def _set_aperture_elements(self):
""" Set internal aperture elements.
``self._ap_rect``, ``self.ap_el_1``, ``self.ap_el_2`` and their
``_in`` counterparts are always made by
``np.atleast_2d(self.position)``, so their results are always in
the ``N x 2`` shape.
"""
if hasattr(self, 'a'):
w = self.w
a = self.a
b = self.b
h = self.h
theta = self.theta
elif hasattr(self, 'a_in'): # annulus
w = self.w
a = self.a_out
b = self.b_out
h = self.h_out
theta = self.theta
else:
raise ValueError('Cannot determine the aperture shape.')
# positions only accepted in the shape of (N, 2), so shape[0]
# gives the number of positions:
pos = np.atleast_2d(self.positions)
self.offset = np.array([w * np.cos(theta) / 2, w * np.sin(theta) / 2])
offsets = np.repeat(np.array([
self.offset,
]), pos.shape[0], 0)
# aperture elements for aperture,
# OUTER aperture elements for annulus:
self._ap_rect = RectangularAperture(positions=pos,
w=w,
h=h,
theta=theta)
self._ap_el_1 = EllipticalAperture(positions=pos - offsets,
a=a,
b=b,
theta=theta)
self._ap_el_2 = EllipticalAperture(positions=pos + offsets,
a=a,
b=b,
theta=theta)
if hasattr(self, 'a_in'): # inner components of annulus
self._ap_rect_in = RectangularAperture(positions=pos,
w=self.w,
h=self.h_in,
theta=self.theta)
self._ap_el_1_in = EllipticalAperture(positions=pos - offsets,
a=self.a_in,
b=self.b_in,
theta=self.theta)
self._ap_el_2_in = EllipticalAperture(positions=pos + offsets,
a=self.a_in,
b=self.b_in,
theta=self.theta)
def aperture_photometry_scan(data,
x_pos,
y_pos,
ap_width,
ap_length,
theta=0.0,
show=False,
plt_title=None):
"""Aperture photometry on source located on x_pos, y_pos with
rectangular aperture of dimensions specified by ap_length, ap_width
is used. Aperture sums are NOT sky subtracted.
Parameters
----------
data : `np.array`
2D array of floats
x_pos : float
X position of source.
y_pos : float
Y position of source
ap_width : int
Width (along x axis) of photometric aperture.
ap_length : int
Length (along y axis) of photometric aperture.
theta : float
Angle of orientation (from x-axis) for aperture, in radians.
Increases counter-clockwise.
show : bool, optional
If true, plot showing aperture(s) on source will pop up. Defaults to F.
plt_title : str or None, optional
Only used if `show` is True. Title for plot. Defaults to None.
Returns
-------
phot_tab : `astropy.table`
Table containing
"""
copy_data = copy.copy(data)
rect_ap = RectangularAperture((x_pos, y_pos),
w=ap_width,
h=ap_length,
theta=theta)
phot_table = aperture_photometry(copy_data, rect_ap, method='exact')
if show:
mask = rect_ap.to_mask(method='center')
data_cutout = mask.cutout(data)
plt.title(plt_title)
z1, z2 = (-12.10630989074707, 32.53888328838081)
plt.imshow(data, origin='lower', vmin=z1, vmax=z2)
rect_ap.plot(color='white', lw=2)
plt.show()
plt.close()
return phot_table
def buildApertures(self):
"""Builds the apertures from the centers"""
if np.size(self.aperCens) < 1:
return
self.apersObj = RectangularAperture(positions=self.aperCens, \
w=self.rectWidth, \
h=self.rectHeight, \
theta=self.rectTheta)
self.apersSky = RectangularAperture(positions=self.skyCens, \
w=self.rectWidth, \
h=self.rectHeight, \
theta=self.rectTheta)
def custom_aperture(self,
shape=None,
r=0.0,
l=0.0,
w=0.0,
theta=0.0,
pos=None,
method='exact'):
"""
Creates a custom circular or rectangular aperture of arbitrary size.
Parameters
----------
shape: str, optional
The shape of the aperture to be used. Must be either `circle` or `rectangle.`
r: float, optional
If shape is `circle` the radius of the circular aperture to be used.
l: float, optional
If shape is `rectangle` the length of the rectangular aperture to be used.
w: float, optional
If shape is `rectangle` the width of the rectangular aperture to be used.
theta: float, optional
If shape is `rectangle` the rotation of the rectangle relative to detector coordinate.
Uses units of radians.
pos: tuple, optional
The center of the aperture, in TPF coordinates. If not set, defaults to the center of the TPF.
method: str, optional
The method of producing a light curve to be used, either `exact`, `center`, or `subpixel`.
Passed through to photutils and used as intended by that package.
"""
if shape is None:
print("Please select a shape: circle or rectangle")
shape = shape.lower()
if pos is None:
pos = (self.tpf.shape[1] / 2, self.tpf.shape[2] / 2)
else:
pos = pos
if shape == 'circle':
if r == 0.0:
print("Please set a radius (in pixels) for your aperture")
else:
aperture = CircularAperture(pos, r=r)
self.aperture = aperture.to_mask(method=method)[0].to_image(
shape=((np.shape(self.tpf[0]))))
elif shape == 'rectangle':
if l == 0.0 or w == 0.0:
print(
"For a rectangular aperture, please set both length and width: custom_aperture(shape='rectangle', l=#, w=#)"
)
else:
aperture = RectangularAperture(pos, l=l, w=w, t=theta)
self.aperture = aperture.to_mask(method=method)[0].to_image(
shape=((np.shape(self.tpf[0]))))
else:
print(
"Aperture shape not recognized. Please set shape == 'circle' or 'rectangle'"
)
def photometry(image2d,
cen_x,
cen_y,
index=0,
shape='Circ',
rad=None,
r_in=None,
r_out=None,
ht=None,
wid=None,
w_in=None,
w_out=None,
h_out=None,
ang=0.0):
"""
PARAMETERS
----------
image2d = 2 dimensional image array. Type = ndarray
cen_x, cen_y = x & y center position. Type = ndarray/list
index = if cen_x and cen_Y is a list of more than 1 element, specify the desired index. Type = Integer
shape = 'Circ':CircularAperture, 'Rect':RectangularAperture, 'CircAnn':CircularAnnulus, 'RectAnn':RectangularAnnulus
rad, r_in, r_out, ht, wid, w_in, w_out, h_out, ang = Astropy's aperture parameters
RETURNS
-------
flux = flux of the image extracted by the aperture described in the "shape" parameter. Type = Float
aperture = aperture object created by astropy
"""
mask = np.isnan(image2d) == True
if shape == 'Circ':
aperture = CircularAperture((cen_x[index], cen_y[index]), r=rad)
elif shape == 'Rect':
aperture = RectangularAperture((cen_x[index], cen_y[index]),
w=wid,
h=ht,
theta=ang)
elif shape == 'CircAnn':
aperture = CircularAnnulus((cen_x[index], cen_y[index]),
r_in=r_in,
r_out=r_out)
elif shape == 'RectAnn':
aperture = RectangularAnnulus((cen_x[index], cen_y[index]),
w_in=w_in,
w_out=w_out,
h_out=h_out,
theta=ang)
phot_table = aperture_photometry(image2d, aperture, mask=mask)
flux = phot_table['aperture_sum']
return flux, aperture
def place_overlay(x_pixel, y_pixel, mousebutton, wcs, ax, FOV=1.5, Size='750'):
"""Places either the main CCD, or the guide CCD apeture overlay
centered on the pixel coordinates depending on the boolean case "guide"
specified in the input arguments. The overlay for the guide camera is placed
with respect to the CCD center. Function also replots image to reset apertures
(Easiest way I could find to do it for now)
"""
fov = FOV * 60
size = int(Size)
scale = fov / size
#Set plate scale of image 60 is number of arcmin across
position_Target = (size / 2, size / 2)
aperture_Target = CircularAperture(position_Target, r=(PlateScale / scale))
aperture_Target.plot(color='blue', lw=1.5, alpha=0.5)
if x_pixel == None or y_pixel == None:
position_CCD = (size / 2, size / 2)
position_Guide = (size / 2 - GuideOffRA / scale,
((size / 2) + (GuideOffDEC / scale)))
else:
if mousebutton:
position_Guide = (x_pixel, y_pixel)
position_CCD = ((x_pixel) + GuideOffRA / scale,
(y_pixel - (GuideOffDEC / scale)))
else:
position_Guide = (x_pixel - GuideOffRA / scale,
y_pixel + (GuideOffDEC / scale))
position_CCD = ((x_pixel), (y_pixel))
aperture_GuideStar = CircularAperture(position_Guide,
r=(PlateScale / scale))
aperture_Guide = RectangularAperture(position_Guide, (GuideX / scale),
(GuideY / scale))
aperture_GuideStar.plot(color='blue', lw=1.5, alpha=0.5)
aperture_Guide.plot(color='red', lw=1.5, alpha=0.5)
aperture_CCD = RectangularAperture(position_CCD, (SciX / scale),
(SciY / scale))
aperture_CCD.plot(color='green', lw=1.5, alpha=0.5)
ax.figure.canvas.draw()
coords = SkyCoord.from_pixel(position_CCD[0], position_CCD[1], wcs)
RA_str = coords.ra.to_string(units.hour)
DEC_str = coords.dec.to_string(units.degree)
#print coords of main CCD center
print(RA_str, DEC_str)
def get_slit_loss(fwhm, sw):
'''
'''
sig = fwhm * gaussian_fwhm_to_sigma
ny = nx = np.int(10 * sig)
Y, X = np.mgrid[-ny:ny + 1, -nx - 10:nx + 1 + 10]
g = Gaussian2D(amplitude=1 / (2 * np.pi * sig**2),
x_mean=0,
y_mean=0,
x_stddev=sig,
y_stddev=sig,
theta=0)
data = g(X, Y)
(y, x) = np.unravel_index(np.argmax(data, axis=None), data.shape)
aper = RectangularAperture((x, y), w=sw, h=data.shape[0])
# HM = data[ind] / 2
# FW = np.where( HM <= data[ind[0]] )[0][-1] - np.where( HM <= data[ind[0]] )[0][ 0]
# print( FW * gaussian_fwhm_to_sigma / sig )
# Aperture photometry
phot_table = aperture_photometry(data,
aper,
method='subpixel',
subpixels=100)
slit_loss = data.sum() - phot_table['aperture_sum'][0]
# fig, ax = plt.subplots( 1, 1, figsize = ( 10, 10 ) )
# ax.imshow( data )
# aper.plot( axes = ax, color = 'red', lw = 2 )
# plt.show()
return slit_loss
def transform_xy_rectangle(centerX, centerY,width, height, cubeObj):
""" Update rectangle data widgets and image object attributes
:param float centerX: x value of the center coordinate
:param float centerY: y value of the center coordinate
:param float width: width value of the rectangle
:param float height: height value of the rectangle
:param object cubeObj: current data from the cube
:return list fValues: flux values list for each wavelength
:return list wValues: wavelenght list for each slice
:return Aperture_Photometry aperture: aperture of the rectangle
"""
fValues = []
#Because it gets all the flux on a pixel, it needs to get the area of it rather than one value.
