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prf.py
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prf.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 10:04:24 2017
@author: yangyang
"""
from __future__ import print_function, division
from astropy.io import fits
import numpy as np
from scipy.interpolate import RectBivariateSpline
from scipy.ndimage import interpolation
from scipy.optimize import fmin_powell
from scipy.stats import multivariate_normal
import batman
import sys, os, re, time, os.path, glob
import math
import time
import argparse
import logging
import model
import diffimg
from PRFfunc import PRF, PRF2DET
from astropy.wcs import WCS
import matplotlib.pylab as plt
def pixel_info(pixel_file):
pf = fits.open(pixel_file)
kepid = pf[0].header['KEPLERID']
channel = pf[0].header['CHANNEL']
try:
skygroup = pf[0].header['SKYGROUP']
skygroup = str(skygroup)
except:
skygroup = '0'
module = pf[0].header['MODULE']
output = pf[0].header['OUTPUT']
campaign = pf[0].header['CAMPAIGN']
data_rel = pf[0].header['DATA_REL']
try:
season = pf[0].header['SEASON']
season = str(season)
except:
season = '0'
ra = pf[0].header['RA_OBJ']
dec = pf[0].header['DEC_OBJ']
kepmag = pf[0].header['KEPMAG']
tdim5 = pf['TARGETTABLES'].header['TDIM5']
xdim = int(tdim5.strip().strip('(').strip(')').split(',')[0])
ydim = int(tdim5.strip().strip('(').strip(')').split(',')[1])
crv5p1 = pf['TARGETTABLES'].header['1CRV5P']
column = crv5p1
crv5p2 = pf['TARGETTABLES'].header['2CRV5P']
row = crv5p2
return (kepid, channel, skygroup, module, output, campaign, data_rel, season,\
ra, dec, column, row, kepmag, xdim, ydim)
def prf_info(prf_file, hdu):
"""read pixel response file"""
prf = fits.open(prf_file)
# read bitmap image
img = prf[hdu].data
naxis1 = prf[hdu].header['NAXIS1']
naxis2 = prf[hdu].header['NAXIS2']
# read WCS keywords
crpix1p = prf[hdu].header['CRPIX1P']
crpix2p = prf[hdu].header['CRPIX2P']
crval1p = prf[hdu].header['CRVAL1p']
crval2p = prf[hdu].header['CRVAL2p']
cdelt1p = prf[hdu].header['CDELT1P']
cdelt2p = prf[hdu].header['CDELT2P']
prf.close()
return img, crpix1p, crpix2p, crval1p, crval2p, cdelt1p, cdelt2p
def bitInBitmap(bitmap, bit):
"""bit map decoding"""
flag = False
for i in range(10, -1,- 1):
if bitmap - 2**i >= 0:
bitmap = bitmap - 2**i
if 2**i == bit:
flag = True
else:
continue
return flag
def intScale2D(image, imscale):
"""intensity scale limits of 2d array"""
nstat = 2
work1 = np.array([], dtype=np.float32)
(ysiz, xsiz) = np.shape(image)
for i in range(ysiz):
for j in range(xsiz):
if np.isfinite(image[i, j]) and image[i, j] > 0.0:
work1 = np.append(work1, image[i, j])
work2 = np.array(np.sort(work1))
if int(float(len(work2)) / 1000 + 0.5) > nstat:
nstat = int(float(len(work2)) / 1000 + 0.5)
zmin = np.median(work2[:nstat])
zmax = np.median(work2[-nstat:])
if imscale == 'logarithmic':
image = np.log10(image)
zmin = math.log10(zmin)
zmax = math.log10(zmax)
if imscale == 'squareroot':
image = np.sqrt(image)
zmin = math.sqrt(zmin)
zmax = math.sqrt(zmax)
return image, zmin, zmax
def prf(DATimg, DATimg_eff, or_pix_file, prfdir, maskimg, columns, rows, fluxes, projection, background=False,\
border=1, focus=False, xtol=1e-4, ftol=0.0001, outfile=None, plot=False,\
