[pix], tau, stau)
# Fill
self.stau = stau
self.pix = pix
def smooth(continuum, wo_continuum):
total = wo_continuum + continuum
g = Gaussian1DKernel(stddev=2)
continuum = convolve(continuum, g, boundary='extend')
wo_continuum = total - continuum
return continuum, wo_continuum
def filter_direct(wavelength_range, image):
# Make the Mexican hat kernel and convolve
# The values given to the kernels should be 1.7328, 2.3963, 3.5270
# for S, M and L.
mex = MexicanHat2DKernel(get_pixFWHM(wavelength_range))
mex_convol = convolve(image, mex, boundary = 'extend')
return mex_convol
def convolve_images():
for image in input_images:
hdulist = fits.open(input_images[image])
data = hdulist[2].data
coords = [data.X_IMAGE, data.Y_IMAGE]
convolved_image = 'g'+ input_images[image]
outfile = convolve(coords, kernel)
fits.writeto(convolved_image,np.array(outfile))
print np.shape(outfile)
def box_smooth(self, nbox, preserve=False, **kwargs):
""" Box car smooth the spectrum
Parameters
----------
nbox: int
Number of pixels to smooth over
preserve: bool (False)
If True, perform a convolution to ensure the new spectrum
has the same number of pixels as the original.
**kwargs: dict
If preserve=True, these keywords are passed on to
astropy.convoution.convolve
Returns
-------
A new XSpectrum1D instance of the smoothed spectrum
"""
if preserve:
from astropy.convolution import convolve, Box1DKernel
new_fx = convolve(self.flux, Box1DKernel(nbox), **kwargs)
new_sig = convolve(self.sig, Box1DKernel(nbox), **kwargs)
new_wv = self.wavelength
else:
# Truncate arrays as need be
npix = len(self.flux)
try:
new_npix = npix // nbox # New division
except ZeroDivisionError:
raise ZeroDivisionError('Dividing by zero..')
orig_pix = np.arange(new_npix * nbox)
# Rebin (mean)
new_wv = liu.scipy_rebin(self.wavelength[orig_pix], new_npix)
new_fx = liu.scipy_rebin(self.flux[orig_pix], new_npix)
if self.sig_is_set:
new_sig = liu.scipy_rebin(
self.sig[orig_pix], new_npix) / np.sqrt(nbox)
else:
new_sig = None
# Return
return XSpectrum1D.from_tuple(
(new_wv, new_fx, new_sig), meta=self.meta.copy())
def snrmap_fast(array, fwhm, nproc=None, plot=False, verbose=True):
""" Serial implementation of the S/N map generation function. To be used as
a quick proxy of the snrmap generated using the small samples statistics
definition.
Parameters
----------
array : array_like
Input frame.
fwhm : float
Size in pixels of the FWHM.
nproc : int or None
Number of processes for parallel computing.
plot : {False, True}, bool optional
If True plots the SNR map.
verbose: {True, False}
Chooses whether to print results or not.
Returns
-------
snrmap : array_like
Frame with the same size as the input frame with each pixel.
"""
if verbose: start_time = time_ini()
if not array.ndim==2:
raise TypeError('Input array is not a 2d array or image.')
cy, cx = frame_center(array)
tophat_kernel = Tophat2DKernel(fwhm/2.)
array = convolve(array, tophat_kernel)
sizey, sizex = array.shape
snrmap = np.zeros_like(array)
width = min(sizey,sizex)/2 - 1.5*fwhm
mask = get_annulus(array, (fwhm/2)+1, width-1)
mask = np.ma.make_mask(mask)
yy, xx = np.where(mask)
coords = [(x,y) for (x,y) in zip(xx,yy)]
if nproc is None:
nproc = int((cpu_count()/2)) # Hyper-threading doubles the # of cores
if nproc==1:
for y,x in zip(yy,xx):
snrmap[y,x] = _snr_approx(array, (x,y), fwhm, cy, cx)[2]
elif nproc>1:
pool = Pool(processes=int(nproc))
res = pool.map(eval_func_tuple, itt.izip(itt.repeat(_snr_approx),
itt.repeat(array),
coords, itt.repeat(fwhm),
itt.repeat(cy),
itt.repeat(cx)))
res = np.array(res)
pool.close()
yy = res[:,0]
xx = res[:,1]
snr = res[:,2]
snrmap[yy.astype('int'), xx.astype('int')] = snr
if plot: pp_subplots(snrmap, colorb=True, title='SNRmap')
if verbose:
print("S/N map created using {:} processes.".format(nproc))
timing(start_time)
return snrmap
def SDR(x, y, prot, yerr=0, sep='peaks', retSD=True):
"""
Calculates the SDR of lightcurve data from any source.
x = time data (days)
y = (normalized) flux
yerr = errors associated with fluxes (default value = 0)
sep = 'dips' or 'peaks'
"""
dt = x - np.roll(x, 1)
dt = dt[1:]
ddt = np.median(dt)
if np.isnan(ddt) == True:
ddt = 0.1
width = int(np.round(prot / (ddt * 8)))
if width == 0:
width = 1
y = convolve(y, Box1DKernel(width), 'wrap')
pwidth = 0.02 # peak width must be greater than Kepler cadence (30 mins)
if sep == 'peaks':
peaks = sig.find_peaks(y, width=pwidth, prominence=yerr)[0]
elif sep == 'dips':
peaks = sig.find_peaks(-y, width=pwidth, prominence=yerr)[0]
xpeaks = [x[i] for i in peaks]
ypeaks = [y[i] for i in peaks]
psep = xpeaks - np.roll(xpeaks, 1)
psep = psep[1:]
psep = np.round(psep, 13)
psep, counts = np.unique(psep, return_counts=True)
if retSD == False:
return xpeaks, ypeaks, psep, y
elif retSD == True:
tdb = 0
tsn = 0
if len(psep) > 0:
for sep in psep:
if sep > 0.8 * prot:
tsn += 1
else:
tdb += 1
#print(tsn,tdb)
if tdb > 0 and tsn > 0:
sdr = np.log10(tsn / tdb)
if sdr > 2.0:
sdr = 2.0
elif sdr < -2.0:
sdr = -2.0
elif tdb == 0 and tsn != 0:
sdr = 2.0
elif tsn == 0 and tdb != 0:
sdr = -2.0
else:
sdr = 0.0
return sdr
else:
raise Exception("retSD must be True or False")
channel = 40
print(vit[channel])
#import_donnees(simu, co=False, c18o=False, c16o=True)
nom = "beta_12_rad_250"
cont_co = lect_donnees(simu, nom, mol="co")
cont_13 = lect_donnees(simu, nom, mol="13co")
cont_18 = lect_donnees(simu, nom, mol="c18o")
cont = cont_13
up, down = maxima(cont)
gauss = Gaussian1DKernel(5)
max_up, max_down = up[channel, :200], down[channel, :200]
max_up, max_down = convolve(
np.concatenate(([max_up[0]] * 10, max_up, [max_up[-1]] * 10)),
gauss)[10:-10], convolve(
np.concatenate(
([max_down[0]] * 10, max_down, [max_down[-1]] * 10)),
gauss)[10:-10]
t_1 = np.zeros(np.shape(cont[:, :, channel]))
t_2 = np.zeros(np.shape(cont[:, :, channel]))
for x in range(0, len(t_1[:, 0])):
for y in range(0, len(t_1[0, :])):
t_1[y, x], t_2[y, x] = sol(x - 201, y - 201, psi=psi)
#fit des données avec "direct mapping ..."
r, h, vit_reel = position(max_up, max_down, angle, vit[channel])
def sym_center(image,rmax):
'''
finds the center of circular symmetry of a single image.
INPUTS:
image - image in the form of a 2D array
rmax - the maximum radius you want it to check from a given center pixel for standard deviations
when determining if that pixel is a good center or not.
OUTPUTS:
xc,yc - center of circular symmetry of the image
'''
global xMax
global yMax
#smoothing before finding approximate center
#helps avoid issues with cosmic rays
gauss = conv.Gaussian2DKernel(stddev=6)
new_im = conv.convolve(image, gauss)
#find approximate center of image
xMax = 0
yMax = 0
max_val = 0
for y in range(centerY-200,centerY+200):
for x in range(centerX-200,centerX+200):
if new_im[y][x] > max_val:
xMax = x
yMax = y
max_val = new_im[y][x]
#create 21 by 21 grid of values showing whether each possible pixel in the grid is a good center
#lower values mean better center
grid = np.zeros((21,21))
for y in range(21):
print("starting row " + str(y))
for x in range(21):
yc = yMax - (10) + y
xc = xMax - (10) + x
radii = make_radial_profile(image, xc, yc)
for k in range(rmax+1):
ids = np.where((radii>=k) & (radii<k+1))
for a in range(ids[0].shape[0]):
ids[0][a] = ids[0][a] + yMax-50
for b in range(ids[1].shape[0]):
ids[1][b] = ids[1][b] + xMax-50
sd = np.nanstd(image[ids])
grid[y][x] = grid[y][x] + sd/abs(np.nanmedian(image[ids]))
print("created grid")
#fitting a 2D gaussian to the grid to determine actual 'best' center
maxval = np.nanmax(grid)
data = grid*-1
data = data+maxval
p = fitgaussian(data)
xcc = p[2]
ycc = p[1]
print("grid centered at " + str(xcc)+","+str(ycc))
#converting position from small grid to actual image:
xc = xMax - (21/2.-0.5) + xcc
yc = yMax - (21/2.-0.5) + ycc
print('Finishing sym_center; center of image is ' + str(xc)+","+str(yc))
return (xc, yc)
def ontype(self, event):
zabs = np.double(0.)
self.ax.draw_artist(self.ax)
# when the user hits 'r': clear the axes and plot the original spectrum
if event.key == 'r':
self.ax.cla()
self.ax.step(self.wave, self.flux, 'k-', linewidth=1)
self.ax.set_xlabel('Wavelength')
self.ax.set_ylabel('Flux')
xr = [np.min(self.wave), np.max(self.wave)]
yr = [np.min(self.flux), np.max(self.flux)]
self.ax.set_ylim([yr[0], yr[1]])
self.ax.set_xlim([xr[0], xr[1]])
# Set top y max
elif event.key == 't':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
self.ax.set_ylim([ylim[0], event.ydata])
self.ax.set_xlim(xlim)
plt.draw()
elif event.key == 'Z':
print('Testing to see if we can get a pop up window to work')
self.set_redshift()
self.DrawLineList(self.label)
elif event.key == 'j':
lambda_rest, LineList = self.identify_line_GUI()
print(lambda_rest, LineList)
self.zabs = (event.xdata - lambda_rest) / lambda_rest
self.label = LineList
self.DrawLineList(self.label)
print('Target Redshfit set at : ' + np.str(self.zabs))
# Manage and plot multiple absorbers
#Clunky GUI to plot lines
elif event.key == 'K':
self.manage_identified_absorbers()
#Load a saved linelist
elif event.key == '0':
self.load_linelist_GUI()
self.manage_identified_absorbers()
#Load pre identified individual absorbers
elif event.key == '+':
#filename=self.read_identified_linelist_GUI()
#print(filename)
#self.plot_identified_linelist(filename)
self.load_linelist_GUI()
self.manage_identified_absorbers()
self.fig.canvas.draw()
plt.draw()
# Set top y min
elif event.key == 'b':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
self.ax.set_ylim([event.ydata, ylim[1]])
self.ax.set_xlim(xlim)
plt.draw()
# Smooth spectrum
elif event.key == 'S':
self.vel[0] += 2
Filter_size = np.int(self.vel[0])
self.smoothed_spectrum = convolve(
self.flux,
Box1DKernel(Filter_size)) #medfilt(flux,np.int(Filter_size))
self.specplot()
plt.draw()
#Unsmooth Spectrum
elif event.key == 'U':
self.vel[0] -= 2
if self.vel[0] <= 0:
self.vel[0] = 1
Filter_size = np.int(self.vel[0])
self.smoothed_spectrum = convolve(
self.flux,
Box1DKernel(Filter_size)) #medfilt(flux,np.int(Filter_size))
self.specplot()
plt.draw()
# Set X max
elif event.key == 'X':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
self.ax.set_xlim([xlim[0], event.xdata])
self.ax.set_ylim(ylim)
plt.draw()
# Set x min
elif event.key == 'x':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
self.ax.set_xlim([event.xdata, xlim[1]])
self.ax.set_ylim(ylim)
plt.draw()
# Set pan spectrum
elif event.key == ']':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
delx = (xlim[1] - xlim[0])
self.ax.set_xlim([xlim[1], xlim[1] + delx])
self.ax.set_ylim(ylim)
plt.draw()
# Set pan spectrum
elif event.key == '[':
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
delx = (xlim[1] - xlim[0])
self.ax.set_xlim([xlim[0] - delx, xlim[0]])
self.ax.set_ylim(ylim)
plt.draw()
# Compute Equivalent Width between two points
elif event.key == 'E':
#ekeycounts +=1
self.lam_lim = np.append(self.lam_lim, event.xdata)
self.lam_ylim = np.append(self.lam_ylim, event.ydata)
# Keep running tab of all E clicks
eclick = len(self.lam_lim)
#self.specplot()
self.ax.plot(event.xdata,
event.ydata,
'rs',
ms=5,
picker=5,
label='EW_pt',
markeredgecolor='k')
#self.fig.canvas.draw()
if eclick == 2:
# Check if the wave entries are monotonously increasing
tab = self.lam_lim.argsort()
EW, sig_EW, cont, wave_slice = self.compute_EW(
self.wave / (1. + zabs), self.flux, self.lam_lim[tab],
self.lam_ylim[tab], self.error)
EW = np.array(EW) * 1000.