pixelArea = (cubeObj.cubeRAValue * 3600.) * (cubeObj.cubeDValue * 3600.)
aperture = RectangularAperture([centerX, centerY], width, height)
for i in range(cubeObj.maxSlice):
phot_table = aperture_photometry(cubeObj.data_cube[i], aperture, method='subpixel')
fValues.append(phot_table['aperture_sum'][0]*pixelArea)
wValues = [((w+1) - cubeObj.cubeZCPix)*cubeObj.cubeWValue + cubeObj.cubeZCRVal for w in range(len(fValues))]
return fValues, wValues, aperture
def A_photometry(image_data,
bg_err,
factor=1,
ape_sum=[],
ape_sum_err=[],
cx=15,
cy=15,
r=2.5,
a=5,
b=5,
w_r=5,
h_r=5,
theta=0,
shape='Circular',
method='center'):
'''
Performs aperture photometry, first by creating the aperture (Circular,
Rectangular or Elliptical), then it sums up the flux that falls into the
aperture.
Parameters
----------
image_data: 3D array
Data cube of images (2D arrays of pixel values).
bg_err : 1D array
Array of uncertainties on pixel value.
factor : float (optional)
Electron count to photon count factor. Default is 1 if none given.
ape_sum : 1D array (optional)
Array of flux to append new flux values to. If 'None', the new values
will be appended to an empty array
ape_sum_err: 1D array (optional)
Array of flux uncertainty to append new flux uncertainty values to. If
'None', the new values will be appended to an empty array.
cx : int (optional)
x-coordinate of the center of the aperture. Default is 15.
cy : int (optional)
y-coordinate of the center of the aperture. Default is 15.
r : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Circular', c_radius is
the radius for the circular aperture. Default is 2.5.
a : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Elliptical', e_semix is
the semi-major axis for elliptical aperture (x-axis). Default is 5.
b : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Elliptical', e_semiy is
the semi-major axis for elliptical aperture (y-axis). Default is 5.
w_r : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Rectangular', r_widthx is
the full width for rectangular aperture (x-axis). Default is 5.
h_r : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Rectangular', r_widthy is
the full height for rectangular aperture (y-axis). Default is 5.
theta : int (optional)
If phot_meth is 'Aperture' and ap_shape is 'Elliptical' or
'Rectangular', theta is the angle of the rotation angle in radians
of the semimajor axis from the positive x axis. The rotation angle
increases counterclockwise. Default is 0.
shape : string object (optional)
If phot_meth is 'Aperture', ap_shape is the shape of the aperture.
Possible aperture shapes are 'Circular', 'Elliptical', 'Rectangular'.
Default is 'Circular'.
method : string object (optional)
If phot_meth is 'Aperture', apemethod is the method used to
determine the overlap of the aperture on the pixel grid. Possible
methods are 'exact', 'subpixel', 'center'. Default is 'center'.
Returns
-------
ape_sum : 1D array
Array of flux with new flux appended.
ape_sum_err: 1D array
Array of flux uncertainties with new flux uncertainties appended.
'''
l, h, w = image_data.shape
position = [cx, cy]
if (shape == 'Circular'):
aperture = CircularAperture(position, r=r)
elif (shape == 'Elliptical'):
aperture = EllipticalAperture(position, a=a, b=b, theta=theta)
elif (shape == 'Rectangular'):
aperture = RectangularAperture(position, w=w_r, h=h_r, theta=theta)
for i in range(l):
data_error = calc_total_error(image_data[i, :, :],
bg_err[i],
effective_gain=1)
phot_table = aperture_photometry(image_data[i, :, :],
aperture,
error=data_error,
pixelwise_error=False)
if (phot_table['aperture_sum_err'] > 0.000001):
ape_sum.extend(phot_table['aperture_sum'] * factor)
ape_sum_err.extend(phot_table['aperture_sum_err'] * factor)
else:
ape_sum.extend([np.nan])
ape_sum_err.extend([np.nan])
return ape_sum, ape_sum_err
class ApertureSet(object):
"""Set of apertures for the photometry"""
# (I'm not sure if I want to stick with photutils, since that
# currently enforces the same aperture size for all
# apertures. Either way, we separate the apertures list out from
# the individual image.
def __init__(self, perpendic=False):
# control variable
self.perpendic = perpendic
# Some parameters for aperture construction
self.rectWidth = 220.
self.rectHeight = 90.
self.rectTheta = np.radians(325.)
# sky rectangles
self.skyWidth = np.copy(self.rectWidth)
self.skyHeight = np.copy(self.rectHeight)
# sky nudge
self.skyNudge = np.array([0., 30.])
if self.perpendic:
self.rectTheta = np.radians(55.)
self.skyNudge = np.array([120., 130.])
# some default positions for apertures
self.aperCens = np.array([])
self.skyCens = np.array([])
# INCLUDE ENTRIES FOR THE SKY REGIONS!!!
def setDefaultCenters(self):
"""Sets up default aperture centers for testing on the
2017-04-14 Jupiter data"""
# the third one is a dummy to help me get the array dimensions
# the right way round...
vX = np.array([944., 892., 1105., 693., 1297.])
vY = np.array([520., 446., 365., 592., 250.5])
if self.perpendic:
vX = np.array([960., 885., 1100., 772., 1285.])
vY = np.array([456., 509., 381., 588., 264.0])
self.aperCens = np.vstack((vX, vY))
# set up the sky apertures
self.skyCens = np.copy(self.aperCens)
# DO THE OFFSET HERE.
self.skyCens[1] -= (self.skyNudge[1] + self.skyHeight)
self.skyCens[0] -= self.skyNudge[0]
if not self.perpendic:
self.skyCens[1, 1] += 2.0 * (self.skyNudge[1] + self.skyHeight)
# self.skyCens[1,0] += 2.0 * (self.skyNudge[1] + self.skyHeight)
def buildApertures(self):
"""Builds the apertures from the centers"""
if np.size(self.aperCens) < 1:
return
self.apersObj = RectangularAperture(positions=self.aperCens, \
w=self.rectWidth, \
h=self.rectHeight, \
theta=self.rectTheta)
self.apersSky = RectangularAperture(positions=self.skyCens, \
w=self.rectWidth, \
h=self.rectHeight, \
theta=self.rectTheta)
def showApertures(self, figNum=1, inAx=None):
"""Uses photutils apertures built-in to show the apertures"""
# This is a really stupid cargo-cult way to proceed... Come
# back to this later!!
if not inAx:
fig = plt.figure(figNum)
fig.clf()
ax = fig.add_subplot(111)
ax.plot([0., 1024], [0., 1024], 'w.', alpha=0.)
else:
ax = inAx
self.apersObj.plot(ax=ax, color='g', alpha=0.7)
self.apersSky.plot(ax=ax, color='b', ls='--', alpha=0.7)
coeff = chebfit(x_sky[~clip_mask], sky_val[~clip_mask], deg=ORDER_APSKY)
data_skysub.append(cut_i - chebval(np.arange(cut_i.shape[0]), coeff))
data_skysub = np.array(data_skysub).T
hdr = objhdu[0].header
hdr.add_history(f"Sky subtracted using sky offset = {ap_sky_offset}, " +
f"{SIGMA_APSKY}-sigma {ITERS_APSKY}-iter clipping " +
f"to fit order {ORDER_APSKY} Chebyshev")
_ = fits.PrimaryHDU(data=data_skysub, header=hdr)
_.data = _.data.astype('float32')
_.writeto(SKYSBPATH / (OBJIMAGE.stem + ".skysub.fits"), overwrite=True)
pos = np.array([x_ap, y_ap]).T
aps = RectangularAperture(positions=pos, w=1, h=apheight, theta=0)
phot = aperture_photometry(data_skysub, aps, method='subpixel', subpixels=30)
ap_summed = phot['aperture_sum'] / EXPTIME
fig, axs = plt.subplots(2,
1,
figsize=(10, 6),
sharex=False,
sharey=False,
gridspec_kw=None)
axs[0].imshow(objimage, vmin=0, vmax=4900, origin='lower')
axs[1].imshow(data_skysub, vmin=0, vmax=200, origin='lower')
axs[1].plot(pos[:, 0], pos[:, 1], 'r-')
axs[0].set(title="Before sky subtraction")
axs[1].set(title="Sky subtracted")
def radial_profile(data, lim):
############################################################
#
# Get radial profiles - model
#
############################################################
############################################################
# Fetching information
imfile = open("Image_jband.out").readlines()
for line in imfile:
if line.split('=')[0] == 'MCobs:fov':
fov = float(line.split('=')[1].split('!')[0])
elif line.split('=')[0] == 'MCobs:npix':
npix = float(line.split('=')[1].split('!')[0])
elif line.split('=')[0] == 'MCobs:phi':
phi_image = float(line.split('=')[1].split('!')[0])
elif line.split('=')[0] == 'MCobs:theta':
theta = float(line.split('=')[1].split('!')[0])
else:
continue
infile = open("input.dat").readlines()
for line in infile:
if line.split('=')[0] == 'Distance':
d = float(line.split('=')[1])
############################################################
# Derived quantities
pxsize = fov / npix # pixel scale (arcsec/px)
phi = (phi_image * units.deg).to(
units.rad).value # PA from north to east (rad)
e = np.sin(theta) # eccentricity of the annulus
angle_annulus = ((0.0) * units.deg).to(units.rad).value
e = np.sin(
(theta * units.deg).to(units.rad).value) # eccentricity of the annulus
# Determining limit for radial profile
linear_lim = 2 * lim # AU
angular_lim = linear_lim / ((d * units.pc).to(units.au).value) # rad
angular_lim = (angular_lim * units.rad).to(units.arcsec).value # arcsec
pixel_lim = int(round(angular_lim / pxsize))
xc = 0.5 * data.shape[0] # Image center in data coordinates
yc = 0.5 * data.shape[1] # Image center in data coordinates
dr = 1.0 # Width of the annulus
w = 1.0
h = 1.0
lim = 120
lim = toPX(lim, pxsize, d)
InRad = np.pi / 180.0
xc_array = []
yc_array = []
x0 = xc
y0 = yc
xc_array.append(x0)
yc_array.append(y0)
for l in np.arange(-lim, lim, 1.0):
xval = x0 + l * np.cos(angle_annulus)
yval = y0 + l * np.sin(angle_annulus)
xc_array.append(xval)
yc_array.append(yval)
positions = [(i, j) for (i, j) in zip(xc_array, yc_array)]
InRad = np.pi / 180.0
apertures = RectangularAperture(positions, w, h, angle_annulus)
"""
############################################################
# Do a check
a=0.4
vmin=np.percentile(data,a)
vmax=np.percentile(data,100-a)
plt.imshow(data,clim=(vmin,vmax))
plt.title("Image observation")
apertures.plot(color='red',lw=1)
#plt.xlim(400,600)
#plt.ylim(600,400)
plt.show()
"""
phot_table = aperture_photometry(data, apertures)
r_au = [
toAU(phot_table['xcenter'][i].value, phot_table['ycenter'][i].value,
xc, yc, pxsize, d) for i in range(0, len(phot_table))
]
brightness = [
phot_table['aperture_sum'][i] for i in range(0, len(phot_table))
]
brightness = np.array(brightness)
############################################################
# Creating brightness profile normalized
rcmin = 35.0
rcmax = 100.0
bmaxc = []
for i in range(0, len(r_au)):
if rcmin <= r_au[i] <= rcmax:
bmaxc.append(brightness[i])
bmaxc = np.array(bmaxc)
fac = 1 / max(bmaxc)
brightness = brightness * fac
"""
fig=plt.figure()
ax=plt.axes()
ax.plot(r_au,brightness,'.')