imscale='linear', cmap='YlOrBr', apercol='#ffffff', verbose=True):
#construct inital guess vector for fit
f =fluxes
x = columns
y = rows
nsrc = 1
guess = np.array([f, x, y])
if background:
if border == 0:
guess.append(0.0)
else:
for i in range((border + 1) * 2):
guess.append(0.0)
if focus:
guess = guess + [1.0, 1.0, 0.0]
kepid, channel, skygroup, module, output, campaign, data_rel, season, ra, \
dec, column, row, kepmag, xdim, ydim = \
pixel_info(or_pix_file)
npix = np.size(np.nonzero(maskimg)[0])
# print target data
if verbose:
print('')
print(' KepID: {}'.format(kepid))
print(' RA (J2000): {}'.format(ra))
print('Dec (J2000): {}'.format(dec))
print(' KepMag: {}'.format(kepmag))
print(' SkyGroup: {}'.format(skygroup))
print(' Season: {}'.format(str(season)))
print(' Campaign: {}'.format(campaign))
print(' Data Realse: {}'.format(data_rel))
print(' Channel: {}'.format(channel))
print(' Module: {}'.format(module))
print(' Output: {}'.format(output))
print('')
#construct pixel image
DATx = np.arange(column,column + xdim)
DATy = np.arange(row, row + ydim)
#determine PRF calibration file
if int(module) < 10:
prefix = 'kplr0'
else:
prefix = 'kplr'
prfglob = prfdir + '/' + prefix + str(module) + '.' + str(output) + '*' + '_prf.fits'
prffile = glob.glob(prfglob)[0]
# read PRF images
prfn = [0,0,0,0,0]
crpix1p = np.zeros(5, dtype='float32')
crpix2p = np.zeros(5, dtype='float32')
crval1p = np.zeros(5, dtype='float32')
crval2p = np.zeros(5, dtype='float32')
cdelt1p = np.zeros(5, dtype='float32')
cdelt2p = np.zeros(5, dtype='float32')
for i in range(5):
prfn[i], crpix1p[i], crpix2p[i], crval1p[i], crval2p[i], cdelt1p[i], cdelt2p[i] = \
prf_info(prffile, i+1)
prfn = np.array(prfn)
PRFx = np.arange(0.5, np.shape(prfn[0])[1] + 0.5)
PRFy = np.arange(0.5, np.shape(prfn[0])[0] + 0.5)
PRFx = (PRFx - np.size(PRFx) / 2) * cdelt1p[0]
PRFy = (PRFy - np.size(PRFy) / 2) * cdelt2p[0]
# interpolate the calibrated PRF shape to the target position
prf = np.zeros(np.shape(prfn[0]), dtype='float32')
prfWeight = np.zeros(5, dtype='float32')
for i in range(5):
prfWeight[i] = math.sqrt((column - crval1p[i])**2 + (row - crval2p[i])**2)
if prfWeight[i] == 0.0:
prfWeight[i] = 1.0e-6
prf = prf + prfn[i] / prfWeight[i]
prf = prf / np.nansum(prf) / cdelt1p[0] / cdelt2p[0]
# location of the data image centered on the PRF image (in PRF pixel units)
prfDimY = int(ydim / cdelt1p[0])
prfDimX = int(xdim / cdelt2p[0])
PRFy0 = int(np.round((np.shape(prf)[0] - prfDimY) / 2))
PRFx0 = int(np.round((np.shape(prf)[1] - prfDimX) / 2))
# interpolation function over the PRF
splineInterpolation = RectBivariateSpline(PRFx,PRFy,prf)
# fit PRF model to pixel data
start = time.time()
args = (DATx, DATy, DATimg, DATimg_eff, nsrc, splineInterpolation, float(x), float(y))
ans = fmin_powell(PRF, guess, args=args, xtol=xtol, ftol=ftol, disp=True)
print("Convergence time = {}s\n".format(time.time() - start))
# pad the PRF data if the PRF array is smaller than the data array
flux = []
OBJx = []
OBJy = []
PRFmod = np.zeros((prfDimY, prfDimX))
if PRFy0 < 0 or PRFx0 < 0.0:
PRFmod = np.zeros((prfDimY, prfDimX))
superPRF = np.zeros((prfDimY + 1, prfDimX + 1))