sig_EW = np.array(sig_EW) * 1000.
self.ax.plot(wave_slice, cont, 'r--')
#ipdb.set_trace()
print(
'---------------------- Equivalent Width -------------------------------------'
)
Wval = 'EW [mAA]: ' + '%.1f' % EW + ' +/- ' + '%.1f' % sig_EW
self.ax.text(np.mean([self.lam_lim]),
np.max(self.lam_ylim) + 0.2,
Wval,
rotation=90,
verticalalignment='bottom')
print(Wval)
print(
'---------------------------------------------------------------------------'
)
self.lam_lim = []
self.lam_ylim = []
plt.draw()
self.fig.canvas.draw()
# Fit a Gaussian
elif event.key == 'F':
self.FXval = np.append(self.FXval, event.xdata)
self.FYval = np.append(self.FYval, event.ydata)
fclick = len(self.FXval)
self.ax.plot(event.xdata,
event.ydata,
'rs',
ms=5,
picker=5,
label='EW_pt',
markeredgecolor='k')
self.fig.canvas.draw()
plt.show()
#Start Fitting
if fclick == 3:
# First fit a quick continuum
qtq = np.where(((self.wave / (1. + zabs)) >= self.FXval[0])
& ((self.wave / (1. + zabs)) <= self.FXval[2]))
ww = self.wave[qtq] / (1. + zabs)
flux1 = self.flux[qtq]
spline = splrep(np.append(self.FXval[0], self.FXval[2]),
np.append(self.FYval[0], self.FYval[2]),
k=1)
continuum = splev(ww, spline)
# Check if it is an absorption or emission line
if ((self.FYval[1] < self.FYval[0]) &
(self.FYval[1] < self.FYval[2])):
ydata = 1. - (flux1 / continuum)
gmodel = Model(gaussian)
result = gmodel.fit(ydata,
x=ww,
amp=self.FYval[1] - self.FYval[1],
cen=self.FXval[2],
wid=0.5 *
(self.FXval[2] - self.FXval[0]))
Final_fit = (1. - result.best_fit) * continuum
else:
ydata = (flux1 / continuum)
gmodel = Model(gaussian)
result = gmodel.fit(ydata,
x=ww,
amp=self.FYval[1] - self.FYval[1],
cen=self.FXval[2],
wid=0.5 *
(self.FXval[2] - self.FXval[0]))
Final_fit = result.best_fit * continuum
print(result.fit_report())
model_fit = self.ax.plot(ww, Final_fit, 'r-')
plt.draw()
print("Gaussian Fit")
FXval = []
FYval = []
#If the user presses 'h': The help is printed on the screen
elif event.key == 'h':
print('''
---------------------------------------------------------------------------
This is an interactive 1D spectrum viewer.
The help scene activates by pressing h on the plot.
The program only works properly if none of the toolbar buttons in the figure is activated.
It also needs pysimpleGUI code to be installed.
https://pysimplegui.readthedocs.io/en/latest/
Useful Keystrokes:
Keystrokes:
r : Reset Spectrum and replot to default settings.
h : Prints this help window.
x or X : Set xmin, xmax
b or t : Set ymin, ymax
[ or ] : Pan left or right
s or S. : Smooth or Unsmooth spectra
E : Two E keystrokes will compute rest frame equivalent width at a defined region
F : Three keystrokes to fit a Gaussian profile. [Currently not drawing on the spectrum]
#GUI ELEMENTS [WARNING UNSTABLE]
Works with TkAGG backend and pysimplegui
Z : pop up window to select absorber redshift and linelist
j : pop up window to select a corresponding rest frame transition and linelist
K : pop up window to select multiple absorber lines and plot them
0 : pop up window to select identified absorber list to show with 1d spectrum
+ : pop up window to select filename of pre-identified list of individual absorbers and plot them
q : Quit Program.
---------------------------------------------------------------------------
Written By: Rongmon Bordoloi August 2020.
HEALTH WARNING: The GUI implementation is still in alpha version and is quite unstable.
User must be careful to make sure that they exit individual GUIs first by pressing the correct button
before closing the plot window.
---------------------------------------------------------------------------
import matplotlib
matplotlib.use('TkAgg')
from linetools.spectra.xspectrum1d import XSpectrum1D
from GUIs import rb_plot_spec as r
sp=XSpectrum1D.from_file('PG0832+251_nbin3_coadd.fits')
r.rb_plot_spec(sp.wavelength.value,sp.flux.value,sp.sig.value)
''')
# Making sure any drawn line list remains drawn
self.DrawLineList(self.label)
q = np.where(self.zabs_list['List'] != 'None')
if len(q[0] > 0):
self.draw_any_linelist()
plt.draw()
self.fig.canvas.draw()
# -*- coding: utf-8 -*- import matplotlib.pyplot as plt from astropy.io import fits from astropy.convolution import Gaussian2DKernel, MexicanHat2DKernel, convolve import os # Variables for the filename path = "/home/abeelen/Herschel/DDT_mustdo_5/PLCK_SZ_G004.5-19.6-1/" filename = "OD1271_0x50012da8L_SpirePhotoLargeScan_PLCK_SZ_G004.5-19.6-1_destriped_PMW.fits" # Load the data fits_data = fits.getdata(path+filename,"image") # Make the Mexican hat filter mex = MexicanHat2DKernel(2, x_size=5, y_size=5) z = convolve(fits_data, mex, boundary = 'None') # Make the gaussian filter #gauss = Gaussian2DKernel(stddev=2) #z = convolve(fits_data, gauss, boundary='extend') # Plot the figure pixels = None fig, main_axes = plt.subplots() #main_axes.imshow(fits_data, origin="lower", interpolation="None") main_axes.imshow(z, origin="lower", interpolation="None") plt.show() #header = fits_data #print header
def smooth_image_toresolution(fn, outfn, target_resolution, clobber=False,
verbose=True, write=True):
"""
Smooth images with known beam size to a target beam size
Parameters
----------
fn : str
outfn : str
target_resolution : `~astropy.units.quantity.Quantity`
A degree-equivalent value that specifies the beam size in the output
image
verbose : bool
Print messages at each step?
clobber : bool
Overwrite files if they exist?
write : bool
Write the file to the output filename?
Returns
-------
f : `~astropy.io.fits.PrimaryHDU`
The smoothed FITS image with appropriately updated BMAJ/BMIN
"""
f = fits.open(fn)
header = f[0].header
native_beamsize = header['BMAJ']*u.deg
if header['BMIN'] != header['BMAJ']:
warnings.warn("Asymmetric beam not well-supported: "
"the images should be smoothed to have symmetric beams "
"prior to using this task")
kernelsize = ((target_resolution*FWHM_TO_SIGMA)**2 -
(native_beamsize*FWHM_TO_SIGMA)**2)**0.5
if kernelsize.value < 0:
raise ValueError("Cannot smooth to target resolution: "
"smaller than current resolution.")
platescale = FITS_header_tools.header_to_platescale(f[0].header,
use_units=True)
kernelsize_pixels = (kernelsize/platescale).decompose()
if not kernelsize_pixels.unit.is_equivalent(u.dimensionless_unscaled):
raise ValueError("Kernel size not in valid units")
kernel = convolution.Gaussian2DKernel(kernelsize_pixels.value)
if verbose:
print("Convolving with {0}-pixel kernel".format(kernelsize_pixels))
if kernelsize_pixels > 15:
sm = convolution.convolve_fft(f[0].data, kernel, interpolate_nan=True)
else:
sm = convolution.convolve(f[0].data, kernel)
f[0].data = sm
comment = "Smoothed with {0:03f}\" kernel".format(kernelsize.to(u.arcsec).value)
f[0].header['BMAJ'] = (target_resolution.to(u.deg).value, comment)
f[0].header['BMIN'] = (target_resolution.to(u.deg).value, comment)
if write:
f.writeto(outfn, clobber=clobber)
return f
def Smoo(self, smoo_scale):
""" smooth the input power using a convolve function """
g = Gaussian1DKernel(stddev=smoo_scale)
self.power = convolve(self.power, g, boundary='extend')
plt.matshow(z_mdt, cmap="RdBu", vmin=vmin, vmax=vmax)
plt.title("Before")
plt.matshow(z_gmdt, cmap="RdBu", vmin=vmin, vmax=vmax)
plt.title("Before")
print("extending MDT and GMDT ...")
# NOTE: Gaussian widths/range optimized for 5 km grid
widths = [3, 5, 7, 9, 11]
z_mdt = extend_field(z_mdt, mask, gauss_widths=widths, taper=None)
z_gmdt = extend_field(z_gmdt, mask, gauss_widths=widths, taper=None)
# Smooth artefacts including GLs with zeros over land
z_mdt[mask == 1] = 0.0
z_gmdt[mask == 1] = 0.0
z_mdt = convolve(z_mdt, Gaussian2DKernel(3), boundary="extend")
z_gmdt = convolve(z_gmdt, Gaussian2DKernel(3), boundary="extend")
z_mdt[mask == 1] = np.nan
z_gmdt[mask == 1] = np.nan
plt.matshow(z_mdt - np.nanmean(z_mdt), cmap="RdBu", vmin=-1.5, vmax=1.5)
plt.title("After")
plt.colorbar()
plt.matshow(z_gmdt - np.nanmean(z_gmdt), cmap="RdBu", vmin=-1.5, vmax=1.5)
plt.title("After")
plt.colorbar()
# --- Regrid all fields to Cube grid and save --- #
# Regrid fields
def main():
"""
The purpose of this code is to compare the Vs+V1+V1n map
(this is currently the top middle panel in Fig 7) with the
Vs+V2+V1n map. This is one of the plots the referee wanted
to see. I think they think that this will display the rotation
better. I'm not entirely sure.
This code is completely based on the codes --
1. stitche_vel_vdisp_cube.py and 2. contours_from_linefits.py.
"""
# Generate the Vs + V2 + V1n map
#genmap()
# Read in the new map and make contours
map_vel_comp2_hdu = fits.open(gaussfits_dir + 'vel_cube_comp2.fits')
map_vel_comp2_new_hdu = fits.open(gaussfits_dir + 'vel_cube_vs_v2_v1n.fits')
map_vel_comp2 = map_vel_comp2_hdu[0].data
map_vel_comp2_new = map_vel_comp2_new_hdu[0].data
# Diagnostic figures
# Do not delete this code block. Useful for checking.
"""
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.imshow(map_vel_comp2, origin='lower', vmin=-350, vmax=350)
ax2.imshow(map_vel_comp2_new, origin='lower', vmin=-350, vmax=350)
plt.show()
"""