ax.set_xlabel(r"Projected radial distance (AU)")
ax.set_ylabel("Density flux (a.u.)")
ax.set_title("Radial profile observation")
#ax.set_ylim(-0.1,5)
plt.show()
sys.exit()
"""
############################################################
# Creating file
file = open('jband_radial_profile_modeled.dat', "w")
for i in range(0, len(r_au)):
file.write('%.15e %.15e \n' % (r_au[i], brightness[i]))
#data_mod=get_profile(data_mod,pxsize,lim)
return None
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model: IFUCubeModel
The input model.
source_type: string
"point" or "extended"
extract_params: dict
The extraction parameters for aperture photometry.
Returns
-------
(ra, dec, wavelength, net, background, dq)
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, and dq arrays no matter what.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.int32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
radius = None
subtract_background = False
inner_bkg = None
outer_bkg = None
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
(ra, dec) = (0., 0.)
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq) # all bad
if hasattr(input_model.meta, 'wcs'):
wcs = input_model.meta.wcs
else:
log.warning("WCS function not found in input.")
wcs = None
if wcs is not None:
x_array = np.empty(shape[0], dtype=np.float64)
x_array.fill(float(shape[2]) / 2.)
y_array = np.empty(shape[0], dtype=np.float64)
y_array.fill(float(shape[1]) / 2.)
z_array = np.arange(shape[0], dtype=np.float64) # for wavelengths
ra, dec, wavelength = wcs(x_array, y_array, z_array)
nelem = len(wavelength)
ra = ra[nelem // 2]
dec = dec[nelem // 2]
else:
(ra, dec) = (0., 0.)
wavelength = np.arange(1, shape[0] + 1, dtype=np.float64)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg,
r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :],
aperture,
method=method,
subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :],
annulus,
method=method,
subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
nan_mask = np.isnan(wavelength)
n_nan = nan_mask.sum(dtype=np.intp)
if n_nan > 0:
log.warning("%d NaNs in wavelength array.", n_nan)
dq[nan_mask] = np.bitwise_or(dq[nan_mask], dqflags.pixel['DO_NOT_USE'])
not_nan = np.logical_not(nan_mask)
flag = np.where(not_nan)
if len(flag[0]) > 0:
n_trimmed = flag[0][0] + nelem - (flag[0][-1] + 1)
if n_trimmed > 0:
log.info("Output arrays have been trimmed by %d elements",
n_trimmed)
slc = slice(flag[0][0], flag[0][-1] + 1)
wavelength = wavelength[slc]
net = net[slc]
background = background[slc]
dq = dq[slc]
else:
dq |= dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq)
def get_profile(file, pxsize, PA_disk, inc, d):
############################################################
#
# Get flux along the semi-major axis with a rectangular
# aperture.
#
# file: the fits file of the observation
# pxsize: pixel scale (arcsec/px)
# PA_disk: position angle of the disk measured east-north (deg)
# inc: disk's inclination (deg)
# d: distance to the source (pc)
#
# returns a file with the brightness profile along the
# semi-major axis and a file with the position and flux
# value of the pixel located along the semi-major axis
# in the "South-East" quadrant containing the maximum flux
# value. The brightness profile returned is not normalized.
#
############################################################
############################################################
# Load data
hdulist = fits.open(file)
data_obs = hdulist[0].data # adu's
############################################################
# Derived properties
angle_annulus = ((PA_disk - 90.0) * units.deg).to(units.rad).value
e = np.sin((inc * units.deg).to(units.rad).value)
xc = 0.5 * data_obs.shape[0] # Image center in data coordinates
yc = 0.5 * data_obs.shape[1] # Image center in data coordinates
w = 1.0
h = 1.0
lim = 120.0
lim = toPX(lim, pxsize, d)
xc_array = []
yc_array = []
############################################################
# Setting up aperture photometry
x0 = xc
y0 = yc
xc_array.append(x0)
yc_array.append(y0)
for l in np.arange(-lim, lim, 1.0):
xval = x0 + l * np.cos(angle_annulus)
yval = y0 + l * np.sin(angle_annulus)
xc_array.append(xval)
yc_array.append(yval)
positions = [(i, j) for (i, j) in zip(xc_array, yc_array)]
apertures = RectangularAperture(positions, w, h, angle_annulus)
"""
# Do a check?
a=0.01
vmin=np.percentile(data_obs,a)
vmax=np.percentile(data_obs,100-a)
plt.imshow(data_obs,clim=(vmin,vmax))
apertures.plot(color='red',lw=1)
plt.show()
sys.exit()
"""
############################################################
# Performing aperture photometry
phot_table = aperture_photometry(data_obs, apertures)
r_au = [
toAU(phot_table['xcenter'][i].value, phot_table['ycenter'][i].value,
xc, yc, pxsize, d) for i in range(0, len(phot_table))
]
brightness = [
phot_table['aperture_sum'][i] for i in range(0, len(phot_table))
]
for i in range(0, len(brightness)):
brightness[i] = brightness[i] / apertures[i].area
"""
# Do a check?
fig=plt.figure()
ax=plt.axes()
ax.plot(r_au,brightness,'.')
ax.set_xlabel(r"Projected radial distance (AU)")
ax.set_ylabel("Density flux (a.u.)")
plt.show()
sys.exit()
"""
############################################################
# Finding maximum value along semi-major axis
rstart = 40.0
rend = 80.0
bmax = []
for i in range(0, len(r_au)):
if rstart <= r_au[i] <= rend:
bmax.append(brightness[i])
bmax = np.array(bmax)
imax = np.where(brightness == max(bmax))[0][0]
############################################################
# Writing files
f1 = open("info_max_Qphi.dat", "w")
f2 = open("radial_cut_0FWHM.dat", "w")
f1.write("r_max=%.2f (AU)\n" % r_au[imax])
f1.write("PA_max=%.2f (deg)\n" % PA_disk)
f1.write("B_max=%.15f (a.u.)\n" % brightness[imax])
for i in range(0, len(r_au)):
f2.write("%.5e %.5e \n" % (r_au[i], brightness[i]))
f1.close()
f2.close()
return None
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"point" or "extended"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each pixel.
net : ndarray, 1-D
The count rate (or flux) minus the background at each pixel.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source count rate to get `net`.
npixels : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `net`.
dq : ndarray, 1-D, uint32
The data quality array.
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, npixels, and dq arrays
# no matter what. We may need to divide by npixels, so the default
# is 1 rather than 0.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
npixels = np.ones(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.uint32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
radius = None
inner_bkg = None
outer_bkg = None
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
subtract_background = False
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
(ra, dec) = (0., 0.)
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, npixels, dq) # all bad
x0 = float(shape[2]) / 2.
y0 = float(shape[1]) / 2.
(ra, dec, wavelength) = get_coordinates(input_model, x0, y0)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg,
r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
npixels[:] = aperture.area()
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :],
aperture,
method=method,
subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :],
annulus,
method=method,
subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
(wavelength, net, background, npixels, dq) = \
nans_in_wavelength(wavelength, net, background, npixels, dq)
return (ra, dec, wavelength, net, background, npixels, dq)
def OutflowIntensity(model, dstar, group, wave):
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from photutils import aperture_photometry as ap
from photutils import CircularAperture, RectangularAperture
import astropy.constants as const
pc = const.pc.cgs.value
# m = ModelOutput('/Volumes/SD-Mac/model131.rtout')
image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='MJy/sr')
# The radius of the aperture in arcsec
# radius = 10
x = 100
y = 100
# area = np.pi*radius**2 / 4.25e10 # in sr
area = x*y / 4.25e10
# The offset to the center
offset = 50
if wave != list:
wave = [wave]
iwav = np.argmin(np.abs(wave - image.wav))
# Image in the unit of MJy/sr, change it into erg/s/cm2/Hz/sr
factor = 1e-23*1e6
val = image.val[::-1, :, iwav] * factor + 1e-30
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
pos_n = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 + offset*len(val[0,:])/2/w)
pos_s = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 - offset*len(val[0,:])/2/w)
# aper_n = CircularAperture(pos_n, r=radius * len(val[0,:])/2/w )
# aper_s = CircularAperture(pos_s, r=radius * len(val[0,:])/2/w )
# # plot to make sure the selection is correct
# from astropy.convolution import Gaussian1DKernel, convolve
# g = Gaussian1DKernel(stddev=20)
#
# fig = plt.figure(figsize=(8,6))
# ax = fig.add_subplot(111)
#
# ax.plot(np.arange(-w, w, 2*w/len(val[0,:])), convolve(np.sum(val[len(val[0,:])/2.-11:len(val[0,:])/2.+9,:], axis=0), g, boundary='extend'), color='b')
# ax.plot(np.arange(-w, w, 2*w/len(val[0,:])), convolve(np.sum(val[:,len(val[0,:])/2.-11:len(val[0,:])/2.+9], axis=1), g, boundary='extend'), color='r')
# # ax.set_xscale('log')
#
# fig.savefig('/Users/yaolun/test/im_test.pdf', format='pdf', dpi=300, bbox_inches='tight')
# fig.clf()
aper_n = RectangularAperture(pos_n, w=x*len(val[0,:])/2/w, h=y*len(val[0,:])/2/w, theta=0)
aper_s = RectangularAperture(pos_s, w=x*len(val[0,:])/2/w, h=y*len(val[0,:])/2/w, theta=0)
# multiply the aperture size in sr and convert to Jy
phot_n = ap(val, aper_n)['aperture_sum'].data * area * 1e23
phot_s = ap(val, aper_s)['aperture_sum'].data * area * 1e23
return phot_n, phot_s
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model: IFUCubeModel
The input model.
source_type: string
"point" or "extended"
extract_params: dict
The extraction parameters for aperture photometry.