superPRF[abs(PRFy0):abs(PRFy0) + np.shape(prf)[0],\
abs(PRFx0):abs(PRFx0) + np.shape(prf)[1]] = prf
prf = superPRF * 1.0
PRFy0 = 0
PRFx0 = 0
# rotate the PRF model around its center
if focus:
angle = ans[-1]
prf = interpolation.rotate(prf, -angle, reshape=False,\
mode='nearest')
for i in range(nsrc):
flux.append(ans[i])
OBJx.append(ans[nsrc + i])
OBJy.append(ans[nsrc * 2 + i])
# calculate best-fit model
y = (OBJy[i] - np.mean(DATy)) / cdelt1p[0]
x = (OBJx[i] - np.mean(DATx)) / cdelt2p[0]
prfTmp = interpolation.shift(prf, [y, x], order=3, mode='constant')
prfTmp = prfTmp[PRFy0:PRFy0 + prfDimY, PRFx0:PRFx0 + prfDimX]
PRFmod = PRFmod + prfTmp * flux[i]
wx = 1.0
wy = 1.0
angle = 0
b = 0.0
# write out best fit parameters
if verbose:
txt = ("Flux = {0} e-/s X = {1} pix Y = {2} pix".format(flux[i], OBJx[i], OBJy[i]))
print(txt)
# measure flux fraction and contamination
PRFall = PRF2DET(flux, OBJx, OBJy, DATx, DATy, wx, wy, angle,\
splineInterpolation)
PRFone = PRF2DET([flux[0]], [OBJx[0]], [OBJy[0]], DATx, DATy,\
wx, wy, angle, splineInterpolation)
FluxInMaskAll = np.nansum(PRFall)
FluxInMaskOne = np.nansum(PRFone)
FluxInAperAll = 0.0
FluxInAperOne = 0.0
for i in range(1, ydim):
for j in range(1, xdim):
if (maskimg[i, j]==1):
FluxInAperAll += PRFall[i, j]
FluxInAperOne += PRFone[i, j]
FluxFraction = FluxInAperOne / flux[0]
try:
Contamination = (FluxInAperAll - FluxInAperOne) / FluxInAperAll
except:
Contamination = 0.0
print("\nTotal flux in mask = {0} e-/s".format(FluxInMaskAll))
print("\nTarget flux in mask = {0} e-/s".format(FluxInMaskOne))
print("\nTotal flux in aperture = {0} e-/s".format(FluxInAperAll))
print("\nTarget flux in aperture = {0} e-/s".format(FluxInAperOne))
print("\nTarget flux fraction in aperture = {0} %".format(FluxFraction * 100.0))
print("\nContamination fraction in aperture = {0} %".format(Contamination * 100.0))
# construct model PRF in detector coordinates
PRFfit = PRFall + 0.0
# calculate residual of DATA - FIT
PRFres = DATimg - PRFfit
FLUXres = np.nansum(PRFres) / npix
# calculate the sum squared difference between data and model
Pearson = abs(np.nansum(np.square(DATimg - PRFfit) / PRFfit))
Chi2 = np.nansum(np.square(DATimg - PRFfit) / np.square(DATimg_eff))
DegOfFreedom = npix - len(guess) - 1
print("\n Residual flux = {0} e-/s".format(FLUXres))
print("\n Pearson\'s chi^2 test = {0} for {1} dof".format(Pearson, DegOfFreedom))
print("\n Chi^2 test = {0} for {1} dof".format(Chi2, DegOfFreedom))
# image scale and intensity limits for plotting images
imgdat_pl, zminfl, zmaxfl = intScale2D(DATimg, imscale)
imgprf_pl, zminpr, zmaxpr = intScale2D(PRFmod, imscale)
imgfit_pl, zminfi, zmaxfi = intScale2D(PRFfit, imscale)
imgres_pl, zminre, zmaxre = intScale2D(PRFres, 'linear')
if imscale == 'linear':
zmaxpr *= 0.9
elif imscale == 'logarithmic':
zmaxpr = np.max(zmaxpr)
zminpr = zmaxpr / 2
plt.figure(figsize=(18, 14))
plt.clf()
ax1 = plt.subplot(221,projection=projection)
ax1.set_xlabel("CCD Column")
ax1.set_ylabel("CCD Row")
ax2 = plt.subplot(222)
ax3 = plt.subplot(223,projection=projection)
ax3.set_xlabel("CCD Column")
ax3.set_ylabel("CCD Row")
ax4 = plt.subplot(224,projection=projection)
ax4.set_xlabel("CCD Column")
ax4.set_ylabel("CCD Row")