# ------- Contours --------- #
# Plotting
# Change MPL RC params first to stop using TeX for all text
# becasue I can't use bold text with TeX.
mpl.rcParams['text.usetex'] = False
# Bold text
f = FontProperties()
f.set_weight('bold')
# read in i band SDSS image
sdss_i, wcs_sdss = vcm.get_sdss('i')
# read in lzifu output file
lzifu_hdulist, wcs_lzifu = vcm.get_lzifu_products()
# plot sdss image
fig, ax = vcm.plot_sdss_image(sdss_i, wcs_sdss)
# draw contours
x = np.arange(58)
y = np.arange(58)
X, Y = np.meshgrid(x,y)
# get colormap
colorbrewer_cm = vcm.get_colorbrewer_cm('blue2yellow')
# select contour map to plot and set the variables
# set the variables, levels, and limits below
con_map = map_vel_comp2_new
# apply min and max limits
minlim = -500
maxlim = 500
minidx = np.where(con_map < minlim)
maxidx = np.where(con_map > maxlim)
con_map[minidx] = np.nan
con_map[maxidx] = np.nan
levels = np.array([-350, -250, -200, -150, -100, 0, 100, 150, 200, 250, 350]) # vel both comp
# try smoothing the map to get smoother contours
# define kernel
kernel = Gaussian2DKernel(stddev=0.9)
con_map = convolve(con_map, kernel, boundary='extend')
c = ax.contour(X, Y, con_map, transform=ax.get_transform(wcs_lzifu),\
levels=levels, cmap=colorbrewer_cm, linewidths=2.0, interpolation='None')
# add colorbar inside figure
cbaxes = inset_axes(ax, width='30%', height='3%', loc=8, bbox_to_anchor=[0.02, 0.08, 1, 1], bbox_transform=ax.transAxes)
cb = plt.colorbar(c, cax=cbaxes, ticks=[min(levels), max(levels)], orientation='horizontal')
cb.ax.get_children()[0].set_linewidths(16.0)
cb.ax.set_xlabel(r'$\mathrm{Radial\ Velocity\ [km\, s^{-1}]}$', fontsize=15)
plt.show()
return None
def main():
# ----------------------- Preliminary stuff ----------------------- #
ext_root = "romansim_prism_"
img_basename = '5deg_'
img_filt = 'Y106_'
exptime1 = '_900s'
exptime2 = '_1800s'
exptime3 = '_3600s'
all_exptimes = [exptime1, exptime2, exptime3]
# ----------------------- Using emcee ----------------------- #
# Labels for corner and trace plots
label_list_sn = [r'$z$', r'$Day$', r'$A_V [mag]$']
# Set jump sizes # ONLY FOR INITIAL POSITION SETUP
jump_size_z = 0.1
jump_size_av = 0.5 # magnitudes
jump_size_day = 3 # days
# Setup dims and walkers
nwalkers = 500
niter = 1000
ndim_sn = 3
# ----------------------- Loop over all simulated and extracted SN spectra ----------------------- #
# Arrays to loop over
pointings = np.arange(1, 30)
detectors = np.arange(1, 19, 1)
for pt in pointings:
for det in detectors:
img_suffix = img_filt + str(pt) + '_' + str(det)
# --------------- Read in sed.lst
sedlst_header = ['segid', 'sed_path']
sedlst_path = roman_slitless_dir + '/pylinear_lst_files/' + 'sed_' + img_suffix + '.lst'
sedlst = np.genfromtxt(sedlst_path,
dtype=None,
names=sedlst_header,
encoding='ascii')
print("Read in sed.lst from:", sedlst_path)
print("Number of spectra in file:", len(sedlst))
# --------------- Read in the extracted spectra
# For all exposure times
ext_spec_filename1 = ext_spectra_dir + ext_root + img_suffix + exptime1 + '_x1d.fits'
ext_hdu1 = fits.open(ext_spec_filename1)
print("Read in extracted spectra from:", ext_spec_filename1)
ext_spec_filename2 = ext_spectra_dir + ext_root + img_suffix + exptime2 + '_x1d.fits'
ext_hdu2 = fits.open(ext_spec_filename2)
print("Read in extracted spectra from:", ext_spec_filename2)
ext_spec_filename3 = ext_spectra_dir + ext_root + img_suffix + exptime3 + '_x1d.fits'
ext_hdu3 = fits.open(ext_spec_filename3)
print("Read in extracted spectra from:", ext_spec_filename3)
print('\n')
all_hdus = [ext_hdu1, ext_hdu2, ext_hdu3]
# --------------- loop and find all SN segids
all_sn_segids = []
for i in range(len(sedlst)):
if 'salt' in sedlst['sed_path'][i]:
all_sn_segids.append(sedlst['segid'][i])
print('ALL SN segids in this file:', all_sn_segids)
# --------------- Loop over all extracted files and SN in each file
expcount = 0
for ext_hdu in all_hdus:
for segid in all_sn_segids:
#if segid == 188 and img_suffix == 'Y106_0_17':
# print('Skipping SN', segid, 'in img_suffix', img_suffix)
# continue
print("\nFitting SegID:", segid, "with exposure time:",
all_exptimes[expcount])
# ----- Get spectrum
segid_idx = int(np.where(sedlst['segid'] == segid)[0])
template_name = os.path.basename(
sedlst['sed_path'][segid_idx])
template_inputs = get_template_inputs(
template_name) # needed for plotting
wav = ext_hdu[('SOURCE', segid)].data['wavelength']
flam = ext_hdu[('SOURCE', segid
)].data['flam'] * pylinear_flam_scale_fac
ferr_lo = ext_hdu[
('SOURCE',
segid)].data['flounc'] * pylinear_flam_scale_fac
ferr_hi = ext_hdu[
('SOURCE',
segid)].data['fhiunc'] * pylinear_flam_scale_fac
# Smooth with boxcar
smoothing_width_pix = 5
sf = convolve(flam, Box1DKernel(smoothing_width_pix))
# ----- Check SNR
snr = get_snr(wav, flam)
print("SNR for this spectrum:", "{:.2f}".format(snr),
get_snr(wav, sf))
if snr < 3.0:
print("Skipping due to low SNR.")
continue
# ----- Set noise level based on snr
#noise_lvl = 1/snr
# Create ferr array
#ferr = noise_lvl * flam
ferr = (ferr_lo + ferr_hi) / 2.0
# ----- Get optimal starting position
z_prior, phase_prior, av_prior = get_optimal_position(
wav, flam, ferr)
rsn_init = np.array([z_prior, phase_prior, av_prior])
# redshift, day relative to peak, and dust extinction
# generating ball of walkers about optimal position defined above
pos_sn = np.zeros(shape=(nwalkers, ndim_sn))
for i in range(nwalkers):
# ---------- For SN
rsn0 = float(rsn_init[0] +
jump_size_z * np.random.normal(size=1))
rsn1 = int(rsn_init[1] +
jump_size_day * np.random.normal(size=1))
rsn2 = float(rsn_init[2] +
jump_size_av * np.random.normal(size=1))
rsn = np.array([rsn0, rsn1, rsn2])
pos_sn[i] = rsn
# ----- Clip data at the ends
wav_idx = np.where((wav > 7600) & (wav < 18000))[0]
sn_wav = wav[wav_idx]
sn_flam = flam[wav_idx]
sn_ferr = ferr[wav_idx]
# ----- Set up args
args_sn = [sn_wav, sn_flam, sn_ferr]
print("logpost at starting position for SN:")
print(logpost_sn(rsn_init, sn_wav, sn_flam, sn_ferr))
print("Starting position:", rsn_init)
# ----- Now run emcee on SN
snstr = str(
segid) + '_' + img_suffix + all_exptimes[expcount]
emcee_savefile = results_dir + \
'emcee_sampler_sn' + snstr + '.h5'
if not os.path.isfile(emcee_savefile):
backend = emcee.backends.HDFBackend(emcee_savefile)
backend.reset(nwalkers, ndim_sn)
with Pool(4) as pool:
sampler = emcee.EnsembleSampler(nwalkers,
ndim_sn,
logpost_sn,
args=args_sn,
pool=pool,
backend=backend)
sampler.run_mcmc(pos_sn, niter, progress=True)
print(f"{bcolors.GREEN}")
print("Finished running emcee.")
print("Mean acceptance Fraction:",
np.mean(sampler.acceptance_fraction), "\n")
print(f"{bcolors.ENDC}")
# ---------- Stuff needed for plotting
truth_dict = {}
truth_dict['z'] = template_inputs[0]
truth_dict['phase'] = template_inputs[1]
truth_dict['Av'] = template_inputs[2]
read_pickle_make_plots_sn('sn' + snstr, ndim_sn,
args_sn, label_list_sn,
truth_dict, results_dir)
print("Finished plotting results.")
expcount += 1
# --------------- close all open fits files
ext_hdu1.close()
ext_hdu2.close()
ext_hdu3.close()
return None
def find_lyman_break(SpectralDataObject):
#smooth the data first because its noisy
gauss_kernel = Gaussian1DKernel(5)
smoothed_data = convolve(SpectralDataObject, gauss_kernel)
def plot():
redshift = 1
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
# Load stuff
fittingobj = load(parfile)
lin = fittingobj.get_lin()
qsomag = fittingobj.get_qsomag()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
galcnt = fittingobj.get_galcnt()
galspc = fittingobj.get_galspc()
wavlen = fittingobj.get_wavlen()
fig, ax = plt.subplots(figsize=figsize(1, vscale=0.9))
# ax.plot(wavlen,wavlen*fluxtmp,color='black')
# import lineid_plot
# line_wave = [1216,
# 1549,
# 1909,
# 2798,
# 4861,
# 6563,
# 18744]
# line_names = [r'Ly$\alpha$',
# r'C\,{\sc iv}',
# r'C\,{\sc iii}]',
# r'Mg\,{\sc ii}',
# r'H$\beta$',
# r'H$\alpha$',
# r'Pa$\alpha$']
# lineid_plot.plot_line_ids(wavlen, wavlen*fluxtmp, line_wave, line_names, ax=ax, arrow_tip=10000)
plslp1 = -0.478
plslp2 = -0.199
plbrk = 2402
bbt = 1306
bbflxnrm = 3.673
elscal = 1.240
scahal = 0.713
galfra = 0.4
bcnrm = 0.135
ebv = 0.0
flux = np.zeros(len(wavlen), dtype=np.float)
flxnrm = parfile['quasar']['flxnrm']
wavnrm = parfile['quasar']['wavnrm']
# Define normalisation constant to ensure continuity at wavbrk
const2 = flxnrm / (wavnrm**(-plslp2))
const1 = const2 * ((plbrk**(-plslp2)) / (plbrk**(-plslp1)))
wavnumbrk = wav2num(wavlen, plbrk)
flux[:wavnumbrk] = flux[:wavnumbrk] + pl(wavlen[:wavnumbrk], plslp1,
const1)
flux[wavnumbrk:] = flux[wavnumbrk:] + pl(wavlen[wavnumbrk:], plslp2,
const2)
# Now add steeper power-law component for sub-Lyman-alpha wavelengths
# Define normalisation constant to ensure continuity at wavbrk
plbrk_tmp = parfile['quasar']['pl_steep']['brk']
plslp_tmp = plslp1 + parfile['quasar']['pl_steep']['step']
plbrknum_tmp = wav2num(wavlen, plbrk_tmp)
const_tmp = flux[plbrknum_tmp] / (plbrk_tmp**-plslp_tmp)
flux[:plbrknum_tmp] = pl(wavlen[:plbrknum_tmp], plslp_tmp, const_tmp)
flux_pl = flux * 1.0
# Hot blackbody ---------------------------------------------------
bbwavnrm = parfile['quasar']['bb']['wavnrm']
flux = flux + bb(
wavlen * u.AA, bbt * u.K, bbflxnrm, bbwavnrm * u.AA, units='freq')
flux_bb = bb(wavlen * u.AA,
bbt * u.K,
bbflxnrm,
bbwavnrm * u.AA,
units='freq')
# Balmer continuum --------------------------------------------------
# Add Balmer Continuum and blur to simulate effect of bulk-velocity
# shifts comparable to those present in emission lines
# Determine height of power-law continuum at wavelength wbcnrm to allow
# correct scaling of Balmer continuum contribution
wbcnrm = parfile['quasar']['balmercont']['wbcnrm']
wbedge = parfile['quasar']['balmercont']['wbedge']
tbc = parfile['quasar']['balmercont']['tbc']
taube = parfile['quasar']['balmercont']['taube']
vfwhm = parfile['quasar']['balmercont']['vfwhm']
# mean wavelength increment
winc = np.diff(wavlen).mean()
cfact = flux[wav2num(wavlen, wbcnrm)]
flux = flux / cfact
flux_bc = bc(wavlen=wavlen * u.AA,
tbb=tbc * u.K,
fnorm=bcnrm,
taube=taube,
wavbe=wbedge * u.AA,
wnorm=wbcnrm * u.AA)
vsigma = vfwhm * (u.km / u.s) / 2.35
wsigma = wbedge * u.AA * vsigma / const.c
wsigma = wsigma.to(u.AA)
psigma = wsigma / (winc * u.AA)
# Performs a simple Gaussian smooth with dispersion psigma pixels
gauss = Gaussian1DKernel(stddev=psigma)
flux_bc = convolve(flux_bc, gauss)