Returns
-------
(wavelength, net, background, dq)
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, and dq arrays no matter what.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.int32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
radius = None
inner_bkg = None
outer_bkg = None
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
subtract_background = False
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (wavelength, net, background, dq) # all bad
wcs = input_model.meta.wcs
x_array = np.empty(shape[0], dtype=np.float64)
x_array.fill(float(shape[2]) / 2.)
y_array = np.empty(shape[0], dtype=np.float64)
y_array.fill(float(shape[1]) / 2.)
z_array = np.arange(shape[0], dtype=np.float64) # for wavelengths
_, _, wavelength = wcs(x_array, y_array, z_array)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg, r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
return (wavelength, net, background, dq)
def do_Rect_phot(pos, FWHM, trail, ap_min=3., ap_factor=1.5, \
win=None, wout=None, hout=None,\
sky_nsigma=3., sky_iter=10 ):
if win == None:
win = 4 * FWHM + trail
if wout == None:
wout = 8 * FWHM + trail
if hout == None:
hout = 8 * FWHM
N = len(pos)
if pos.ndim == 1:
N = 1
theta = give_theta(number_of_stars=5)
an = RectAn(pos, w_in=win, w_out=wout, h_out=hout, theta=theta)
ap_size = np.max([ap_min, ap_factor*FWHM])
aperture = RectAp(pos, w=(trail+ap_size), h=ap_size, theta=theta)
flux = aperture.do_photometry(image_reduc, method='exact')[0]
# do phot and get sum from aperture. [0] is sum and [1] is error.
#For test:
#FWHM = FWHM_moffat.copy()
#trail=trail_len.copy()
#win = 4 * FWHM + trail
#wout = 8 * FWHM + trail
#hout = 8 * FWHM
#N=len(pos_star_fit)
#an = RectAn(pos_star_fit, w_in=win, w_out=wout, h_out=hout, theta=(theta+np.pi/2))
#ap_size = 1.5*FWHM_moffat
#aperture = RectAp(pos_star_fit, w=(trail+ap_size), h=ap_size, theta=(theta+np.pi/2))
#flux = aperture.do_photometry(image_reduc, method='exact')[0]
#plt.figure(figsize=(12,12))
#plt.imshow(image_reduc, origin='lower', vmin=-10, vmax=1000)
#an.plot(color='white')
#aperture.plot(color='red')
flux_ss = np.zeros(N)
error = np.zeros(N)
for i in range(0, N):
mask_an = (an.to_mask(method='center'))[i]
# cf: test = mask_an.cutout(image_reduc) <-- will make cutout image.
sky_an = mask_an.apply(image_reduc)
all_sky = sky_an[np.nonzero(sky_an)]
# only annulus region will be saved as np.ndarray
msky, stdev, nsky, nrej = sky_fit(all_sky, method='Mode', mode_option='sex')
area = aperture.area()
flux_ss[i] = flux[i] - msky*area # sky subtracted flux
error[i] = np.sqrt( flux_ss[i]/gain \
+ area * stdev**2 \
+ area**2 * stdev**2 / nsky )
if inputs.star_img_save:
from matplotlib import pyplot as plt
mask_ap = (aperture.to_mask(method='exact'))[i]
star_ap_ss = mask_ap.apply(image_reduc-msky)
sky_an_ss = mask_an.apply(image_reduc-msky)
plt.suptitle('{0}, Star ID={1} ({nsky:3d} {nrej:3d} {msky:7.2f} {stdev:7.2f})'.format(
inputs.filename, i, nsky=nsky, nrej=nrej, msky=msky, stdev=stdev ))
ax1 = plt.subplot(1,2,1)
im1 = ax1.imshow(sky_an_ss, origin='lower')
plt.colorbar(im1, orientation='horizontal')
ax2 = plt.subplot(1,2,2)
im2 = ax2.imshow(star_ap_ss, origin='lower')
plt.colorbar(im2, orientation='horizontal')
plt.savefig('{0}.star{1}.png'.format(inputs.filename, i))
plt.clf()
if pos.ndim > 1:
print('\t[{x:7.2f}, {y:7.2f}], {nsky:3d} {nrej:3d} {msky:7.2f} {stdev:7.2f} {flux:7.1f} {ferr:3.1f}'.format(\
x=pos[i][0], y=pos[i][1], \
nsky=nsky, nrej=nrej, msky=msky, stdev=stdev,\
flux=flux_ss[i], ferr=error[i]))
return flux_ss, error
def _prepare_mask(bbox, ap_r, ap_1, ap_2, method, subpixels, min_mask=0):
""" Make the pill box mask array.
Note
----
To make an ndarray to represent the overlapping mask, the three
(a rectangular and two elliptical) apertures are generated, but
parallely shifted such that the bounding box has ``ixmin`` and
``iymin`` both zero. Then proper mask is generated as an
ndarray. It is then used by ``PillBoxMaskMixin.to_mask`` to make
an ``ApertureMask`` object by combining this mask with the
original bounding box.
Parameters
----------
bbox : `~photutils.BoundingBox`
The bounding box of the original aperture.
ap_r : `~photutils.RectangularAperture`
The rectangular aperture of a pill box.
ap_1, ap_2 : `~photutils.EllipticalAperture`
The elliptical apertures of a pill box. The order of
left/right ellipses is not important for this method.
method : See `~photutils.PillBoxMaskMixin.to_mask`
subpixels : See `~photutils.PillBoxMaskMixin.to_mask`
min_mask : float, optional
The mask values smaller than this value is ignored (set to
0). This is required because the subtraction of elliptical
and rectangular masks give some negative values. One can set
it to be ``1/subpixels**2`` because ``RectangularAperture``
does not support ``method='exact'`` yet.
Returns
-------
mask_pill : ndarray
The mask of the pill box.
"""
aps = []
for ap in [ap_r, ap_1, ap_2]:
pos_cent = ap.positions
tmp_cent = pos_cent - np.array([bbox.ixmin, bbox.iymin])
if hasattr(ap, 'w'):
tmp_ap = RectangularAperture(positions=tmp_cent,
w=ap.w,
h=ap.h,
theta=ap.theta)
else:
tmp_ap = EllipticalAperture(positions=tmp_cent,
a=ap.a,
b=ap.b,
theta=ap.theta)
aps.append(tmp_ap)
bbox_shape = bbox.shape
mask_kw = dict(method=method, subpixels=subpixels)
mask_r = (aps[0].to_mask(**mask_kw).to_image(bbox_shape))
mask_1 = (aps[1].to_mask(**mask_kw).to_image(bbox_shape))
mask_2 = (aps[2].to_mask(**mask_kw).to_image(bbox_shape))
# Remove both machine epsilon artifact & negative mask values:
mask_pill_1 = mask_1 - mask_r
mask_pill_1[mask_pill_1 < min_mask] = 0
mask_pill_2 = mask_2 - mask_r
mask_pill_2[mask_pill_2 < min_mask] = 0
mask_pill = mask_r + mask_pill_1 + mask_pill_2
# Overlap of elliptical parts may make value > 1:
mask_pill[mask_pill > 1] = 1
return mask_pill
def rectangle(pos, l, w, t):
return RectangularAperture(pos, l, w, t)
def square(pos, l, w, theta):
return RectangularAperture(pos, l, w, theta)
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"point" or "extended"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each plane of the IFU cube.
temp_flux : ndarray, 1-D
The sum of the data values in the extraction aperture minus the
sum of the data values in the background region (scaled by the
ratio of areas), for each plane.
The data values are in units of surface brightness, so this value
isn't really the flux, it's an intermediate value. Dividing by
`npixels` (to compute the average) will give the value for the
`surf_bright` (surface brightness) column, and multiplying by
the solid angle of a pixel will give the flux for a point source.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source data values to get `temp_flux`.
npixels : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `temp_flux`.
dq : ndarray, 1-D, uint32
The data quality array.
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate temp_flux, background, npixels, and dq arrays
# no matter what. We may need to divide by npixels, so the default
# is 1 rather than 0.
temp_flux = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
npixels = np.ones(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.uint32)
# For an extended target, the entire aperture will be extracted, so
# it makes no sense to shift the extraction location.
if source_type != "extended":
ra_targ = input_model.meta.target.ra
dec_targ = input_model.meta.target.dec
locn = locn_from_wcs(input_model, ra_targ, dec_targ)
if locn is None or np.isnan(locn[0]):
log.warning("Couldn't determine pixel location from WCS, so "
"nod/dither correction will not be applied.")
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[-1]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[-2]) / 2.
else:
y_center = float(y_center)
else:
(x_center, y_center) = locn
log.info("Using x_center = %g, y_center = %g, based on "
"TARG_RA and TARG_DEC.", x_center, y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[-2], shape[-1])) # for defaults
radius = None
inner_bkg = None
outer_bkg = None
if source_type == 'extended':
# Ignore any input parameters, and extract the whole image.
width = float(shape[-1])
height = float(shape[-2])
x_center = width / 2. - 0.5
y_center = height / 2. - 0.5
theta = 0.
subtract_background = False
else:
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(theta))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
x0 = float(shape[2]) / 2.
y0 = float(shape[1]) / 2.