ax1.imshow(imgdat_pl, aspect='auto',interpolation='nearest',\
vmin=zminfl,vmax=zmaxfl, cmap=cmap)
ax1.text(0.05, 0.9,'observation',horizontalalignment='left',verticalalignment='center',\
fontsize=26,fontweight=500)
ax2.imshow(imgprf_pl, aspect='auto',interpolation='nearest',\
vmin=zminfl,vmax=zmaxfl, cmap=cmap)
ax2.text(47, 60,'model',horizontalalignment='left',verticalalignment='center',\
fontsize=26,fontweight=500)
ax3.imshow(imgfit_pl, aspect='auto',interpolation='nearest',\
vmin=zminfl,vmax=zmaxfl, cmap=cmap)
ax3.scatter(projection.all_world2pix(OBJx,OBJy,1)[0]-1, 15-projection.all_world2pix(OBJx,OBJy,1)[1],marker='X',color='white',s=100)
ax3.text(0.05, 0.9,'fit',horizontalalignment='left',verticalalignment='center',\
fontsize=26,fontweight=500)
ax4.imshow(imgres_pl, aspect='auto',interpolation='nearest',\
vmin=zminfl,vmax=zmaxfl, cmap=cmap)
ax4.text(0.05, 0.9,'residual',horizontalalignment='left',verticalalignment='center',\
fontsize=26,fontweight=500)
plt.savefig(outfile)
return flux, OBJx, OBJy
def cent_RMS(fixel_file, orient, time_range):
t = fits.open(fixel_file)
mask = np.where(t[1].data['SAP_QUALITY']==0)
if orient=='column':
cent = t[1].data['PSF_CENTR1'][mask]
cent_err = t[1].data['PSF_CENTR1_ERR'][mask]
elif orient =='row':
cent = t[1].data['PSF_CENTR2'][mask]
cent_err = t[1].data['PSF_CENTR2_ERR'][mask]
cent_tmp = []
cent_weight = []
for i in range(len(time_range)):
idx_i = np.where((t[1].data['TIME'][mask]>time_range[i][0])&(t[1].data['TIME'][mask]<time_range[i][1]))
cent_tmp = np.append(cent_tmp,cent[idx_i])
cent_weight = np.append(cent_weight,cent_err[idx_i])
return cent_tmp, cent_weight
filedir = "/home/yangyang/Documents/Code/astronomy/centroidtest/test/"
i_o = model.In_out_transit()
ini = i_o.from_ini(os.path.join(filedir,'model.ini'))
LC = fits.open(os.path.join(filedir,"hlsp_everest_k2_llc_201920032-c01_kepler_v2.0_lc.fits"))
pf = fits.open("ktwo201920032-c01_lpd-targ.fits")
mask = np.where(LC[1].data['QUALITY'] == 0)
t = LC[1].data['TIME'][mask]
timemodel = np.linspace(t.min(),t.max(),t.shape[0])
LC_Model = i_o.model(ini, timemodel)
plt.scatter(timemodel,LC_Model,marker='.')
it = i_o.in_transit_range(timemodel,LC_Model,ini)
ot = i_o.out_transit_range(timemodel,it,ini)
img_i = diffimg.add_img("hlsp_everest_k2_llc_201920032-c01_kepler_v2.0_lc.fits",it)
img_o = diffimg.add_img("hlsp_everest_k2_llc_201920032-c01_kepler_v2.0_lc.fits",ot)
wcs = WCS(LC[3].header, key='P')
diff_img = diffimg.img_in_apr(LC[3].data,img_o-img_i)
diff_img_i = diffimg.img_in_apr(LC[3].data,img_i)
diff_img_o = diffimg.img_in_apr(LC[3].data,img_o)
unc = diffimg.unc_img(pf[1].data['FLUX_ERR'][mask],LC[3].data, pf[1].data['TIME'][mask],np.vstack((it,ot)))
unc_i = diffimg.unc_img(pf[1].data['FLUX_ERR'][mask],LC[3].data, pf[1].data['TIME'][mask],it)
unc_o = diffimg.unc_img(pf[1].data['FLUX_ERR'][mask],LC[3].data, pf[1].data['TIME'][mask],ot)
#ax = plt.subplot(111, projection=wcs)
#ax.imshow(diff_img)
#ax.scatter(9.870969162523352-1,16-7.849627832013198-1,marker='x',color='white')
#centroid calculation from out-transit & diff imgage
wcs2 = WCS(LC[3].header)
column = 329.87096916252335
row = 571.8496278320132