flux = flux + flux_bc
#-----------------------------------------------------------------
# Now convert to flux per unit wavelength
# Presumably the emission line spectrum and galaxy spectrum
# are already in flux per unit wavelength.
# c / lambda^2 conversion
flux = flux * (u.erg / u.s / u.cm**2 / u.Hz)
flux = flux.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
scale = flux[wav2num(wavlen, wavnrm)]
flux = flxnrm * flux / scale
flux_pl = flux_pl * (u.erg / u.s / u.cm**2 / u.Hz)
flux_pl = flux_pl.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_pl = flxnrm * flux_pl / scale
flux_bb = flux_bb * (u.erg / u.s / u.cm**2 / u.Hz)
flux_bb = flux_bb.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_bb = flxnrm * flux_bb / scale
flux_bc = flux_bc * (u.erg / u.s / u.cm**2 / u.Hz)
flux_bc = flux_bc.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_bc = flxnrm * flux_bc / scale
# Emission lines -------------------------------------------------
linwav, linval, conval = lin[:, 0], lin[:, 1], lin[:, 2]
# Normalise such that continuum flux at wavnrm equal to that
# of the reference continuum at wavnrm
inorm = wav2num(wavlen, wavnrm)
scale = flux[inorm]
flux = conval[inorm] * flux / scale
flux_pl = conval[inorm] * flux_pl / scale
flux_bb = conval[inorm] * flux_bb / scale
flux_bc = conval[inorm] * flux_bc / scale
# Calculate Baldwin Effect Scaling for Halpha
zbenrm = parfile['quasar']['el']['zbenrm']
beslp = parfile['quasar']['el']['beslp']
# Line added to stop enormous BE evolution at low z
zval = np.max([redshift, zbenrm])
# I think this is the absolute magnitude of the SDSS sample as
# a function of redshift, which is not the same as how the
# absolute magnitude of a object of a given flux changes
# as a function of redshift
qsomag_itp = interp1d(qsomag[:, 0], qsomag[:, 1])
# Absolute magnitude at redshift z minus
# normalisation absolute magnitude
vallum = qsomag_itp(zval) - qsomag_itp(zbenrm)
# Convert to luminosity
vallum = 10.0**(-0.4 * vallum)
scabe = vallum**(-beslp)
flux_el = np.zeros_like(flux)
flux_el[:whmin] = linval[:whmin] * np.abs(
elscal) * flux[:whmin] / conval[:whmin]
flux_el[whmax:] = linval[whmax:] * np.abs(
elscal) * flux[whmax:] / conval[whmax:]
flux[:whmin] = flux[:whmin] + linval[:whmin] * np.abs(
elscal) * flux[:whmin] / conval[:whmin]
flux[whmax:] = flux[whmax:] + linval[whmax:] * np.abs(
elscal) * flux[whmax:] / conval[whmax:]
# Scaling for Ha with Baldwin effect
scatmp = elscal * scahal / scabe
flux_el[whmin:whmax] = linval[whmin:whmax] * np.abs(
scatmp) * flux[whmin:whmax] / conval[whmin:whmax]
flux[whmin:whmax] = flux[whmin:whmax] + \
linval[whmin:whmax] * np.abs(scatmp) * flux[whmin:whmax] / conval[whmin:whmax]
gznorm = parfile['gal']['znrm']
gplind = parfile['gal']['plind']
# Determine fraction of galaxy sed to add to unreddened quasar SED
qsocnt = np.sum(flux[ignmin:ignmax])
# Factor cscale just to bring input galaxy and quasar flux zero-points equal
cscale = qsocnt / galcnt
# Find absolute magnitude of quasar at redshift z
vallum = qsomag_itp(redshift)
# Find absolute magnitude of quasar at redshift gznorm
galnrm = qsomag_itp(gznorm)
# Subtract supplied normalisation absolute magnitude
vallum = vallum - galnrm
# Convert to a luminosity
vallum = 10.0**(-0.4 * vallum)
# Luminosity scaling
scaval = vallum**(gplind - 1.0)
scagal = (galfra / (1.0 - galfra)) * scaval
flux_gal = cscale * scagal * galspc
# flux = flux + cscale * scagal * galspc
ax.plot(wavlen[:wavnumbrk],
wavlen[:wavnumbrk] * flux_pl[:wavnumbrk],
color=cs[1],
label='Accretion Disc')
# ax.fill_between(wavlen[:wavnumbrk], wavlen[:wavnumbrk]*flux_pl[:wavnumbrk], facecolor=cs[1], alpha=0.2)
ax.plot(wavlen[wavnumbrk:],
wavlen[wavnumbrk:] * flux_pl[wavnumbrk:],
color=cs[0],
label='Accretion Disc')
# ax.fill_between(wavlen[wavnumbrk:], wavlen[wavnumbrk:]*flux_pl[wavnumbrk:], facecolor=cs[0], alpha=0.2)
ax.plot(wavlen, wavlen * (flux_bc), color=cs[2], label='Balmer Continuum')
# ax.fill_between(wavlen, wavlen*(flux_bc), facecolor=cs[2], alpha=0.2)
ax.plot(wavlen, wavlen * (flux_bb), color=cs[4], label='Hot Dust')
# ax.fill_between(wavlen, wavlen*(flux_bb), facecolor=cs[4], alpha=0.2)
# ax.plot(wavlen, wavlen*(flux_gal), color=cs[3], label='Galaxy')
# ax.fill_between(wavlen, wavlen*(flux_gal), facecolor=cs[3], alpha=0.2)
ax.plot(wavlen, wavlen * flux, color='black', label='Total')
ax.legend()
ax.set_xlim(1216, 20000)
ax.set_ylim(0, 10000)
ax.set_ylabel(r'${\lambda}F_{\lambda}$ [Arbitary Units]')
ax.set_xlabel(r'Wavelength $\lambda$ [${\rm \AA}$]')
plt.tight_layout()
plt.subplots_adjust(top=0.85)
fig.savefig('/home/lc585/thesis/figures/chapter05/sed_model.pdf')
plt.show()
return None
def smooth(x, window_len=9, window='hanning'):
"""
Smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal.
Parameters
----------
x : array_like
the input signal
window_len : int
The length of the smoothing window
window : str
The type of window from 'flat', 'hanning', 'hamming', 'bartlett',
'blackman'
'flat' window will produce a moving average smoothing.
Returns
-------
out : The smoothed signal
Example
-------
>>> t=linspace(-2,2,0.1)
>>> x=sin(t)+randn(len(t))*0.1
>>> y=smooth(x)
See Also
--------
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman,
numpy.convolve
scipy.signal.lfilter
TODO: the window parameter could be the window itself if an array
instead of a string
"""
if isinstance(x, list):
x = np.array(x)
if x.ndim != 1:
raise ValueError("smooth only accepts 1 dimension arrays.")
if len(x) < window_len:
print("length of x: ", len(x))
print("window_len: ", window_len)
raise ValueError("Input vector needs to be bigger than window size.")
if window_len < 3:
return x
if (window_len % 2) == 0:
window_len = window_len + 1
if window not in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window is on of 'flat', 'hanning', \
'hamming', 'bartlett', 'blackman'")
if window == 'flat': # moving average
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = convolve(x, w / w.sum(), normalize_kernel=False, boundary='extend')
# return the smoothed signal
return y
def convolution_smooth(spectrum, kernel):
"""
Apply a convolution based smoothing to the spectrum. The kernel must be one
of the 1D kernels defined in `astropy.convolution`.
This method can be used along but also is used by other specific methods below.
If the spectrum uncertainty exists and is StdDevUncertainty, VarianceUncertainty or InverseVariance
then the errors will be propagated through the convolution using a standard propagation of errors. The
covariance is not considered, currently.
Parameters
----------
spectrum : `~specutils.Spectrum1D`
The `~specutils.Spectrum1D` object to which the smoothing will be applied.
kernel : `astropy.convolution.Kernel1D` subclass or array.
The convolution based smoothing kernel - anything that `astropy.convolution.convolve` accepts.
Returns
-------
spectrum : `~specutils.Spectrum1D`
Output `~specutils.Spectrum1D` which is copy of the one passed in with the updated flux.
Raises
------
ValueError
In the case that ``spectrum`` and ``kernel`` are not the correct types.
"""
# Parameter checks
if not isinstance(spectrum, Spectrum1D):
raise ValueError('The spectrum parameter must be a Spectrum1D object')
# Get the flux of the input spectrum
flux = spectrum.flux
# Smooth based on the input kernel
smoothed_flux = convolution.convolve(flux, kernel)
# Propagate the uncertainty if it exists...
uncertainty = copy.deepcopy(spectrum.uncertainty)
if uncertainty is not None:
if isinstance(uncertainty, StdDevUncertainty):
# Convert
values = uncertainty.array
ivar_values = 1 / values**2
# Propagate
prop_ivar_values = convolution.convolve(ivar_values, kernel)
# Put back in
uncertainty.array = 1 / np.sqrt(prop_ivar_values)
elif isinstance(uncertainty, VarianceUncertainty):
# Convert
values = uncertainty.array
ivar_values = 1 / values
# Propagate
prop_ivar_values = convolution.convolve(ivar_values, kernel)
# Put back in
uncertainty.array = 1 / prop_ivar_values
elif isinstance(uncertainty, InverseVariance):
# Convert
ivar_values = uncertainty.array
# Propagate
prop_ivar_values = convolution.convolve(ivar_values, kernel)
# Put back in
uncertainty.array = prop_ivar_values
else:
uncertainty = None
warnings.warn("Uncertainty is {} but convolutional error propagation is not defined for that type. Uncertainty will be dropped in the convolved spectrum.".format(type(uncertainty)),
AstropyUserWarning)
# Return a new object with the smoothed flux.
return Spectrum1D(flux=u.Quantity(smoothed_flux, spectrum.unit),
spectral_axis=u.Quantity(spectrum.spectral_axis,
spectrum.spectral_axis_unit),
wcs=spectrum.wcs,
uncertainty=uncertainty,
velocity_convention=spectrum.velocity_convention,
rest_value=spectrum.rest_value)
def frame_filter_lowpass(array, mode='gauss', median_size=5, fwhm_size=5,
gauss_mode='conv', kernel_sz=None, psf=None, mask=None,
iterate=True, **kwargs):
"""
Low-pass filtering of input frame depending on parameter ``mode``.
Parameters
----------
array : numpy ndarray
Input array, 2d frame.
mode : {'median', 'gauss', 'moff', 'psf'}, str optional
Type of low-pass filtering.
median_size : int, optional
Size of the median box for filtering the low-pass median filter.
fwhm_size : float, optional
Size of the Gaussian kernel for the low-pass Gaussian filter.
gauss_mode : {'conv', 'convfft'}, str optional
'conv' uses the multidimensional gaussian filter from scipy.ndimage and
'convfft' uses the fft convolution with a 2d Gaussian kernel.
kernel_sz: int or None, optional
Size of the kernel in pixels for 2D Gaussian and Moffat convolutions.
If None, astropy.convolution will automatically consider 8*radius
kernel sizes.
psf: numpy ndarray, optional
Input normalised and centered psf, 2d frame. Should be provided if
mode is set to 'psf'.
mask: numpy ndarray, optional
Binary mask indicating where the low-pass filtered image should be
interpolated with astropy.convolution. This option can be useful if the
low-pass filtered image is aimed to capture low-spatial frequency sky
signal, while avoiding a stellar halo (set to one in the binary mask).
Note: only works with Gaussian kernel or PSF convolution.
iterate: bool, opt
If the first convolution leaves nans, whether to continue replacing
nans by interpolation until they are all replaced.
**kwargs : dict
Passed through to the astropy.convolution.convolve or convolve_fft
function.
Returns
-------
filtered : numpy ndarray
Low-pass filtered image.
"""
if array.ndim != 2:
raise TypeError('Input array is not a frame or 2d array.')
if not isinstance(median_size, int):
raise ValueError('`Median_size` must be integer')
if mask is not None:
if mode== 'median':
msg="Masking not available for median filter"
if mask.shape != array.shape:
msg = "Mask dimensions should be the same as array"
raise TypeError(msg)
if mode == 'median':
# creating the low_pass filtered (median) image
filtered = median_filter(array, median_size, mode='nearest')
elif mode == 'gauss':
# 2d Gaussian filter
sigma = fwhm_size * gaussian_fwhm_to_sigma
if gauss_mode == 'conv':
filtered = convolve(array, Gaussian2DKernel(x_stddev=sigma,
x_size=kernel_sz,
y_size=kernel_sz),
mask=mask, **kwargs)
if iterate and np.sum(np.isnan(filtered))>0:
filtered = interp_nan(filtered,
Gaussian2DKernel(x_stddev=sigma,
x_size=kernel_sz,
y_size=kernel_sz),
convolve=convolve)
elif gauss_mode == 'convfft':
# FFT Convolution with a 2d gaussian kernel created with Astropy.
filtered = convolve_fft(array, Gaussian2DKernel(x_stddev=sigma,
x_size=kernel_sz,
y_size=kernel_sz),
mask=mask, **kwargs)
if iterate and np.sum(np.isnan(filtered))>0:
filtered = interp_nan(filtered,
Gaussian2DKernel(x_stddev=sigma,
x_size=kernel_sz,
y_size=kernel_sz),
convolve=convolve_fft, **kwargs)
else:
raise TypeError('2d Gaussian filter mode not recognized')
elif mode == 'psf':
if psf is None:
raise TypeError('psf should be provided for convolution')
elif psf.ndim != 2:
raise TypeError('Input psf is not a frame or 2d array.')
if psf.shape[-1] > array.shape[-1]:
raise TypeError('Input psf is larger than input array. Crop.')