(ra, dec, wavelength) = get_coordinates(input_model, x0, y0)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
else:
aperture = RectangularAperture(position, width, height, theta)
if subtract_background and inner_bkg is not None and outer_bkg is not None:
annulus = CircularAnnulus(position, r_in=inner_bkg, r_out=outer_bkg)
else:
annulus = None
# Compute the area of the aperture and possibly also of the annulus.
normalization = 1.
temp = np.ones(shape[-2:], dtype=np.float64)
phot_table = aperture_photometry(temp, aperture,
method=method, subpixels=subpixels)
aperture_area = float(phot_table['aperture_sum'][0])
if LooseVersion(photutils.__version__) >= '0.7':
log.debug("aperture.area = %g; aperture_area = %g",
aperture.area, aperture_area)
else:
log.debug("aperture.area() = %g; aperture_area = %g",
aperture.area(), aperture_area)
if subtract_background and annulus is not None:
# Compute the area of the annulus.
phot_table = aperture_photometry(temp, annulus,
method=method, subpixels=subpixels)
annulus_area = float(phot_table['aperture_sum'][0])
if LooseVersion(photutils.__version__) >= '0.7':
log.debug("annulus.area = %g; annulus_area = %g",
annulus.area, annulus_area)
else:
log.debug("annulus.area() = %g; annulus_area = %g",
annulus.area(), annulus_area)
if annulus_area > 0.:
normalization = aperture_area / annulus_area
else:
log.warning("Background annulus has no area, so background "
"subtraction will be turned off.")
subtract_background = False
del temp
npixels[:] = aperture_area
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
temp_flux[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
temp_flux[k] = temp_flux[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
(wavelength, temp_flux, background, npixels, dq) = \
nans_in_wavelength(wavelength, temp_flux, background, npixels, dq)
return (ra, dec, wavelength, temp_flux, background, npixels, dq)
def A_photometry(image_data,
bg_err,
factor=1,
ape_sum=None,
ape_sum_err=None,
cx=15,
cy=15,
r=2.5,
a=5,
b=5,
w_r=5,
h_r=5,
theta=0,
shape='Circular',
method='center'):
"""
Performs aperture photometry, first by creating the aperture (Circular,
Rectangular or Elliptical), then it sums up the flux that falls into the
aperture.
Args:
image_data (3D array): Data cube of images (2D arrays of pixel values).
bg_err (1D array): Array of uncertainties on pixel value.
factor (float, optional): Electron count to photon count factor. Default is 1 if none given.
ape_sum (1D array, optional): Array of flux to append new flux values to.
If None, the new values will be appended to an empty array
ape_sum_err (1D array, optional): Array of flux uncertainty to append new flux uncertainty values to.
If None, the new values will be appended to an empty array.
cx (int, optional): x-coordinate of the center of the aperture. Default is 15.
cy (int, optional): y-coordinate of the center of the aperture. Default is 15.
r (int, optional): If shape is 'Circular', r is the radius for the circular aperture. Default is 2.5.
a (int, optional): If shape is 'Elliptical', a is the semi-major axis for elliptical aperture (x-axis). Default is 5.
b (int, optional): If shape is 'Elliptical', b is the semi-major axis for elliptical aperture (y-axis). Default is 5.
w_r (int, optional): If shape is 'Rectangular', w_r is the full width for rectangular aperture (x-axis). Default is 5.
h_r (int, optional): If shape is 'Rectangular', h_r is the full height for rectangular aperture (y-axis). Default is 5.
theta (int, optional): If shape is 'Elliptical' or 'Rectangular', theta is the angle of the rotation angle in radians
of the semimajor axis from the positive x axis. The rotation angle increases counterclockwise. Default is 0.
shape (string, optional): shape is the shape of the aperture. Possible aperture shapes are 'Circular',
'Elliptical', 'Rectangular'. Default is 'Circular'.
method (string, optional): The method used to determine the overlap of the aperture on the pixel grid. Possible
methods are 'exact', 'subpixel', 'center'. Default is 'center'.
Returns:
tuple: ape_sum (1D array) Array of flux with new flux appended.
ape_sum_err (1D array) Array of flux uncertainties with new flux uncertainties appended.
"""
if ape_sum is None:
ape_sum = []
if ape_sum_err is None:
ape_sum_err = []
l, h, w = image_data.shape
tmp_sum = []
tmp_err = []
movingCentroid = (isinstance(cx, Iterable) or isinstance(cy, Iterable))
if not movingCentroid:
position = [cx, cy]
if (shape == 'Circular'):
aperture = CircularAperture(position, r=r)
elif (shape == 'Elliptical'):
aperture = EllipticalAperture(position, a=a, b=b, theta=theta)
elif (shape == 'Rectangular'):
aperture = RectangularAperture(position, w=w_r, h=h_r, theta=theta)
for i in range(l):
if movingCentroid:
position = [cx[i], cy[i]]
if (shape == 'Circular'):
aperture = CircularAperture(position, r=r)
elif (shape == 'Elliptical'):
aperture = EllipticalAperture(position, a=a, b=b, theta=theta)
elif (shape == 'Rectangular'):
aperture = RectangularAperture(position,
w=w_r,
h=h_r,
theta=theta)
data_error = calc_total_error(image_data[i, :, :],
bg_err[i],
effective_gain=1)
phot_table = aperture_photometry(
image_data[i, :, :], aperture, error=data_error,
method=method) #, pixelwise_error=False)
tmp_sum.extend(phot_table['aperture_sum'] * factor)
tmp_err.extend(phot_table['aperture_sum_err'] * factor)
# removing outliers
tmp_sum = sigma_clip(tmp_sum, sigma=4, maxiters=2, cenfunc=np.ma.median)
tmp_err = sigma_clip(tmp_err, sigma=4, maxiters=2, cenfunc=np.ma.median)
ape_sum.extend(tmp_sum)
ape_sum_err.extend(tmp_err)
return ape_sum, ape_sum_err
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"POINT" or "EXTENDED"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each plane of the IFU cube.
temp_flux : ndarray, 1-D
The sum of the data values in the extraction aperture minus the
sum of the data values in the background region (scaled by the
ratio of areas), for each plane.
The data values are in units of surface brightness, so this value
isn't really the flux, it's an intermediate value. Dividing by
`npixels` (to compute the average) will give the value for the
`surf_bright` (surface brightness) column, and multiplying by
the solid angle of a pixel will give the flux for a point source.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source data values to get `temp_flux`.
npixels : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `temp_flux`.
dq : ndarray, 1-D, uint32
The data quality array.
npixels_annulus : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `temp_flux` for an annulus region.
radius_match: ndarray,1-D, float64
The size of the extract radius in pixels used at each wavelength of the IFU cube
x_center, y_center : float
The x and y center of the extraction region
"""
data = input_model.data
weightmap = input_model.weightmap
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate temp_flux, background, npixels, and dq arrays
# no matter what. We may need to divide by npixels, so the default
# is 1 rather than 0.
temp_flux = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
npixels = np.ones(shape[0], dtype=np.float64)
npixels_annulus = np.ones(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.uint32)
# For an extended target, the entire aperture will be extracted, so
# it makes no sense to shift the extraction location.
if source_type != "EXTENDED":
ra_targ = input_model.meta.target.ra
dec_targ = input_model.meta.target.dec
locn = locn_from_wcs(input_model, ra_targ, dec_targ)
if locn is None or np.isnan(locn[0]):
log.warning("Couldn't determine pixel location from WCS, so "
"source offset correction will not be applied.")
x_center = float(shape[-1]) / 2.
y_center = float(shape[-2]) / 2.
else:
(x_center, y_center) = locn
log.info("Using x_center = %g, y_center = %g, based on "
"TARG_RA and TARG_DEC.", x_center, y_center)
method = extract_params['method']
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
radius = None
inner_bkg = None
outer_bkg = None
width = None
height = None
theta = None
# pull wavelength plane out of input data.
# using extract 1d wavelength, interpolate the radius, inner_bkg, outer_bkg to match input wavelength
# find the wavelength array of the IFU cube
x0 = float(shape[2]) / 2.
y0 = float(shape[1]) / 2.
(ra, dec, wavelength) = get_coordinates(input_model, x0, y0)
# interpolate the extraction parameters to the wavelength of the IFU cube
radius_match = None
if source_type == 'POINT':
wave_extract = extract_params['wavelength'].flatten()
inner_bkg = extract_params['inner_bkg'].flatten()
outer_bkg = extract_params['outer_bkg'].flatten()
radius = extract_params['radius'].flatten()
frad = interp1d(wave_extract, radius, bounds_error=False, fill_value="extrapolate")
radius_match = frad(wavelength)
# radius_match is in arc seconds - need to convert to pixels
# the spatial scale is the same for all wavelengths do we only need to call compute_scale once.
if locn is None:
locn_use = (input_model.meta.wcsinfo.crval1, input_model.meta.wcsinfo.crval2, wavelength[0])
else:
locn_use = (ra_targ, dec_targ, wavelength[0])
scale_degrees = compute_scale(
input_model.meta.wcs,
locn_use,
disp_axis=input_model.meta.wcsinfo.dispersion_direction)
scale_arcsec = scale_degrees*3600.00
radius_match /= scale_arcsec
finner = interp1d(wave_extract, inner_bkg, bounds_error=False, fill_value="extrapolate")
inner_bkg_match = finner(wavelength)/scale_arcsec
fouter = interp1d(wave_extract, outer_bkg, bounds_error=False, fill_value="extrapolate")
outer_bkg_match = fouter(wavelength)/scale_arcsec
elif source_type == 'EXTENDED':
# Ignore any input parameters, and extract the whole image.
width = float(shape[-1])
height = float(shape[-2])
x_center = width / 2. - 0.5
y_center = height / 2. - 0.5
theta = 0.
subtract_background = False
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'POINT':
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(theta))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
position = (x_center, y_center)
# get aperture for extended it will not change with wavelength
if source_type == 'EXTENDED':
aperture = RectangularAperture(position, width, height, theta)
annulus = None
for k in range(shape[0]):
inner_bkg = None
outer_bkg = None
if source_type == 'POINT':
radius = radius_match[k] # this radius has been converted to pixels
aperture = CircularAperture(position, r=radius)
inner_bkg = inner_bkg_match[k]
outer_bkg = outer_bkg_match[k]
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
if subtract_background and inner_bkg is not None and outer_bkg is not None:
annulus = CircularAnnulus(position, r_in=inner_bkg, r_out=outer_bkg)
else:
annulus = None
subtract_background_plane = subtract_background
# Compute the area of the aperture and possibly also of the annulus.
# for each wavelength bin (taking into account empty spaxels)
normalization = 1.