out_of_transit_cent = prf(diff_img_o, unc_o, "ktwo201920032-c01_lpd-targ.fits","kplr2011265_prf", LC[3].data, column, row, np.nansum(img_o), projection=wcs, outfile='out-of-transit.png')
diff_cent = prf(diff_img, unc, "ktwo201920032-c01_lpd-targ.fits","kplr2011265_prf", LC[3].data, column, row, np.nansum(diff_img), projection=wcs, outfile='diff.png')
out_of_transit_x = out_of_transit_cent[1][0]
out_of_transit_y = out_of_transit_cent[2][0]
x2, y2 = wcs.all_world2pix(out_of_transit_x,out_of_transit_y,1)
x2_, y2_ = wcs2.all_pix2world(x2,y2,1)
diff_x = diff_cent[1][0]
diff_y = diff_cent[2][0]
x1, y1 = wcs.all_world2pix(diff_x,diff_y,1)
x1_, y1_ = wcs2.all_pix2world(x1,y1,1)
x0, y0 = wcs.all_world2pix(column,row,1)
x0_,y0_ = wcs2.all_pix2world(x0,y0,1)
#centroid uncertainties estimation(from centroid time series)------------------
c = cent_RMS('ktwo201920032-c01_llc.fits', 'column', ot)
w = 1.0/c[1]**2/np.sum(1.0/c[1]**2)
c2 = cent_RMS('ktwo201920032-c01_llc.fits', 'row', ot)
w2 = 1.0/c2[1]**2/np.sum(1.0/c2[1]**2)
w_total = np.transpose([w,w2])
center = wcs2.all_pix2world(wcs.all_world2pix(np.transpose([c[0],c2[0]]),1),1)-np.tile([x1_,y1_],(len(w),1))
m=np.sum(w_total*center,axis=0)
std=np.sum(w_total*(center - np.tile(m,(len(center),1)))**2,axis=0)**0.5*3600
#erro propagation (standard deviation)
total_ang = (((x1_ - x2_)*np.cos(y2_*np.pi/180.0)*3600)**2+((y1_ - y2_)*3600)**2)**0.5
std_t = np.sqrt((((x1_ - x2_)*np.cos(y2_*np.pi/180.0)**2))**2*std[0]**2+((x1_ - x2_)**2*0.5*np.sin(2*y2_*np.pi/180.0)*np.pi/180+(y1_-y2_))**2*std[1]**2)*3600/total_ang
sigma3 = 3*std_t
#plot--------------------------------------------------------------------------
centroid_off_x = (x1_ - x2_)*np.cos(y2_*np.pi/180.0)*3600
centroid_off_y = (y1_ - y2_)*3600
fig1 = plt.figure(figsize=(14,10))
ax1 = plt.subplot()
circle1=plt.Circle((centroid_off_x,centroid_off_y),sigma3,color='blue',fill=False)
plt.gcf().gca().add_artist(circle1)
X, Y = np.mgrid[-5:5:.01, -5:5:.01]
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X; pos[:, :, 1] = Y
rv = multivariate_normal([centroid_off_x,centroid_off_y], [[std[0], 0], [0, std[1]]])
ax1.contourf(X, Y, rv.pdf(pos),offset=0.15, level=[0.069182369442786328,0.069182369442786329])
ax1.axes.errorbar(centroid_off_x, centroid_off_y, xerr=std[0],yerr=std[1],marker='+',color='green')
ax1.scatter(centroid_off_x, centroid_off_y,marker='X',color='white',s=150)
ax1.scatter(0,0, marker='*', color = 'red',s=150)
ax1.set_xlim(xmin=-5,xmax=5)
ax1.set_ylim(ymin=-5,ymax=5)
ax1.set_title('Offset Relative to Out of Transit Centroid',fontweight=1000)
ax1.set_xlabel('RA Offset (arcsec)', fontweight=1000)
ax1.set_ylabel('Dec Offset (arcsec)', fontweight=1000)
fig1.savefig("Centroid_Offset1.eps",format='eps')
fig2 = plt.figure(figsize=(14,10))
plt.scatter((x1_ - x0_)*np.cos(y0_*np.pi/180.0)*3600, (y1_ - y0_)*3600, marker='X', color = 'purple',s=150)
plt.scatter(0,0, marker='*', color = 'red',s=150)
plt.set_xlim(xmin=-1,xmax=1)
plt.set_ylim(ymin=-1,ymax=1)
plt.set_title('Offset Relative to K2 Position',fontweight=1000)
plt.set_xlabel('RA Offset (arcsec)', fontweight=1000)
plt.set_ylabel('Dec Offset (arcsec)', fontweight=1000)
fig2.savefig("Centroid_Offset2.eps",format='eps')