# psf convolution
filtered = convolve_fft(array, psf, mask=mask, **kwargs)
if iterate and np.sum(np.isnan(filtered))>0:
filtered = interp_nan(filtered, psf, convolve=convolve_fft,
**kwargs)
else:
raise TypeError('Low-pass filter mode not recognized')
return filtered
def do_plot(self):
if self.plate != int(self.e1.get()) or self.mjd != int(self.e2.get()):
self.plate = int(self.e1.get())
self.mjd = int(self.e2.get())
self.fiber = int(self.e3.get())
self.znum = int(self.e5.get())
self.platepath = join(environ['BOSS_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
'spPlate-%s-%s.fits' % (self.plate, self.mjd))
hdu = fits.open(self.platepath)
self.specs = hdu[0].data
self.wave = 10**(hdu[0].header['COEFF0'] +
n.arange(hdu[0].header['NAXIS1']) *
hdu[0].header['COEFF1'])
self.models = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[2].data
self.fiberid = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate,
self.mjd)))[1].data.FIBERID
self.type1 = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.CLASS1
self.type2 = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.CLASS2
self.type3 = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.CLASS3
self.type4 = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.CLASS4
self.type5 = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.CLASS5
self.z = n.zeros((self.fiberid.shape[0],5))
self.z[:,0] = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.Z1
self.z[:,1] = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.Z2
self.z[:,2] = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.Z3
self.z[:,3] = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.Z4
self.z[:,4] = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate, self.mjd)))[1].data.Z5
self.zwarning = fits.open(join(environ['REDMONSTER_SPECTRO_REDUX'],
environ['RUN2D'], '%s' % self.plate,
environ['RUN1D'],
'redmonster-%s-%s.fits' %
(self.plate,
self.mjd)))[1].data.ZWARNING
else:
self.fiber = int(self.e3.get())
f = Figure(figsize=(10,6), dpi=100)
a = f.add_subplot(111)
loc = n.where(self.fiberid == self.fiber)[0]
if self.znum == 1:
z = self.z[loc[0],0]
thistype = self.type1[loc[0]]
elif self.znum == 2:
z = self.z[loc[0],1]
thistype = self.type2[loc[0]]
elif self.znum == 3:
z = self.z[loc[0],2]
thistype = self.type3[loc[0]]
elif self.znum == 4:
z = self.z[loc[0],3]
thistype = self.type4[loc[0]]
elif self.znum == 5:
z = self.z[loc[0],4]
thistype = self.type5[loc[0]]
if self.var.get() == 0:
if self.restframe.get() == 0:
a.plot(self.wave, self.specs[self.fiber], color='black')
elif self.restframe.get() == 1:
a.plot(self.wave/(1+self.z[loc][0]), self.specs[self.fiber],
color='black')
elif self.var.get() == 1:
smooth = self.e4.get()
if smooth is '':
if self.restframe.get() == 0:
a.plot(self.wave, self.specs[self.fiber], color='black')
elif self.restframe.get() == 1:
a.plot(self.wave/(1+z), self.specs[self.fiber],
color='black')
else:
if self.restframe.get() == 0:
a.plot(self.wave, convolve(self.specs[self.fiber],
Box1DKernel(int(smooth))),
color='black')
elif self.restframe.get() == 1:
a.plot(self.wave/(1+z), convolve(self.specs[self.fiber],
Box1DKernel(int(smooth))),
color='black')
# Overplot model
if len(loc) is not 0:
if self.restframe.get() == 0:
#a.plot(self.wave, self.models[loc[0]], color='black')
# This for when multiple models are in redmonster file
a.plot(self.wave, self.models[loc[0],self.znum-1],
color='cyan')
if self.ablines.get() == 1:
for i, line in enumerate(self.ablinelist):
if ((line*(1+z) > self.wave[0]) &
(line*(1+z) < self.wave[-1])):
a.axvline(line*(1+z), color='blue',
linestyle='--',
label=self.ablinenames[i])
if self.emlines.get() == 1:
for i, line in enumerate(self.emlinelist):
if (line*(1+z) > self.wave[0]) & (line*(1+z) < \
self.wave[-1]):
a.axvline(line*(1+z), color='red',
linestyle='--',
label=self.emlinenames[i])
if self.ablines.get() == 1 or self.emlines.get() == 1:
a.legend(prop={'size':10})
elif self.restframe.get() == 1:
a.plot(self.wave/(1+z), self.models[loc[0],self.znum-1],
color='cyan')
if self.ablines.get() == 1:
for i, line in enumerate(self.ablinelist):
if (line > self.wave[0]) & (line < self.wave[-1]):
a.axvline(line, color='blue', linestyle='--',
label=self.ablinenames[i])
if self.emlines.get() == 1:
for i, line in enumerate(self.emlinelist):
if (line > self.wave[0]) & (line < self.wave[-1]):
a.axvline(line, color='red', linestyle='--',
label=self.emlinenames[i])
if self.ablines.get() == 1 or self.emlines.get() == 1:
a.legend(prop={'size':10})
a.set_title('Plate %s Fiber %s: z=%s class=%s zwarning=%s' %
(self.plate, self.fiber, z, thistype,
self.zwarning[loc[0]]))
else:
print('Fiber %s is not in redmonster-%s-%s.fits' % \
(self.fiber, self.plate, self.mjd))
a.set_title('Plate %s Fiber %s' % (self.plate, self.fiber))
if self.restframe.get() == 1:
lower_data, upper_data = self.set_limits()
a.axis([self.wave[0]/(1+z)-100,self.wave[-1]/(1+z)+100,
lower_data,upper_data])
elif self.restframe.get() == 0:
lower_data, upper_data = self.set_limits()
a.axis([self.wave[0]-100,self.wave[-1]+100,lower_data,upper_data])
a.set_xlabel('Wavelength ($\AA$)')
a.set_ylabel('Flux ($10^{-17} erg\ cm^2 s^{-1} \AA^{-1}$)')
canvas = FigureCanvasTkAgg(f, master=self.root)
canvas.get_tk_widget().grid(row=0, column=5, rowspan=20)
toolbar_frame = Frame(self.root)
toolbar_frame.grid(row=20,column=5)
toolbar = NavigationToolbar2TkAgg( canvas, toolbar_frame )
canvas.show()
def w80eval(wl: np.ndarray,
spec: np.ndarray,
wl0: float,
smooth: float = None,
clip_negative_flux: bool = True) -> tuple:
"""
Evaluates the W80 parameter of a given emission fature.
Parameters
----------
wl : array-like
Wavelength vector.
spec : array-like
Flux vector.
wl0 : number
Central wavelength of the emission feature.
smooth : float
Smoothing sigma to apply after the cumulative sum.
clip_negative_flux : bool
Sets negative flux values to zero.
Returns
-------
w80 : float
The resulting w80 parameter.
r0 : float
r1 : float
velocity : numpy.ndarray
Velocity vector.
velocity_spectrum : numpy.ndarray
Spectrum in velocity coordinates.
Notes
-----
W80 is the width in velocity space which encompasses 80% of the
light emitted in a given spectral feature. It is widely used as
a proxy for identifying outflows of ionized gas in active galaxies.
For instance, see Zakamska+2014 MNRAS.
"""
velocity = (wl * units.angstrom).to(
units.km / units.s,
equivalencies=units.doppler_relativistic(wl0 * units.angstrom))
if not (spec == 0.0).all() and not np.isnan(spec).all():
y = ma.masked_invalid(spec)
if clip_negative_flux:
y = np.clip(y, a_min=0.0, a_max=None)
if smooth is not None:
kernel = Gaussian1DKernel(smooth)
y_mask = copy.deepcopy(y.mask)
y = ma.array(data=convolve(y, kernel=kernel, boundary='extend'),
mask=y_mask)
cumulative = cumtrapz(y[~y.mask], velocity[~y.mask], initial=0)[0]
if len(cumulative.shape) > 1:
raise ValueError(
f'cumulative must have only one dimension, but it has {len(cumulative.shape)}.'
)
cumulative /= cumulative.max()
r0 = velocity[(np.abs(cumulative - 0.1)).argsort()[0]].value
r1 = velocity[(np.abs(cumulative - 0.9)).argsort()[0]].value
w80 = r1 - r0
else:
w80, r0, r1 = np.nan, np.nan, np.nan
return w80, r0, r1, velocity, spec
c=colors[k],
source=True)
except:
print(
"\n\nLSST (Chang) p(z) not available. Plotting whitebook p(z) estimate for %.4lf.\n\n"
% ilims[ilim])
pl.plot(zs,
whitebook_pz(zs, ilims[ilim]),
label=ilim,
lw=1.5,
c=colors[k])
elg_pz = elg_pz(interp=True)
smooth_elgs = convolve(elg_pz(zs), Box1DKernel(30))
## Hildebrandt et al. (2009) -- measured dndz.
midz, pz_H09 = getpz_H09()
pz_H09 = interp1d(midz,
pz_H09,
kind='linear',
bounds_error=False,
fill_value=0.0)
## BX: 'khaki'
colors = [
'purple', 'lightskyblue', 'mediumseagreen', 'khaki', 'dodgerblue',
'darkgreen', 'r'
]
labels = [
def highpassflist(signal, highpassWidth):
g = Box1DKernel(highpassWidth)
return convolve(signal, g, boundary='extend')
def spectrometer_convolve(self, spec):
"""
This method convolves a spectrum with a wavelength-dependent spectral
resolving power R=lambda/delta lambda, where delta lambda is the FWHM of an
unresolved line at a wavelength lambda. This is accomplished by internally
changing the sampling of the spectrum, such that delta-lambda is sampled by
exactly a constant number of samples as a function of wavelength. The convolution
is then accomplished using the convolution theorem and knowledge that the kernel is
independent of wavelength.
Parameters
----------
spec: pandeia.engine.astro_spectrum.AstroSpectrum instance
The input spectrum to convolve
Returns
-------
spec: pandeia.engine.astro_spectrum.AstroSpectrum instance
Same dimensions as input spec, but now convolved according to the wavelength-dependent resolving
power of the instrument, if applicable. Same as the input spec in the case of no dispersion (i.e. imaging).
"""
r = self.get_resolving_power(spec.wave)
if r is None:
# no dispersion defined so return unmolested spectrum
return spec
dw_min = np.min(np.abs(spec.wave - np.roll(spec.wave, 1))) # find the minimum spacing between wavelengths
fwhm = spec.wave / r # FWHM of resolution element as a function of wavelength
fwhm_s = np.min(fwhm / dw_min) # find min of FWHM expressed in units of minimum spacing (i.e. sampling)
# but do not allow the sampling FWHM to be less than Nyquist
fwhm_s = np.max([2., fwhm_s])
# divide FWHM(wave) by the FWHM of the sampling to get sampling as a function of wavelength
ds = fwhm / fwhm_s
# use the min as a starting point; initialize array
w = np.min(spec.wave)
# it's much faster (~50%) to append to lists than np.array()'s
wave_constfwhm = []
# doing this as a loop is slow, but straightforward.
while w < np.max(spec.wave):
# use interpolation to get delta-wavelength from the sampling as a function of wavelength.
# this method is over 5x faster than the original use of scipy.interpolate.interp1d.
w += np.interp(w, spec.wave, ds)
wave_constfwhm.append(w)
wave_constfwhm.pop() # remove last point which is an extrapolation
wave_constfwhm = np.array(wave_constfwhm)
# interpolate the flux onto the new wavelength set
flux_constfwhm = spectrum_resample(spec.flux, spec.wave, wave_constfwhm)
# convolve the flux with a gaussian kernel; first convert the FWHM to sigma
sigma_s = fwhm_s / 2.3548
try:
# for astropy < 0.4
g = Gaussian1DKernel(width=sigma_s)
except TypeError:
# for astropy >= 0.4
g = Gaussian1DKernel(sigma_s)
# use boundary='extend' to set values outside the array to nearest array value.
# this is the best approximation in this case.
flux_conv = convolve(flux_constfwhm, g, normalize_kernel=True, boundary='extend')
flux_oldsampling = np.interp(spec.wave, wave_constfwhm, flux_conv)
spec.flux = flux_oldsampling
return spec
print('\n*****************************************************')
print('Objeto: ', filename)
print('RA: ', ra)
print('DEC: ', dec)
print('----------------------------------------------------')
### SPLUS ###
# Normalization
max_splus = hdu_splus[0].data.max()
norm_splus = hdu_splus[0].data / max_splus
# Gaussian
gauss_kernel = Gaussian2DKernel(1)
result1 = convolve(hdu_splus[0].data, gauss_kernel)
max_splus = result1.max()
result1 = result1 / max_splus
### SDSS ###
# Normalization
max_norm = hdu_sdss[0].data.max()
norm_sdss = hdu_sdss[0].data / max_norm
# Gaussian
gauss_kernel = Gaussian2DKernel(1)
result2 = convolve(hdu_sdss[0].data, gauss_kernel)
max_sdss = result2.max()
result2 = result2 / max_sdss
def astropy_smooth(x, window):
from astropy.convolution import convolve, Box1DKernel
kernel = Box1DKernel(window)
smoothed = convolve(x, kernel, boundary="extend", normalize_kernel=True)
return smoothed
def skyview_stamp(ra, decl,
survey='DSS2 Red',
scaling='Linear',
flip=True,
convolvewith=None,
forcefetch=False,
cachedir='~/.astrobase/stamp-cache',
timeout=10.0,
retry_failed=False,
savewcsheader=True,
verbose=False):
'''This is the internal version of the astroquery_skyview_stamp function.
Why this exists:
- SkyView queries don't accept timeouts (should put in a PR for this)
- we can drop the dependency on astroquery (but add another on requests)
flip = True will flip the image top to bottom.
if convolvewith is an astropy.convolution kernel:
http://docs.astropy.org/en/stable/convolution/kernels.html
this will return the stamp convolved with that kernel. This can be useful to
see effects of wide-field telescopes (like the HATNet and HATSouth lenses)
degrading the nominal 1 arcsec/px of DSS, causing blending of targets and
any variability.
cachedir points to the astrobase stamp-cache directory.