temp = weightmap[k,:,:]
temp[temp>1] = 1
aperture_area = 0
annulus_area = 0
# aperture_photometry - using weight map
phot_table = aperture_photometry(temp, aperture,
method=method, subpixels=subpixels)
aperture_area = float(phot_table['aperture_sum'][0])
if LooseVersion(photutils.__version__) >= '0.7':
log.debug("aperture.area = %g; aperture_area = %g",
aperture.area, aperture_area)
else:
log.debug("aperture.area() = %g; aperture_area = %g",
aperture.area(), aperture_area)
if(aperture_area ==0 and aperture.area > 0):
aperture_area = aperture.area
if subtract_background and annulus is not None:
# Compute the area of the annulus.
phot_table = aperture_photometry(temp, annulus,
method=method, subpixels=subpixels)
annulus_area = float(phot_table['aperture_sum'][0])
if LooseVersion(photutils.__version__) >= '0.7':
log.debug("annulus.area = %g; annulus_area = %g",
annulus.area, annulus_area)
else:
log.debug("annulus.area() = %g; annulus_area = %g",
annulus.area(), annulus_area)
if(annulus_area ==0 and annulus.area > 0):
annulus_area = annulus.area
if annulus_area > 0.:
normalization = aperture_area / annulus_area
else:
log.warning("Background annulus has no area, so background "
"subtraction will be turned off. %g" ,k)
subtract_background_plane = False
del temp
npixels[k] = aperture_area
npixels_annulus[k] = 0.0
if annulus is not None:
npixels_annulus[k] = annulus_area
# aperture_photometry - using data
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
temp_flux[k] = float(phot_table['aperture_sum'][0])
if subtract_background_plane:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
temp_flux[k] = temp_flux[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
(wavelength, temp_flux, background, npixels, dq, npixels_annulus) = \
nans_in_wavelength(wavelength, temp_flux, background, npixels, dq, npixels_annulus)
return (ra, dec, wavelength, temp_flux, background, npixels, dq,
npixels_annulus, radius_match, x_center, y_center)
def A_photometry(bg_err,
cx=15,
cx_med=15,
cy=15,
cy_med=15,
r=[2.5],
a=[5],
b=[5],
w_r=[5],
h_r=[5],
theta=[0],
scale=1,
shape='Circular',
methods=['center', 'exact'],
moveCentroids=[True],
i=0,
img_data=None):
"""Performs aperture photometry, first by creating the aperture then summing the flux within the aperture.
Note that this will implicitly use the global variable image_stack (3D array) to allow for parallel computing
with large (many GB) datasets.
Args:
bg_err (1D array): Array of uncertainties on pixel value.
cx (int/array, optional): x-coordinate(s) of the center of the aperture. Default is 15.
cy (int/array, optional): y-coordinate(s) of the center of the aperture. Default is 15.
r (int/array, optional): If shape is 'Circular', the radii to try for aperture photometry
in units of pixels. Default is just 2.5 pixels.
a (int/array, optional): If shape is 'Elliptical', the semi-major axes to try for elliptical aperture
in units of pixels. Default is 5.
b (int/array, optional): If shape is 'Elliptical', the semi-minor axes to try for elliptical aperture
in units of pixels. Default is 5.
w_r (int/array, optional): If shape is 'Rectangular', the full widths to try for rectangular aperture (x-axis).
Default is 5.
h_r (int/array, optional): If shape is 'Rectangular', the full heights to try for rectangular aperture (y-axis).
Default is 5.
theta (int/array, optional): If shape is 'Elliptical' or 'Rectangular', the rotation angles in radians
of the semimajor axis from the positive x axis. The rotation angle increases counterclockwise. Default is 0.
scale (int, optional): If the image is oversampled, scaling factor for centroid and bounds,
i.e, give centroid in terms of the pixel value of the initial image.
shape (string, optional): shape is the shape of the aperture. Possible aperture shapes are 'Circular',
'Elliptical', 'Rectangular'. Default is 'Circular'.
methods (iterable, optional): The methods used to determine the overlap of the aperture on the pixel grid. Possible
methods are 'exact', 'subpixel', 'center'. Default is ['center', 'exact'].
i (int, optional): The current frame number being examined.
img_data (3D array): The image stack being analyzed if not using the global variable to allow for parallel computing.
Returns:
tuple: results (2D array) Array of flux and flux errors, of shape (nMethods*nSizes, 2), where the nSizes loop
is nested inside the nMethods loop which is itself nested inside the moveCentoids loop.
"""
# Access the global variable
if img_data is None:
global image_stack
else:
image_stack = img_data
if not isinstance(r, Iterable):
r = [r]
if not isinstance(cx, Iterable):
cx = [cx]
if not isinstance(cy, Iterable):
cy = [cy]
if not isinstance(a, Iterable):
a = [a]
if not isinstance(b, Iterable):
b = [b]
if not isinstance(w_r, Iterable):
w_r = [w_r]
if not isinstance(h_r, Iterable):
h_r = [h_r]
if not isinstance(theta, Iterable):
theta = [theta]
if not isinstance(methods, Iterable):
methods = [methods]
data_error = calc_total_error(image_stack[i, :, :],
bg_err[i],
effective_gain=1)
results = []
for moveCentroid in moveCentroids:
# Set up aperture(s)
apertures = []
if not moveCentroid:
position = [cx_med * scale, cy_med * scale]
else:
position = [cx[i] * scale, cy[i] * scale]
if (shape == 'Circular'):
for j in range(len(r)):
apertures.append(CircularAperture(position, r=r[j] * scale))
elif (shape == 'Elliptical'):
for j in range(len(a)):
apertures.append(
EllipticalAperture(position,
a=a[j] * scale,
b=b[j] * scale,
theta=theta[j]))
elif (shape == 'Rectangular'):
for j in range(len(w_r)):
apertures.append(
RectangularAperture(position,
w=w_r[j] * scale,
h=h_r[j] * scale,
theta=theta[j]))
for method in methods:
phot_table = aperture_photometry(image_stack[i, :, :],
apertures,
error=data_error,
method=method)
results.extend([
float(phot_table[f'aperture_sum_{j}'])
for j in range(len(apertures))
])
return np.array(results)
def show_stamps(pscs,
frame_idx=None,
stamp_size=11,
aperture_position=None,
aperture_size=None,
show_residual=False,
stretch=None,
save_name=None,
show_max=False,
show_pixel_grid=False,
**kwargs):
if aperture_position is None:
midpoint = (stamp_size - 1) / 2
aperture_position = (midpoint, midpoint)
if aperture_size:
aperture = RectangularAperture(aperture_position,
w=aperture_size,
h=aperture_size,
theta=0)
ncols = len(pscs)
if show_residual:
ncols += 1
nrows = 1
fig = Figure()
FigureCanvas(fig)
fig.set_figheight(4)
fig.set_figwidth(8)
if frame_idx is not None:
s0 = pscs[0][frame_idx]
s1 = pscs[1][frame_idx]
else:
s0 = pscs[0]
s1 = pscs[1]
if stretch == 'log':
stretch = LogStretch()
else:
stretch = LinearStretch()
norm = ImageNormalize(s0, interval=MinMaxInterval(), stretch=stretch)
ax1 = fig.add_subplot(nrows, ncols, 1)
im = ax1.imshow(s0, cmap=get_palette(), norm=norm)
if aperture_size:
aperture.plot(color='r', lw=4, ax=ax1)
# annulus.plot(color='c', lw=2, ls='--', ax=ax1)
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
# https://stackoverflow.com/questions/18195758/set-matplotlib-colorbar-size-to-match-graph
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(im, cax=cax)
ax1.set_title('Target')
# Comparison
ax2 = fig.add_subplot(nrows, ncols, 2)
im = ax2.imshow(s1, cmap=get_palette(), norm=norm)
if aperture_size:
aperture.plot(color='r', lw=4, ax=ax1)
# annulus.plot(color='c', lw=2, ls='--', ax=ax1)
divider = make_axes_locatable(ax2)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(im, cax=cax)
ax2.set_title('Comparison')
if show_pixel_grid:
add_pixel_grid(ax1, stamp_size, stamp_size, show_superpixel=False)
add_pixel_grid(ax2, stamp_size, stamp_size, show_superpixel=False)
if show_residual:
ax3 = fig.add_subplot(nrows, ncols, 3)
# Residual
residual = s0 - s1
im = ax3.imshow(residual,
cmap=get_palette(),
norm=ImageNormalize(residual,
interval=MinMaxInterval(),
stretch=LinearStretch()))
divider = make_axes_locatable(ax3)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(im, cax=cax)
ax3.set_title('Noise Residual')
ax3.set_title('Residual RMS: {:.01%}'.format(residual.std()))
ax3.set_yticklabels([])
ax3.set_xticklabels([])
if show_pixel_grid:
add_pixel_grid(ax1, stamp_size, stamp_size, show_superpixel=False)
# Turn off tick labels
ax1.set_yticklabels([])
ax1.set_xticklabels([])
ax2.set_yticklabels([])
ax2.set_xticklabels([])
if save_name:
try:
fig.savefig(save_name)
except Exception as e:
warn("Can't save figure: {}".format(e))
return fig
def asymmetric_elongated_aperture_photom(scidata, starts, inverted, jpeg='', plot=False, sat_adu = 16383, spectral_band='G'):
'''
Compute photometry using rectangular apertures.
This is a good method to use for "April 2014" de Bruijn encoding
'''
logger = logging.getLogger()
logger.debug(f'science image is dimensions: {np.shape(scidata)}')
match, factor = table_matches_image(starts, scidata)
if not match:
logger.warning(f'table data does not match image data. Dividing table coordinates by a factor of {factor:.2f} to table data (spectral band is {spectral_band})')
starts['x_image'] /= factor
starts['y_image'] /= factor
positions, radii, thetas = aperture_centers_weighted_start_differential(starts)
# get median pixel value inside the fireball bounding box
median_bckgnd = np.median(get_bounding_box(scidata, starts))
if plot:
plt.close()
fig = plt.gcf()
ax = fig.gca()
plt.imshow(jpeg, cmap=plt.cm.gray)
starts['aperture_sum'] = np.nan
starts['bckgng_sub_measurements'] = np.nan
starts['SNR'] = np.nan
starts['signal'] = np.nan
starts['exp_time'] = np.nan
starts['aperture_sum_err'] = np.nan
starts['aperture_sum_err_plus'] = np.nan
starts['aperture_sum_err_minus'] = np.nan
starts['aperture_saturated'] = False
for i in range(len(starts)-1):
starts[i]['exp_time'] = LC_open_time(starts, i, i+1, inverted)
for el, pos, radius, theta in zip(starts[:-1], positions, radii, thetas):
#zero_order_light = scidata[int(pos[1])][int(pos[0])]
aperture = RectangularAperture(pos, w=radius*2, h=7.*2, theta=theta) # 7
# determine if any of the pixels in the aperture have reached saturation level,
# flag accordingly
aperture_mask = aperture.to_mask('center')
aperture_values = aperture_mask.multiply(scidata)
if np.any(aperture_values == sat_adu):
el['aperture_saturated'] = True
# do photometry
aperture_result = aperture_photometry(scidata, aperture, method='subpixel', subpixels=16)
el['aperture_sum'] = aperture_result['aperture_sum'].data
#el['aperture_sum_err'] = aperture_result['aperture_sum_err'].data TODO FIXME
el['bckgng_sub_measurements'] = el['aperture_sum'] - aperture.area*median_bckgnd
el['SNR'] = el['aperture_sum'] / np.sqrt(el['aperture_sum'] + aperture.area*median_bckgnd)
el['aperture_sum_err_plus'] = 1/el['SNR']
el['aperture_sum_err_minus'] = 1/el['SNR']
if plot:
aperture.plot(color='white')
if plot:
#min_x, min_y, max_x, max_y = get_bounds([p['x_image'] for p in points], pos2)
ax.set_xlim([np.min(starts['x_image']), np.max(starts['x_image'])])
ax.set_ylim([np.min(starts['y_image']), np.max(starts['y_image'])])
full_path = starts.meta['self_file_name']
dirname = os.path.dirname(full_path)
basename = os.path.basename(full_path).split('.')[0]
fname = os.path.join(dirname, basename + "_aperture_photometry_"+spectral_band+".jpg")
plt.savefig(fname, dpi=150)
return starts
def analyse_source(source, cube, plot=False, return_fit_params=False):
"""
Convenience method to spatially analyse a source.
A 30x30 pixels 'flux map' is build from the sum of a few frames around each detected lines for the source.
Two analysis are then performed:
* Aperture photometry from the center of the source, to estimate a flux growth function and fit it with a custom erf function.
* A Gaussian 2D fit of the PSF on the flux map
This method can be used in a parallel process.
Parameters
----------
source : :class:`~pandas:pandas.Series`
A row from a :class:`~pandas:pandas.DataFrame` containing detected sources. Should have columns ``xpos``, ``ypos`` (Not astropy convention), ``velocity``, ``*_detected`` whare * is a line name, containing True or False for each line.
cube : :class:`~ORCS:orcs.process.SpectralCube`
SpectralCube instance where we are looking at the source
plot : bool, Default = False
(Optional) If True, the two fits are plotted
return_fit_params : bool, Default = False
(Optional) If True, returns the full fits parameters
Returns
-------
res : dict
A dictionnary containing all the relevant fitted quantities.
+-----------------------+---------------------------------------------------------------------------------------------------+
|Parameter |Description |
+=======================+===================================================================================================+
|flux_map_ks_pvalue |Estimates the 'randomness' of the flux map, i.e if it's just noise or if we actually have a signal |
+-----------------------+---------------------------------------------------------------------------------------------------+
|flux_r |Flux at different radius *r* |
+-----------------------+---------------------------------------------------------------------------------------------------+
|flux_err_r |Flux error varying with *r* |
+-----------------------+---------------------------------------------------------------------------------------------------+
|erf_amplitude |Amplitude estimated from erf fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|erf_amplitude_err |Amplitude error |
+-----------------------+---------------------------------------------------------------------------------------------------+
|erf_xfwhm |x-axis fwhm from erf fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|erf_yfwhm |y-axis fwhm from erf fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|erf_fwhm |Fwhm defined as *r* at which half of the max flux is reached |
+-----------------------+---------------------------------------------------------------------------------------------------+
|flux_fraction_3 |Ratio between flux measured at 3 pixels from the center and max flux |
+-----------------------+---------------------------------------------------------------------------------------------------+
|model_flux_fraction_15 |Ratio between estimated flux at 15 pixels from the center and max flux |
+-----------------------+---------------------------------------------------------------------------------------------------+
|modeled_flux_r |Modeled flux varying with *r* |
+-----------------------+---------------------------------------------------------------------------------------------------+
|psf_snr |Ratio between amplitude of the 2D fit and noise in the flux map |
+-----------------------+---------------------------------------------------------------------------------------------------+
|psf_amplitude |Amplitude of the 2D fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|psf_xfwhm |x-axis fwhm from 2D fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|psf_yfwhm |y-axis fwhm from 2D fit |
+-----------------------+---------------------------------------------------------------------------------------------------+
|psf_ks_pvalue |Randomness of the residuals map |
+-----------------------+---------------------------------------------------------------------------------------------------+
"""
result = {}
try:
from astropy.stats import sigma_clipped_stats, gaussian_sigma_to_fwhm
from sitelle.constants import SN2_LINES, SN3_LINES
from sitelle.region import centered_square_region
from orb.utils.spectrum import line_shift
from orb.core import Lines
filter_name = cube.params.filter_name
if filter_name == 'SN2':
LINES = SN2_LINES
elif filter_name == 'SN3':
LINES = SN3_LINES
else:
raise ValueError(filter_name)
## We build a flux map of the detected lines
try:
detected_lines = [
line_name for line_name in LINES
if source['%s_detected' %
line_name.lower().replace('[', '').replace(']', '')]
]
except KeyError as e:
raise ValueError('No columns *_detected in the source')
if detected_lines == []:
return pd.Series(result)
x, y = source.as_matrix(['xpos', 'ypos']).astype(int)
big_box = centered_square_region(x, y, 30)
medium_box = centered_square_region(15, 15, 5)
small_box = centered_square_region(15, 15, 3)
data = cube._extract_spectra_from_region(big_box, silent=True)
mask = np.ones((30, 30))
mask[medium_box] = 0
bkg_spec = np.nanmedian(data[np.nonzero(mask)], axis=0)
data -= bkg_spec
axis = cube.params.base_axis
spec = np.nansum(data[small_box], axis=0)
line_pos = np.atleast_1d(
Lines().get_line_cm1(detected_lines) +
line_shift(source['velocity'],
Lines().get_line_cm1(detected_lines),
wavenumber=True))
pos_min = line_pos - cube.params.line_fwhm
pos_max = line_pos + cube.params.line_fwhm
pos_index = np.array([[
np.argmin(np.abs(axis - pos_min[i])),
np.argmin(np.abs(axis - pos_max[i]))
] for i in range(pos_min.shape[0])])
bandpass_size = 0
flux_map = np.zeros(data.shape[:-1])
for line_detection in pos_index:
bandpass_size += line_detection[1] - line_detection[0]
flux_map += np.nansum(data[:, :,
line_detection[0]:line_detection[1]],
axis=-1)
_, _, std_map = sigma_clipped_stats(data, axis=-1)
flux_noise_map = np.sqrt(bandpass_size) * std_map
#Test for randomness of the flux_map
from scipy import stats
result['flux_map_ks_pvalue'] = stats.kstest(
(flux_map / flux_noise_map).flatten(), 'norm').pvalue
#Fit of the growth function
from photutils import RectangularAperture
from scipy.special import erf
from scipy.optimize import curve_fit
try:
_x0 = source['xcentroid'] - x + 15.
_y0 = source['ycentroid'] - y + 15.
except:
_x0 = source['xpos'] - x + 15.
_y0 = source['ypos'] - y + 15.
flux_r = [0.]
flux_err_r = [np.nanmin(flux_noise_map)]
r_max = 15
r_range = np.arange(1, r_max + 1)
for r in r_range:
# aper = CircularAperture((_x0,_y0), r)
aper = RectangularAperture((_x0, _y0), r, r, 0)
flux_r.append(aper.do_photometry(flux_map)[0][0])
flux_err_r.append(
np.sqrt(aper.do_photometry(flux_noise_map**2)[0][0]))
flux_r = np.atleast_1d(flux_r)
flux_err_r = np.atleast_1d(flux_err_r)
result['flux_r'] = flux_r
result['flux_err_r'] = flux_err_r
try:
def model(r, x0, y0, sx, sy, A):
return A * erf((r / 2. - x0) / (2 * sx * np.sqrt(2))) * erf(
(r / 2. - y0) / (2 * sy * np.sqrt(2)))
R = np.arange(r_max + 1)
p, cov = curve_fit(model,
R,
flux_r,
p0=[0, 0, 1.5, 1.5,
flux_map.max()],
bounds=([-2, -2, -np.inf, -np.inf, -np.inf],
[2, 2, np.inf, np.inf, np.inf]),
sigma=flux_err_r,
absolute_sigma=True,
maxfev=10000)
if (p[2] < 0) != (p[3] < 0):
if p[-1] < 0:
p[-1] = -p[-1]
if p[2] < 0:
p[2] = -p[2]
elif p[3] < 0:
p[3] = -p[3]
if plot:
f, ax = plt.subplots()
ax.plot(R, model(R, *p), label='Fit')
ax.errorbar(R, flux_r, flux_err_r, label='Flux')
ax.set_ylabel('Flux')
ax.set_xlabel('Radius from source')
ax.legend()
from scipy.optimize import bisect
fwhm = bisect(lambda x: model(x, *p) - p[-1] / 2, 0.1, 10)
result['erf_amplitude'] = p[-1]
result['erf_amplitude_err'] = np.sqrt(np.diag(cov))[-1]
result['erf_xfwhm'] = gaussian_sigma_to_fwhm * p[2]
result['erf_yfwhm'] = gaussian_sigma_to_fwhm * p[3]
result['erf_ks_pvalue'] = stats.kstest(
(flux_r - model(R, *p)) / flux_err_r, 'norm').pvalue
result['erf_fwhm'] = fwhm
result['flux_fraction_3'] = flux_r[3] / p[-1]
result['model_flux_fraction_15'] = model(R, *
p)[r_range[-1]] / p[-1]
result['modeled_flux_r'] = model(R, *p)
except Exception as e:
print(e)
pass
## 2D fit of the PSF
from astropy.modeling import models, fitting
fitter = fitting.LevMarLSQFitter()
X, Y = np.mgrid[:30, :30]
flux_std = np.nanmean(flux_noise_map)
gauss_model = models.Gaussian2D(amplitude=np.nanmax(flux_map /
flux_std),
x_mean=_y0,
y_mean=_x0)
gauss_model.bounds['x_mean'] = (14, 16)
gauss_model.bounds['y_mean'] = (14, 16)
gauss_fit = fitter(gauss_model, X, Y, flux_map / flux_std)
if plot is True:
f, ax = plt.subplots(1, 3, figsize=(8, 3))
v_min = np.nanmin(flux_map)
v_max = np.nanmax(flux_map)
plot_map(flux_map, ax=ax[0], cmap='RdBu_r', vmin=v_min, vmax=v_max)
ax[0].set_title("Data")
plot_map(gauss_fit(X, Y) * flux_std,
ax=ax[1],
cmap='RdBu_r',
vmin=v_min,
vmax=v_max)
ax[1].set_title("Model")
plot_map(flux_map - gauss_fit(X, Y) * flux_std,
ax=ax[2],
cmap='RdBu_r',
vmin=v_min,
vmax=v_max)
ax[2].set_title("Residual")
result['psf_snr'] = gauss_fit.amplitude[0]
result['psf_amplitude'] = flux_std * gauss_fit.amplitude[
0] * 2 * np.pi * gauss_fit.x_stddev * gauss_fit.y_stddev
result['psf_xfwhm'] = gauss_fit.x_fwhm
result['psf_yfwhm'] = gauss_fit.y_fwhm
normalized_res = (flux_map -
gauss_fit(X, Y) * flux_std) / flux_noise_map
result['psf_ks_pvalue'] = stats.kstest(normalized_res.flatten(),
'norm').pvalue
if return_fit_params:
return pd.Series(result), p, gauss_fit
else:
return pd.Series(result)
except Exception as e:
print e
return pd.Series(result)
def measure_flux(fitsimage,
detections=None,
pixel_coords=None,
skycoords=None,
method='single-aperture',
a=None,
b=None,
theta=None,
n_boostrap=100,
minimal_aperture_size=1,
aperture_size=None,
aperture_scale=6.0,
gaussian_segment_scale=4.0,
plot=False,
ax=None,
color='white',
debug=False):
"""Accurate flux measure
Args:
fitsimage: the FitsImage class
detections: astropy.table, including all source position and shapes
pixel_coords: the pixel coordinates of the detections
skycoords: the sky coordinates of the detections.