'''
stampdict = get_stamp(ra, decl,
survey=survey,
scaling=scaling,
forcefetch=forcefetch,
cachedir=cachedir,
timeout=timeout,
retry_failed=retry_failed,
verbose=verbose)
#
# DONE WITH FETCHING STUFF
#
if stampdict:
# open the frame
stampfits = pyfits.open(stampdict['fitsfile'])
header = stampfits[0].header
frame = stampfits[0].data
stampfits.close()
# finally, we can process the frame
if flip:
frame = np.flipud(frame)
if verbose:
LOGINFO('fetched stamp successfully for (%.3f, %.3f)'
% (ra, decl))
if convolvewith:
convolved = aconv.convolve(frame, convolvewith)
if savewcsheader:
return frame, header
else:
return frame
else:
if savewcsheader:
return frame, header
else:
return frame
else:
LOGERROR('could not fetch the requested stamp for '
'coords: (%.3f, %.3f) from survey: %s and scaling: %s'
% (ra, decl, survey, scaling))
return None
def plot_background(star, n_peaks=10):
"""
Creates a plot summarising the results of the fit background routine.
Parameters
----------
star : target.Target
the main pipeline Target class object
n_peaks : int
the number of peaks to highlight in the zoomed-in power spectrum
Results
-------
None
"""
from pysyd.functions import return_max, max_elements
from pysyd.models import harvey
fig = plt.figure(figsize=(12, 12))
# Time series data
ax1 = fig.add_subplot(3, 3, 1)
if star.lc:
ax1.plot(star.time, star.flux, 'w-')
ax1.set_xlim([min(star.time), max(star.time)])
ax1.set_title(r'$\rm Time \,\, series$')
ax1.set_xlabel(r'$\rm Time \,\, [days]$')
ax1.set_ylabel(r'$\rm Flux$')
# Initial background guesses
star.smooth_power = convolve(
star.random_pow,
Box1DKernel(int(np.ceil(star.fitbg['box_filter'] / star.resolution))))
ax2 = fig.add_subplot(3, 3, 2)
ax2.plot(star.frequency,
star.random_pow,
c='lightgrey',
zorder=0,
alpha=0.5)
ax2.plot(star.frequency[star.frequency < star.maxpower[0]],
star.random_pow[star.frequency < star.maxpower[0]],
'w-',
zorder=1)
ax2.plot(star.frequency[star.frequency > star.maxpower[1]],
star.random_pow[star.frequency > star.maxpower[1]],
'w-',
zorder=1)
ax2.plot(star.frequency[star.frequency < star.maxpower[0]],
star.smooth_power[star.frequency < star.maxpower[0]],
'r-',
linewidth=0.75,
zorder=2)
ax2.plot(star.frequency[star.frequency > star.maxpower[1]],
star.smooth_power[star.frequency > star.maxpower[1]],
'r-',
linewidth=0.75,
zorder=2)
for r in range(star.nlaws):
ax2.plot(star.frequency,
harvey(star.frequency,
[star.a_orig[r], star.b_orig[r], star.noise]),
color='blue',
linestyle=':',
linewidth=1.5,
zorder=4)
ax2.plot(star.frequency,
harvey(star.frequency, star.pars, total=True),
color='blue',
linewidth=2.,
zorder=5)
ax2.errorbar(star.bin_freq,
star.bin_pow,
yerr=star.bin_err,
color='lime',
markersize=0.,
fillstyle='none',
ls='None',
marker='D',
capsize=3,
ecolor='lime',
elinewidth=1,
capthick=2,
zorder=3)
for m, n in zip(star.mnu_orig, star.a_orig):
ax2.plot(m,
n,
color='blue',
fillstyle='none',
mew=3.0,
marker='s',
markersize=5.0)
ax2.axvline(star.maxpower[0],
color='darkorange',
linestyle='dashed',
linewidth=2.0,
zorder=1,
dashes=(5, 5))
ax2.axvline(star.maxpower[1],
color='darkorange',
linestyle='dashed',
linewidth=2.0,
zorder=1,
dashes=(5, 5))
ax2.axhline(star.noise,
color='blue',
linestyle='dashed',
linewidth=1.5,
zorder=3,
dashes=(5, 5))
ax2.set_xlim([min(star.frequency), max(star.frequency)])
ax2.set_ylim([min(star.power), max(star.power) * 1.25])
ax2.set_title(r'$\rm Initial \,\, guesses$')
ax2.set_xlabel(r'$\rm Frequency \,\, [\mu Hz]$')
ax2.set_ylabel(r'$\rm Power \,\, [ppm^{2} \mu Hz^{-1}]$')
ax2.set_xscale('log')
ax2.set_yscale('log')
# Fitted background
ax3 = fig.add_subplot(3, 3, 3)
ax3.plot(star.frequency,
star.random_pow,
c='lightgrey',
zorder=0,
alpha=0.5)
ax3.plot(star.frequency[star.frequency < star.maxpower[0]],
star.random_pow[star.frequency < star.maxpower[0]],
'w-',
linewidth=0.75,
zorder=1)
ax3.plot(star.frequency[star.frequency > star.maxpower[1]],
star.random_pow[star.frequency > star.maxpower[1]],
'w-',
linewidth=0.75,
zorder=1)
ax3.plot(star.frequency[star.frequency < star.maxpower[0]],
star.smooth_power[star.frequency < star.maxpower[0]],
'r-',
linewidth=0.75,
zorder=2)
ax3.plot(star.frequency[star.frequency > star.maxpower[1]],
star.smooth_power[star.frequency > star.maxpower[1]],
'r-',
linewidth=0.75,
zorder=2)
for r in range(star.nlaws):
ax3.plot(
star.frequency,
harvey(star.frequency,
[star.pars[2 * r], star.pars[2 * r + 1], star.pars[-1]]),
color='blue',
linestyle=':',
linewidth=1.5,
zorder=4)
ax3.plot(star.frequency,
harvey(star.frequency, star.pars, total=True),
color='blue',
linewidth=2.0,
zorder=5)
ax3.errorbar(star.bin_freq,
star.bin_pow,
yerr=star.bin_err,
color='lime',
markersize=0.0,
fillstyle='none',
ls='None',
marker='D',
capsize=3,
ecolor='lime',
elinewidth=1,
capthick=2,
zorder=3)
ax3.axvline(star.maxpower[0],
color='darkorange',
linestyle='dashed',
linewidth=2.0,
zorder=1,
dashes=(5, 5))
ax3.axvline(star.maxpower[1],
color='darkorange',
linestyle='dashed',
linewidth=2.0,
zorder=1,
dashes=(5, 5))
ax3.axhline(star.pars[-1],
color='blue',
linestyle='dashed',
linewidth=1.5,
zorder=3,
dashes=(5, 5))
ax3.plot(star.frequency,
star.pssm,
color='yellow',
linewidth=2.0,
linestyle='dashed',
zorder=6)
ax3.set_xlim([min(star.frequency), max(star.frequency)])
ax3.set_ylim([min(star.power), max(star.power) * 1.25])
ax3.set_title(r'$\rm Fitted \,\, model$')
ax3.set_xlabel(r'$\rm Frequency \,\, [\mu Hz]$')
ax3.set_ylabel(r'$\rm Power \,\, [ppm^{2} \mu Hz^{-1}]$')
ax3.set_xscale('log')
ax3.set_yscale('log')
# Smoothed power excess w/ gaussian
ax4 = fig.add_subplot(3, 3, 4)
ax4.plot(star.region_freq, star.region_pow, 'w-', zorder=0)
idx = return_max(star.region_freq, star.region_pow, index=True)
ax4.plot([star.region_freq[idx]], [star.region_pow[idx]],
color='red',
marker='s',
markersize=7.5,
zorder=0)
ax4.axvline([star.region_freq[idx]],
color='white',
linestyle='--',
linewidth=1.5,
zorder=0)
ax4.plot(star.new_freq, star.numax_fit, 'b-', zorder=3)
ax4.axvline(star.exp_numax,
color='blue',
linestyle=':',
linewidth=1.5,
zorder=2)
ax4.plot([star.exp_numax], [max(star.numax_fit)],
color='b',
marker='D',
markersize=7.5,
zorder=1)
ax4.set_title(r'$\rm Smoothed \,\, bg$-$\rm corrected \,\, PS$')
ax4.set_xlabel(r'$\rm Frequency \,\, [\mu Hz]$')
ax4.set_xlim([min(star.region_freq), max(star.region_freq)])
# Background-corrected power spectrum with n highest peaks
star.freq = star.frequency[star.params[star.name]['ps_mask']]
star.psd = star.bg_corr_smooth[star.params[star.name]['ps_mask']]
peaks_f, peaks_p = max_elements(star.freq, star.psd, n_peaks)
ax5 = fig.add_subplot(3, 3, 5)
ax5.plot(star.freq, star.psd, 'w-', zorder=0, linewidth=1.0)
ax5.scatter(peaks_f,
peaks_p,
s=25.0,
edgecolor='r',
marker='s',
facecolor='none',
linewidths=1.0)
ax5.set_title(r'$\rm Bg$-$\rm corrected \,\, PS$')
ax5.set_xlabel(r'$\rm Frequency \,\, [\mu Hz]$')
ax5.set_ylabel(r'$\rm Power$')
ax5.set_xlim([min(star.region_freq), max(star.region_freq)])
ax5.set_ylim([
min(star.psd) - 0.025 * (max(star.psd) - min(star.psd)),
max(star.psd) + 0.1 * (max(star.psd) - min(star.psd))
])
sig = 0.35 * star.exp_dnu / 2.35482
weights = 1. / (sig * np.sqrt(2. * np.pi)) * np.exp(
-(star.lag - star.exp_dnu)**2. / (2. * sig**2))
new_weights = weights / max(weights)
# ACF for determining dnu
ax6 = fig.add_subplot(3, 3, 6)
ax6.plot(star.lag, star.auto, 'w-', zorder=0, linewidth=1.)
ax6.scatter(star.peaks_l,
star.peaks_a,
s=30.0,
edgecolor='r',
marker='^',
facecolor='none',
linewidths=1.0)
ax6.axvline(star.exp_dnu,
color='red',
linestyle=':',
linewidth=1.5,
zorder=5)
ax6.axvline(star.obs_dnu,
color='lime',
linestyle='--',
linewidth=1.5,
zorder=2)
ax6.scatter(star.best_lag,
star.best_auto,
s=45.0,
edgecolor='lime',
marker='s',
facecolor='none',
linewidths=1.0)
ax6.plot(star.zoom_lag, star.zoom_auto, 'r-', zorder=5, linewidth=1.0)
ax6.plot(star.lag,
new_weights,
c='yellow',
linestyle=':',
zorder=0,
linewidth=1.0)
ax6.set_title(r'$\rm ACF \,\, for \,\, determining \,\, \Delta\nu$')
ax6.set_xlabel(r'$\rm Frequency \,\, separation \,\, [\mu Hz]$')
ax6.set_xlim([min(star.lag), max(star.lag)])
ax6.set_ylim([
min(star.auto) - 0.05 * (max(star.auto) - min(star.auto)),
max(star.auto) + 0.1 * (max(star.auto) - min(star.auto))
])
# dnu fit
ax7 = fig.add_subplot(3, 3, 7)
ax7.plot(star.zoom_lag, star.zoom_auto, 'w-', zorder=0, linewidth=1.0)
ax7.axvline(star.obs_dnu,
color='lime',
linestyle='--',
linewidth=1.5,
zorder=2)
ax7.plot(star.new_lag, star.dnu_fit, color='lime', linewidth=1.5)
ax7.axvline(star.exp_dnu,
color='red',
linestyle=':',
linewidth=1.5,
zorder=5)
ax7.set_title(r'$\rm \Delta\nu \,\, fit$')
ax7.set_xlabel(r'$\rm Frequency \,\, separation \,\, [\mu Hz]$')
ax7.annotate(r'$\Delta\nu = %.2f$' % star.obs_dnu,
xy=(0.025, 0.85),
xycoords="axes fraction",
fontsize=18,
color='lime')
ax7.set_xlim([min(star.zoom_lag), max(star.zoom_lag)])
if star.fitbg['interp_ech']:
interpolation = 'bilinear'
else:
interpolation = 'none'
# echelle diagram
ax8 = fig.add_subplot(3, 3, 8)
ax8.imshow(star.ech,
extent=star.extent,
interpolation=interpolation,
aspect='auto',
origin='lower',
cmap=plt.get_cmap('viridis'))
ax8.axvline([star.obs_dnu],
color='white',
linestyle='--',
linewidth=1.0,
dashes=(5, 5))
ax8.set_title(r'$\rm \grave{E}chelle \,\, diagram$')
ax8.set_xlabel(r'$\rm \nu \,\, mod \,\, %.2f \,\, [\mu Hz]$' %
star.obs_dnu)
ax8.set_ylabel(r'$\rm \nu \,\, [\mu Hz]$')
ax8.set_xlim([0.0, 2.0 * star.obs_dnu])
ax8.set_ylim([star.maxpower[0], star.maxpower[1]])
ax9 = fig.add_subplot(3, 3, 9)
ax9.plot(star.xax, star.yax, color='white', linestyle='-', linewidth=0.75)
ax9.set_title(r'$\rm Collapsed \,\, \grave{e}chelle \,\, diagram$')
ax9.set_xlabel(r'$\rm \nu \,\, mod \,\, %.2f \,\, [\mu Hz]$' %
star.obs_dnu)
ax9.set_ylabel(r'$\rm Collapsed \,\, power$')
ax9.set_xlim([0.0, 2.0 * star.obs_dnu])
ax9.set_ylim([
min(star.yax) - 0.025 * (max(star.yax) - min(star.yax)),
max(star.yax) + 0.05 * (max(star.yax) - min(star.yax))
])
plt.tight_layout()
if star.params['save']:
if star.fitbg['interp_ech']:
plt.savefig('%sbackground_smooth_echelle.png' %
star.params[star.name]['path'],
dpi=300)
else:
plt.savefig('%sbackground.png' % star.params[star.name]['path'],
dpi=300)
if star.params['show']:
plt.show()
plt.close()
dataset.OWA = OWA
# fileprefix="pyklip_parallelizedRDItest"
# parallelized.klip_dataset(dataset, outputdir=dir_test + "test_results/", fileprefix=fileprefix, annuli=annuli,
# subsections=subsections, numbasis=numbasis, maxnumbasis=maxnumbasis, mode="RDI",
# aligned_center=aligned_center, highpass=False, psf_library=psflib)
# fileprefix="pyklip_parallelizedADItest"
# parallelized.klip_dataset(dataset, outputdir=dir_test + "test_results/", fileprefix=fileprefix, annuli=annuli,
# subsections=subsections, numbasis=numbasis, maxnumbasis=maxnumbasis, mode="ADI",
# aligned_center=aligned_center, highpass=False)
# create a phony disk model and convovle it by the instrument psf
phony_disk_model = make_phony_disk(281)
model_convolved = convolve(phony_disk_model, instrument_psf, boundary="wrap")
fits.writeto(
dir_test + "test_results/psf.fits",
instrument_psf,
overwrite=True,
)
fits.writeto(
dir_test + "test_results/disk_model_before.fits",
phony_disk_model,
overwrite=True,
)
fits.writeto(
dir_test + "test_results/disk_model_after.fits",
def correct_MRS(input_model, straylight_model):
"""
Short Summary
-------------
Corrects the MIRI MRS data for straylight
Uses the straylight reference file
Parameter
----------
input_model: data model object
science data to be corrected
straylight_model: holds the straylight mask for the correction
Returns
-------
output: data model object
straylight-subtracted science data
"""
# Save some data parameterss for easy use later
nrows, ncols = input_model.data.shape
# mask is either 1 or 0
mask = straylight_model.data
x = float('nan')
# The straylight mask has values of 1 and 0. The straylight task uses data
# in-between the slices (also called slice gaps) of the MRS data to correct
# the science data in the slices. In the mask the pixels found in the gaps
# have a value of 1 and the science pixels have a value of 0.
# Create output as a copy of the input science data model
# sci_mask is the input science image * mask
# science regions = 0 (reference pixel are also = 0)
output = input_model.copy() # this is used in algorithm to
# find the straylight correction.
#_________________________________________________________________
# if there are nans remove them because they mess up the correction
index_inf = np.isinf(output.data).nonzero()
output.data[index_inf] = 0.0
mask[index_inf] = 0 # flag associated mask so we do not use any
# slice gaps that are nans, now data=0 .
#_________________________________________________________________
# flag bad pixels
mask_dq = input_model.dq.copy() # * mask # find DQ flags of the gap values
all_flags = (dqflags.pixel['DEAD'] + dqflags.pixel['HOT'])
# where are pixels set to any one of the all_flags cases
testflags = np.bitwise_and(mask_dq, all_flags)
# where are testflags ne 0 and mask == 1
bad_flags = np.where(testflags != 0)
mask[bad_flags] = 0
#_________________________________________________________________
sci_mask = output.data * mask #sci_maskcontains 0's in science regions of detector.
straylight_image = output.data * 0.0
#We Want Sci mask smoothed for GAP region with 3 X 3 box car filter
# Handle edge cases for boxcar smoothing, by determining the
# boxcar smoothing of the mask.
sci_ave = convolve(sci_mask, Box2DKernel(3))
mask_ave = convolve(mask, Box2DKernel(3))
# catch /0 cases
index = np.where(mask_ave == 0) # zero catches cases that would be #/0
# near edges values are 0.3333 0.6667 1
sci_ave[index] = 0
mask_ave[index] = 1
sci_smooth = sci_ave / mask_ave
x = np.arange(ncols)
# Loop over each row (0 to 1023)
for j in range(nrows):
row_mask = mask[j, :]
if (np.sum(row_mask) > 0):
# find the locations of slice gaps
#determine the data in the slice gaps
yuse = sci_smooth[j, np.where(row_mask == 1)]
#find the x locations of the slice gaps
xuse = x[np.where(row_mask == 1)]
#if(j ==1):
# print 'row ',j+1,yuse.shape,yuse
# print 'xuse',xuse,xuse.shape,type(xuse)
#find data in same gap area
nn = len(xuse)
#dx difference in adjacent slice gaps pixels --> used
# to find x limits of each gap
dx = xuse[1:nn] - xuse[0:nn - 1]
# Find the number of slice gaps in row
idx = np.asarray(np.where(dx > 1))
ngroups = len(idx[0]) + 1
# xlimits are the x values that mark limits of a slice gaps
xlimits = np.zeros(ngroups + 1)
i = 1
xlimits[0] = xuse[0]
for index in idx[0]:
xlimits[i] = xuse[index]
i = i + 1
xlimits[ngroups] = xuse[nn - 1] #+1
xg = np.zeros(ngroups)
yg = np.zeros(ngroups)
# loop over all slice gaps in a row
# find the mean y straylight value for each slice gaps
# also find the mean x value for this slice gap
for i in range(ngroups):
lower_limit = xlimits[i]
upper_limit = xlimits[i + 1]
igap = np.asarray(
np.where(
np.logical_and(xuse > lower_limit,
xuse <= upper_limit)))
ymean = np.mean(yuse[0, igap[0]])
xmean = np.mean(xuse[igap[0]])
yg[i] = ymean
xg[i] = xmean
# else entire row is zero
else:
xg = np.array([0, 1032])
yg = np.array([0, 0])
#using mean y value in slice gaps and x location of ymean
#interpolate what straylight contribution based on yg and xg
# for all points in row
for k in x:
if (x[k] >= xg[0] and x[k] <= xg[-1]):
ynew = np.interp(x[k], xg, yg)
else:
if x[k] < xg[0]:
ynew = yg[0] + (x[k] - xg[0]) * (yg[1] - yg[0]) / (xg[1] -
xg[0])
elif x[k] > xg[-1]:
ynew = yg[-1] + (x[k] - xg[-1]) * (yg[-1] - yg[-2]) / (
xg[-1] - xg[-2])
straylight_image[j, k] = ynew
# end loop over rows
straylight_image[straylight_image < 0] = 0
# pull out the science region (1024 pixel/row) to do boxcar smoothing on
simage = convolve(straylight_image, Box2DKernel(25))
# remove the straylight correction for the reference pixels
simage[:, 1028:1032] = 0.0
simage[:, 0:4] = 0.0
output.data = output.data - simage
return output
def ppxf_single(object_id,
z_init,
lambda_spec,
galaxy_lin,
error_lin,
cars_model,
emsub_specfile=None,
plotfile=None,
outfile=None,
outfile_spec=None,
mpoly=None,
apoly=None,
reflines=None):
# Speed of light
c = 299792.458
#h_spec = spec_hdu[0].header
#Ang_air = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
#s = 10**4/Ang_air
#n = 1 + 0.00008336624212083 + 0.02408926869968 / (130.1065924522 - s**2) + 0.000159740894897 / (38.92568793293 - s**2)
#lambda_spec = Ang_air*n
#wave = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
#lambda_spec = vactoair(wave)
#lambda_spec = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
# Crop to finite
use = (np.isfinite(galaxy_lin) & (galaxy_lin > 0.0))
# Making a "use" vector
use_indices = np.arange(galaxy_lin.shape[0])[use]
galaxy_lin = (galaxy_lin[use_indices.min():(use_indices.max() + 1)])[2:-3]
error_lin = (error_lin[use_indices.min():(use_indices.max() + 1)])[2:-3]
lambda_spec = (lambda_spec[use_indices.min():(use_indices.max() +
1)])[2:-3]
lamRange_gal = np.array([np.min(lambda_spec), np.max(lambda_spec)])
# New resolution estimate = 0.9 \AA (sigma), converting from sigma to FWHM
FWHM_gal = 2.355 * (np.max(lambda_spec) -
np.min(lambda_spec)) / len(lambda_spec)
print('FWHM', FWHM_gal)
lamRange_gal = lamRange_gal / (
1 + float(z_init)) # Compute approximate restframe wavelength range
FWHM_gal = FWHM_gal / (1 + float(z_init)) # Adjust resolution in Angstrom
sigma_gal = FWHM_gal / (2.3548 * 4500.0) * c # at ~4500 \AA
# log rebinning for the fits
galaxy, logLam_gal, velscale = util.log_rebin(np.around(lamRange_gal,
decimals=3),
galaxy_lin,
flux=True)
noise, logLam_noise, velscale = util.log_rebin(np.around(lamRange_gal,decimals=3), error_lin, \
velscale=velscale, flux=True)
# correcting for infinite or zero noise
noise[np.logical_or((noise == 0.0), np.isnan(noise))] = 1.0
galaxy[np.logical_or((galaxy < 0.0), np.isnan(galaxy))] = 0.0
if galaxy.shape != noise.shape:
galaxy = galaxy[:-1]
logLam_gal = logLam_gal[:-1]
# Define lamRange_temp and logLam_temp
#lamRange_temp, logLam_temp = setup_spectral_library_conroy(velscale[0], FWHM_gal)
# Construct a set of Gaussian emission line templates.
# Estimate the wavelength fitted range in the rest frame.
#
gas_templates, line_names, line_wave = util.emission_lines(
logLam_gal, lamRange_gal, FWHM_gal)
goodpixels = np.arange(galaxy.shape[0])
wave = np.exp(logLam_gal)
# crop very red end (only affects a very small subsample)
# goodpixels = goodpixels[wave <= 5300]
# exclude lines at the edges (where things can go very very wrong :))
include_lines = np.where((line_wave > (wave.min() + 10.0))
& (line_wave < (wave.max() - 10.0)))
if line_wave[include_lines[0]].shape[0] < line_wave.shape[0]:
line_wave = line_wave[include_lines[0]]
line_names = line_names[include_lines[0]]
gas_templates = gas_templates[:, include_lines[0]]
#reg_dim = stars_templates.shape[1:]
reg_dim = gas_templates.shape[1:]
templates = gas_templates
#np.hstack([gas_templates, gas_templates])
dv = 0 #(logLam_temp[0]-logLam_gal[0])*c # km/s
vel = 0 #np.log(z_init + 1)*c # Initial estimate of the galaxy velocity in km/s
#z = np.exp(vel/c) - 1 # Relation between velocity and redshift in pPXF
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
t = clock()
nNLines = gas_templates.shape[1]
component = [0] * nNLines
start_gas = [vel, 100.]
start = start_gas # adopt the same starting value for both gas (BLs and NLs) and stars
moments = [
2
] # fit (V,sig,h3,h4) for the stars and (V,sig) for the gas' broad and narrow components
fixed = None
## Additive polynomial degree
if apoly is None:
degree = int(
np.ceil((lamRange_gal[1] - lamRange_gal[0]) /
(100.0 * (1. + float(z_init)))))
else:
degree = int(apoly)
if mpoly is None: mdegree = 3
else: mdegree = int(mpoly)
# Trying: sigmas must be kinematically decoupled
# #Trying: velocities must be kinematically decoupled
#A_ineq = [[0,0,2,0,-1,0]]
#b_ineq = [0]
bounds_gas = [[-1000, 1000], [0, 1000]]
bounds = bounds_gas
pp = ppxf.ppxf(templates,
galaxy,
noise,
velscale,
start,
fixed=fixed,
plot=False,
moments=moments,
mdegree=mdegree,
degree=degree,
vsyst=dv,
reg_dim=reg_dim,
goodpixels=goodpixels,
bounds=bounds)
#component=component,
# redshift_to_newtonian:
# return (v-astropy.constants.c.to('km/s').value*redshift)/(1.0+redshift)
# "v" from above is actually the converted velocity which is
# (np.exp(v_out/c) - 1)*c
v_gas = pp.sol[0]
ev_gas = pp.error[0]
conv_vel_gas = (np.exp(pp.sol[0] / c) - 1) * c
vel_gas = (1 + z_init) * (conv_vel_gas - c * z_init)
sigma_gas = pp.sol[1] #first # is template, second # is the moment
esigma_gas = pp.error[1] #*np.sqrt(pp.chi2)
zfit_gas = (z_init + 1) * (1 + pp.sol[0] / c) - 1
zfit_stars = z_init
ezfit_gas = (z_init + 1) * pp.error[0] * np.sqrt(pp.chi2) / c
if plotfile is not None:
#
### All of the rest of this plots and outputs the results of the fit
### Feel free to comment anything out at will
#
maskedgalaxy = np.copy(galaxy)
lowSN = np.where(noise > (0.9 * np.max(noise)))
maskedgalaxy[lowSN] = np.nan
wave = np.exp(logLam_gal) * (1. + z_init) / (1. + zfit_stars)
fig = plt.figure(figsize=(12, 7))
ax1 = fig.add_subplot(211)
# plotting smoothed spectrum
smoothing_fact = 3
ax1.plot(wave,
convolve(maskedgalaxy, Box1DKernel(smoothing_fact)),
color='Gray',
linewidth=0.5)
ax1.plot(wave[goodpixels],
convolve(maskedgalaxy[goodpixels],
Box1DKernel(smoothing_fact)),
'k',
linewidth=1.)