aperture_size: the fixed size of the aperture, in arcsec
a,b,theta: the size of the source, in arcsec and deg
minimal_aperture_size: if the aperture_size is None, this can control the
minial aperture_size for the fain source, where the adaptive aperture
could not be securely measured
aperture_scale: the source shape determined aperture, lower priority than
aperture_size
Note:
When several coordinates parameters are provided, detections has the
higher priority
"""
pixel2arcsec = fitsimage.pixel2deg_ra * 3600
arcsec2pixel = 1 / pixel2arcsec
if detections is not None:
if len(detections) < 1:
print('No source founded...')
return None, None
dets_colnames = detections.colnames
# if ('x' in dets_colnames) and ('y' in dets_colnames):
# pixel_coords = np.array(list(zip(detections['x'], detections['y'])))
if ('ra' in dets_colnames) and ('dec' in dets_colnames):
ra = np.array([detections['ra']])
dec = np.array([detections['dec']])
skycoords = SkyCoord(ra.flatten(), dec.flatten(), unit='deg')
pixel_coords = np.array(
list(zip(*skycoord_to_pixel(skycoords, fitsimage.wcs))))
if aperture_scale is not None:
if a is None: # in arcsec
if 'a' in detections.colnames:
a = detections['a']
else:
a = fitsimage.bmaj * 0.5 * 3600
if b is None:
if 'b' in detections.colnames:
b = detections['b']
else:
b = fitsimage.bmin * 0.5 * 3600
if theta is None: # in deg
if 'theta' in detections.colnames:
theta = detections['theta']
else:
theta = fitsimage.bpa
elif skycoords is not None:
pixel_coords = np.array(
list(zip(*skycoord_to_pixel(skycoords, fitsimage.wcs))))
if a is None:
a = fitsimage.bmaj * 0.5 * 3600
if b is None:
b = fitsimage.bmin * 0.5 * 3600
if theta is None:
theta = fitsimage.bpa # in deg
elif pixel_coords is not None:
if a is None:
a = fitsimage.bmaj * 0.5 * 3600
if b is None:
b = fitsimage.bmin * 0.5 * 3600
if theta is None:
theta = fitsimage.bpa # in deg
else:
print("Nothing to do...")
return None, None
n_sources = len(pixel_coords)
# define aperture for all the detections
if aperture_scale is not None:
if isinstance(a, (tuple, list, np.ndarray)):
a_aper = aperture_scale * a * arcsec2pixel
# a_aper = aperture_scale*a*u.arcsec
else:
a_aper = np.full(n_sources, aperture_scale * a * arcsec2pixel)
if isinstance(b, (tuple, list, np.ndarray)):
b_aper = aperture_scale * b * arcsec2pixel
# b_aper = aperture_scale*b*u.arcsec
else:
b_aper = np.full(n_sources, aperture_scale * b * arcsec2pixel)
if minimal_aperture_size is not None:
minimal_aperture_size_in_pixel = minimal_aperture_size * arcsec2pixel
a_aper[
a_aper <
minimal_aperture_size_in_pixel] = minimal_aperture_size_in_pixel
b_aper[
b_aper <
minimal_aperture_size_in_pixel] = minimal_aperture_size_in_pixel
if not isinstance(theta, (tuple, list, np.ndarray)):
theta = np.full(n_sources, theta)
if aperture_size is not None:
aperture_size_pixel = aperture_size * arcsec2pixel
a_aper = np.full(n_sources, aperture_size_pixel)
b_aper = np.full(n_sources, aperture_size_pixel)
theta = np.full(n_sources, 0)
apertures = []
for i, coord in enumerate(pixel_coords):
apertures.append(
EllipticalAperture(coord, a_aper[i], b_aper[i], theta[i]))
# apertures.append(SkyEllipticalAperture(skycoords, a_aper[i], b_aper[i], theta[i]))
detections_mask = np.zeros(fitsimage.imagesize, dtype=bool)
for mask in apertures:
image_aper_mask = mask.to_mask().to_image(shape=fitsimage.imagesize)
if image_aper_mask is not None:
detections_mask = detections_mask + image_aper_mask
else:
continue
detections_mask = (detections_mask > 0) | fitsimage.imagemask
detections_apers = []
flux = np.zeros(n_sources)
fluxerr = np.zeros(n_sources)
if method == 'single-aperture':
# measuring flux density
for i, aper in enumerate(apertures):
x, y = pixel_coords[i]
pixel_fluxscale = 1. / (fitsimage.beamsize)
detections_apers.append(
EllipticalAperture([x, y], a_aper[i], b_aper[i], theta[i]))
if fitsimage.has_pbcor:
phot_table = aperture_photometry(fitsimage.image_pbcor,
aper,
mask=fitsimage.imagemask)
else:
phot_table = aperture_photometry(fitsimage.image,
aper,
mask=fitsimage.imagemask)
flux[i] = phot_table['aperture_sum'].value * pixel_fluxscale
# measuring the error of flux density
# run the boostrap for random aperture with the image
pixel_x = np.random.random(n_boostrap) * fitsimage.imagesize[
1] # 1 for x axis
pixel_y = np.random.random(n_boostrap) * fitsimage.imagesize[
0] # 0 for y axis
# points_select = (pixel_x**2 + pixel_y**2) < (np.min(fitsimage.imagesize)-np.max([a,b]))**2
# points_select = (pixel_x-**2 + pixel_y**2) > (np.max([a,b]))**2
# pixel_coords_boostrap = np.vstack([pixel_x[points_select], pixel_y[points_select]]).T
pixel_coords_boostrap = np.vstack([pixel_x, pixel_y]).T
apertures_boostrap = EllipticalAperture(pixel_coords_boostrap,
a_aper[i], b_aper[i],
theta[i])
noise_boostrap = aperture_photometry(fitsimage.image,
apertures_boostrap,
mask=detections_mask)
fluxerr[i] = np.std(
np.ma.masked_invalid(
noise_boostrap['aperture_sum'])) * pixel_fluxscale
# fluxerr[i] = np.std(noise_boostrap['aperture_sum']) * pixel_fluxscale
if fitsimage.has_pbcor:
pixel_pbcor = 1. / fitsimage.image_pb[int(np.round(x)),
int(np.round(y))]
fluxerr[i] = fluxerr[i] * pixel_pbcor
if method == 'gaussian':
seg_size = gaussian_segment_scale * np.int(fitsimage.bmaj_pixel)
segments = RectangularAperture(pixel_coords,
seg_size,
seg_size,
theta=0)
segments_mask = segments.to_mask(method='center')
for i, s in enumerate(segments_mask):
x, y = pixel_coords[i]
pixel_fluxscale = 1 / (fitsimage.beamsize)
image_cutout = s.cutout(fitsimage.image)
gaussian_fitting = gaussian_2Dfitting(image_cutout, debug=debug)
flux[i] = gaussian_fitting['flux'] * pixel_fluxscale
# boostrap for noise measurement
a_fitted_aper = 1.0 * 2.355 * gaussian_fitting[
'x_stddev'] # 2xFWHM of gaussian
b_fitted_aper = 1.0 * 2.355 * gaussian_fitting['y_stddev']
theta_fitted = gaussian_fitting['theta']
detections_apers.append(
EllipticalAperture([x, y], a_fitted_aper, b_fitted_aper,
theta_fitted))
pixel_x = np.random.random(n_boostrap) * fitsimage.imagesize[
1] # 1 for x axis
pixel_y = np.random.random(n_boostrap) * fitsimage.imagesize[
0] # 0 for y axis
pixel_coords_boostrap = np.vstack([pixel_x, pixel_y]).T
apertures_boostrap = EllipticalAperture(pixel_coords_boostrap,
a_fitted_aper,
b_fitted_aper,
theta_fitted)
noise_boostrap = aperture_photometry(fitsimage.image,
apertures_boostrap,
mask=detections_mask)
fluxerr[i] = np.std(
np.ma.masked_invalid(
noise_boostrap['aperture_sum'])) * pixel_fluxscale
if fitsimage.has_pbcor:
pixel_pbcor = 1. / fitsimage.image_pb[int(np.round(x)),
int(np.round(y))]
flux[i] = flux[i] * pixel_pbcor
fluxerr[i] = fluxerr[i] * pixel_pbcor
if plot:
if ax is None:
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(fitsimage.image,
interpolation='nearest',
vmin=-0.2 * fitsimage.std,
vmax=10.0 * fitsimage.std,
origin='lower')
plt.colorbar(im, fraction=0.046, pad=0.04)
for i in range(n_sources):
obj = pixel_coords[i]
im = detections_apers[i].plot(color=color, lw=1, alpha=0.8)
ax.text(
obj[0],
(1.0 - 2.0 * detections_apers[i].a / fitsimage.imagesize[0]) *
obj[1],
"{:.2f}mJy".format(flux[i] * 1e3),
color=color,
horizontalalignment='center',
verticalalignment='top',
)
# # only for test
# for ap in apertures_boostrap:
# im = ap.plot(color='gray', lw=2, alpha=0.2)
return flux, fluxerr