label = "Best fit template from high res Conroy SSPs + emission lines at z={0:.3f}".format(
zfit_stars)
# overplot stellar templates alone
ax1.plot(wave, pp.bestfit, 'r', linewidth=1.0, alpha=0.75, label=label)
ax1.set_ylabel('Flux')
ax1.legend(loc='upper right', fontsize=10)
ax1.set_title(object_id)
xmin, xmax = ax1.get_xlim()
ax2 = fig.add_subplot(413, sharex=ax1, sharey=ax1)
# plotting emission lines if included in the fit
gas = pp.matrix[:, -nNLines:].dot(pp.weights[-nNLines:])
ax2.plot(wave, gas, 'b', linewidth=2,\
label = '$\sigma_{gas}$'+'={0:.0f}$\pm${1:.0f} km/s'.format(sigma_gas, esigma_gas)+', $V_{gas}$'+'={0:.0f}$\pm${1:.0f} km/s'.format(v_gas, ev_gas)) # overplot emission lines alone
cars_model = (cars_model[use_indices.min():(use_indices.max() +
1)])[2:-3]
ax2.plot(np.array(lambda_spec)/(1+z_init), cars_model, color='orange', linewidth=1,\
label = 'CARS Model')
#(lambda_spec[use_indices.min():(use_indices.max()+1)])[2:-3]
stars = pp.bestfit - gas
#if (ymax > 3.0*np.median(stars)): ymax = 3.0*np.median(stars)
#if (ymin < -0.5): ymin = -0.5
ax2.set_ylabel('Best Fits')
ax2.legend(loc='upper left', fontsize=10)
# Plotting the residuals
ax3 = fig.add_subplot(817, sharex=ax1)
ax3.plot(wave[goodpixels],
(convolve(maskedgalaxy, Box1DKernel(smoothing_fact)) -
pp.bestfit)[goodpixels],
'k',
label='Fit Residuals')
#ax3.set_yticks([-0.5,0,0.5])
ax3.set_ylabel('Residuals')
ax4 = fig.add_subplot(818, sharex=ax1)
ax4.plot(wave, noise, 'k', label='Flux Error')
ax4.set_ylabel('Noise')
ax4.set_xlabel('Rest Frame Wavelength [$\AA$]')
#ax4.set_yticks(np.arange(0,0.5,0.1))
'''if reflines is not None:
for i,w,label in zip(range(len(reflines)),reflines['wave'],reflines['label']):
if ((w > xmin) and (w < xmax)):
# ax1.text(w,ymin+(ymax-ymin)*(0.03+0.08*(i % 2)),'$\mathrm{'+label+'}$',fontsize=10,\
# horizontalalignment='center',\
# bbox=dict(boxstyle='round', facecolor='white', alpha=0.5))
print(label.decode("utf-8"))
ax1.text(w,ymin+(ymax-ymin)*(0.03+0.08*(i % 2)),'$\mathrm{'+label.decode("utf-8")+'}$',fontsize=10,\
horizontalalignment='center',\
bbox=dict(boxstyle='round', facecolor='white', alpha=0.5))
ax1.plot([w,w],[ymin,ymax],':k',alpha=0.5)'''
fig.subplots_adjust(hspace=0.05)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
#print('Saving figure to {0}'.format(plotfile))
ax1.set_xlim([6450, 6750])
ax2.set_xlim([6450, 6750])
#ymin, ymax = ax1.get_ylim([])
plt.savefig(plotfile, dpi=150)
plt.close()
return v_gas, vel_gas, sigma_gas, pp.chi2
# print('# id z_stars ez_stars sigma_stars esigma_stars z_gas ez_gas sigma_gas esigma_gas SN_median SN_rf_4000 SN_obs_8030 chi2dof')
print('{0:d} {1:.6f} {2:.6f} {3:.1f} {4:.1f} {5:.6f} {6:.6f} {7:.1f} {8:.1f} {9:.1f} {10:.1f} {11:.1f} {12:.2f}\n'.format(\
object_id,zfit_stars,ezfit_stars, sigma_stars,esigma_stars,\
zfit_gas, ezfit_gas, sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2))
## This prints the fit parameters to an open file object called outfile
if outfile is not None:
outfile.write('{0:d} {1:d} {2:.6f} {3:.6f} {4:.1f} {5:.1f} {6:.6f} {7:.6f} {8:.1f} {9:.1f} {10:.1f} {11:.1f} {12:.1f} {13:.2f}\n'.format(\
object_id, row2D, zfit_stars, ezfit_stars, sigma_stars,esigma_stars,\
zfit_gas, ezfit_gas, sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2))
## This outputs the spectrum and best-fit model to an open file object called outfile_spec
if outfile_spec is not None:
outfile_spec.write(
'# l f ef f_stars f_gas f_model_tot used_in_fit add_poly mult_poly\n'
)
for i in np.arange(wave.shape[0]):
isgood = 0
if goodpixels[goodpixels == i].shape[0] == 1: isgood = 1
outfile_spec.write(
'{0:0.4f} {1:0.4f} {2:0.4f} {3:0.4f} {4:0.4f} {5:0.4f} {6} {7:0.4f} {8:0.4f}\n'
.format(wave[i], galaxy[i], noise[i], stars[i], gas[i],
pp.bestfit[i], isgood, add_polynomial[i],
mult_polynomial[i]))
# ## This outputs the best-fit emission-subtracted spectrum to a fits file
# ## but I've commented it out since you are unlikely to need this!
# if emsub_specfile is not None:
# wave = np.exp(logLam_gal)*(1.+z_init)
# if include_gas:
# emsub = galaxy - gas
# else:
# emsub = galaxy
# col1 = fits.Column(name='wavelength', format='E', array=wave)
# col2 = fits.Column(name='flux',format='E',array=galaxy)
# col3 = fits.Column(name='error',format='E',array=noise)
# col4 = fits.Column(name='flux_emsub',format='E',array=emsub)
# cols = fits.ColDefs([col1,col2,col3,col4])
# tbhdu = fits.BinTableHDU.from_columns(cols)
# # delete old file if it exists
# if os.path.isfile(emsub_specfile): os.remove(emsub_specfile)
# tbhdu.writeto(emsub_specfile)
# return zfit_stars, ezfit_stars, sigma_stars, esigma_stars, zfit_gas, ezfit_gas, \
# sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2
return zfit_stars, sigma_stars, sigma_blr, wave, pp.bestfit
#plt.plot(d[:,1], d[:,2]*0.95316816783, label = 'GCM', color = '0.1') #multiplied by correction factor for rp/rs (Vivien assuemd 0.1055)
#plt.ylim(0.0, 1.8e-3)
#plt.xlim(0.75, 1.7)
plt.ylim(0.0, 4.8e-3)
plt.xlim(0.75, 5.7)
#plt.gca().annotate('TiO features', xy=(1.05, 0.0008), xytext=(0.8, 0.0005), arrowprops=dict(facecolor='black', shrink=0.05),)
#plt.gca().annotate('', xy=(0.97, 0.00095), xytext=(0.95, 0.0006), arrowprops=dict(facecolor='black', shrink=0.05),)
g = Gaussian1DKernel(stddev=50)
offset = 0.0000
d = np.genfromtxt("wasp33b_from_Caroline/wasp33b_rfacv1.0_m0.0_co1.0nc.flx")
plt.plot(d[:, 0],
convolve(d[:, 1], g, boundary='extend') + offset,
label="forward model, rfacv1.0_m0.0")
d = np.genfromtxt("wasp33b_from_Caroline/wasp33b_rfacv0.85_m0.0_co1.0nc.flx")
plt.plot(d[:, 0],
convolve(d[:, 1], g, boundary='extend') + offset,
label="forward model, rfacv0.85_m0.0")
#d = np.genfromtxt("wasp33b_m0.0_co1.0nc.spec")
#plt.plot(d[:,0], convolve(d[:,1], g, boundary = 'extend') + offset, label = "forward model, with TiO")
#d = np.genfromtxt("wasp33b_m0.0_co1.0nc_noTiO.spec")
#plt.plot(d[:,0], convolve(d[:,1], g, boundary = 'extend') + offset, label = "forward model, no TiO")
plt.errorbar(4.5, 4250e-6, 160e-6, fmt='xk')
plt.errorbar(3.6, 3506e-6, 173e-6, fmt='xk')
def main():
"""
# Script to get lineflux
"""
# Get extraction
data = np.genfromtxt("../data/spectroscopy/NIRext.dat", dtype=None)
# data = np.genfromtxt("../data/NIROB4skysubstdext.dat", dtype=None)
# print(np.std([18, 31, 38, 20]))
# exit()
wl = data[:, 1]
flux = data[:, 2]
error = data[:, 3]
error[error > 1e-15] = np.median(error)
# Load synthetic sky
sky_model = fits.open("../data/spectroscopy/NIRskytable.fits")
wl_sky = 10 * (sky_model[1].data.field('lam')) # In nm
# Convolve to observed grid
f = interpolate.interp1d(wl_sky,
convolve(sky_model[1].data.field('flux'),
Gaussian1DKernel(stddev=3)),
bounds_error=False,
fill_value=np.nan)
# f = interpolate.interp1d(wl_sky, sky_model[1].data.field('flux'), bounds_error=False, fill_value=np.nan)
flux_sky = f(wl)
flux[(wl > 21055) & (wl < 21068)] = np.nan
flux[(wl > 21098) & (wl < 21102)] = np.nan
flux[(wl > 21109) & (wl < 21113)] = np.nan
m = (flux_sky < 9000) & ~np.isnan(flux)
g = interpolate.interp1d(wl[m],
flux[m],
bounds_error=False,
fill_value=np.nan)
flux = g(wl)
mask = (wl > convert_air_to_vacuum(21076) - 9) & (
wl < convert_air_to_vacuum(21076) + 9)
# mask = (wl > 21020) & (wl < 21150)
F_ha = np.trapz(flux[mask], x=wl[mask])
print("Total %0.1e" % F_ha)
F_ha_err = np.sqrt(np.trapz((error**2.)[mask], x=wl[mask]))
print("Total %0.1e +- %0.1e" % (F_ha, F_ha_err))
# exit()
# F_ha = 4.1e-17
dL = cosmo.luminosity_distance(2.211).to(u.cm).value
L_ha = F_ha * 4 * np.pi * dL**2
L_ha_err = F_ha_err * 4 * np.pi * dL**2
SFR = 7.9e-42 * L_ha
SFR_err = 7.9e-42 * L_ha_err
print(SFR, SFR_err)
# exit()
# print(len(wl_sky), len(wl))
v_rot = (((16092 - 16068) / 16080) * 3e5) / 2 * (u.km / u.s)
print(v_rot * 2)
M = v_rot**2 * ((7 / 2) * 0.21 * 8.46597 * u.kpc) / c.G
print("%0.4e" % M.to(u.M_sun).value)
# flux[flux_sky > 10000] = np.nan
pl.plot(wl, flux, label="Spectrum")
pl.plot(wl[mask], flux[mask], label="Integration limits")
pl.plot(wl, flux_sky * 1e-22, label="Sky spectrum")
pl.errorbar(wl,
flux,
yerr=error,
fmt=".k",
capsize=0,
elinewidth=0.5,
ms=3,
label="SFR = " + str(np.around(SFR, 0)) + " +-" +
str(np.around(SFR_err, 0)))
pl.errorbar(1,
1,
yerr=1,
fmt=".k",
capsize=0,
elinewidth=0.5,
ms=3,
label=r"f$_{H\alpha}$ = %0.1e +- %0.1e" % (F_ha, F_ha_err))
pl.xlim(21000, 21150)
pl.ylim(-0.5e-17, 3e-17)
# pl.show()
# Save figure for tex
pl.legend()
pl.savefig("../figures/Ha_flux.pdf", dpi="figure")
pl.show()
