def test_masks():
"""All the late targets should be covered."""
c9fov = getKeplerFov(9)
for idx, target in LATE_TARGETS.iterrows():
ch, col, row = c9fov.getChannelColRow(target.ra, target.dec)
assert c9.pixelInMicrolensRegion(int(ch), int(col), int(row))
assert c9.maskInMicrolensRegion(int(ch), int(col), int(row), padding=4.99)
def maketpf(filelist, ra, dec, tpfsize):
files = np.loadtxt(filelist, dtype=str)
# Make size odd so that target is centered:
if (tpfsize % 2 == 0): tpfsize += 1
# Get detector coordinates to cut out around
midnum = int(len(files) / 2) - 1
instr = fits.open(files[midnum], mode='readonly', memmap=True)
campaign = instr[0].header['CAMPAIGN']
instr.close()
outfile = 'ktwo-' + str(np.round(ra, 4)) + '-' + str(np.round(
dec, 4)) + '-c' + str(campaign) + '_lpd-targ.fits'
fovobj = fields.getKeplerFov(campaign)
ch, detcol, detrow = fovobj.getChannelColRow(ra, dec)
detcol = np.round(detcol)
detrow = np.round(detrow)
skyposition = SkyCoord(ra, dec, unit=('deg', 'deg'), frame='icrs')
wcs = WCS(files[midnum])
pixelpos = skycoord_to_pixel(skyposition, wcs=wcs)
column = int(np.round(pixelpos[0]))
row = int(np.round(pixelpos[1]))
# For now, use a template to write the new TPF:
template = '/Volumes/Work/Field_5/M67/tpf_template.fits'
tpf = fits.open(template, mode='readonly', memmap=True)
timearray = np.empty(len(files), dtype=float)
timecorrarray = np.empty(len(files), dtype=float)
cadencearray = np.empty(len(files), dtype=float)
qualityarray = np.empty(len(files), dtype=float)
pixels = np.empty([len(files), tpfsize, tpfsize])
for i in np.arange(len(files)):
instr = fits.open(files[i], mode='readonly', memmap=True)
allpixels = instr[0].data[:]
btlx = instr[0].header['CRVAL1P']
btly = instr[0].header['CRVAL2P']
dimx = instr[0].header['NAXIS1']
dimy = instr[0].header['NAXIS2']
campaign = instr[0].header['CAMPAIGN']
timearray[i] = instr[0].header['MIDTIME'] - 2454833.
timecorrarray[i] = instr[0].header['TIMECORR']
cadencearray[i] = instr[0].header['CADENCEN']
qualityarray[i] = instr[0].header['QUALITY']
pixels[i, :] = allpixels[row - int((tpfsize - 1) / 2):row +
int((tpfsize - 1) / 2) + 1,
column - int((tpfsize - 1) / 2):column +
int((tpfsize - 1) / 2) + 1]
instr.close()
pixels = np.reshape(pixels, (len(files), tpfsize * tpfsize))
cards0 = tpf[0].header.cards
cards1 = tpf[1].header.cards
cards2 = tpf[2].header.cards
# construct output primary extension
hdu0 = fits.PrimaryHDU()
for i in range(len(cards0)):
try:
if cards0[i].keyword not in hdu0.header.keys():
hdu0.header[cards0[i].keyword] = (cards0[i].value,
cards0[i].comment)
else:
hdu0.header.cards[
cards0[i].keyword].comment = cards0[i].comment
except:
pass
# Change a few header keywords:
hdu0.header['OBJECT'] = ''
hdu0.header['KEPLERID'] = ''
hdu0.header['CHANNEL'] = ''
hdu0.header['CHANNEL'] = int(ch)
hdu0.header['OUTPUT'] = ''
hdu0.header['RA_OBJ'] = ra
hdu0.header['DEC_OBJ'] = dec
outstr = fits.HDUList(hdu0)
# construct output light curve extension
coldim = '(' + str(tpfsize) + ',' + str(tpfsize) + ')'
eformat = str(tpfsize * tpfsize) + 'E'
jformat = str(tpfsize * tpfsize) + 'J'
kformat = str(tpfsize * tpfsize) + 'K'
col1 = fits.Column(name='TIME',
format='D',
unit='BJD - 2454833',
array=timearray)
col2 = fits.Column(name='TIMECORR',
format='E',
unit='d',
array=timecorrarray)
col3 = fits.Column(name='CADENCENO', format='J', array=cadencearray)
col5 = fits.Column(name='FLUX',
format=eformat,
unit='e-/s',
dim=coldim,
array=pixels)
col10 = fits.Column(name='QUALITY', format='J', array=qualityarray)
cols = fits.ColDefs([col1, col2, col3, col5, col10])
hdu1 = fits.BinTableHDU.from_columns(cols)
for i in range(len(cards1)):
try:
if cards1[i].keyword not in hdu1.header.keys():
hdu1.header[cards1[i].keyword] = (cards1[i].value,
cards1[i].comment)
else:
hdu1.header.cards[
cards1[i].keyword].comment = cards1[i].comment
except:
pass
hdu1.header['1CRV4P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRV4P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX4'] = (tpfsize + 1) / 2
hdu1.header['2CRPX4'] = (tpfsize + 1) / 2
hdu1.header['1CRV5P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRV5P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX5'] = (tpfsize + 1) / 2
hdu1.header['2CRPX5'] = (tpfsize + 1) / 2
hdu1.header['1CRV6P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRV6P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX6'] = (tpfsize + 1) / 2
hdu1.header['2CRPX6'] = (tpfsize + 1) / 2
hdu1.header['1CRV7P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRV7P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX7'] = (tpfsize + 1) / 2
hdu1.header['2CRPX7'] = (tpfsize + 1) / 2
hdu1.header['1CRV8P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRP8P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX8'] = (tpfsize + 1) / 2
hdu1.header['2CRPX8'] = (tpfsize + 1) / 2
hdu1.header['1CRV9P'] = detrow - (tpfsize - 1) / 2
hdu1.header['2CRV9P'] = detcol - (tpfsize - 1) / 2
hdu1.header['1CRPX9'] = (tpfsize + 1) / 2
hdu1.header['2CRPX9'] = (tpfsize + 1) / 2
hdu1.header['NAXIS2'] = len(files)
outstr.append(hdu1)
# Write output file
outstr.writeto(outfile, checksum=True)
The format is: ID|SkyGroup|Flags|Row|Col|Delta;Delta;Delta
SkyGroups are as they were in Kepler Season 0, i.e. rotated by 180 degrees
"""
import pandas as pd
from K2fov import fields
# K2 skygroups are as they were in Kepler Season 0
# hence rotate the focal plane by 180 degrees
CHANNEL_2_SKYGROUP = {29: 53,
30: 54,
48: 40,
49: 33,
51: 35}
fov = fields.getKeplerFov(9)
delta_10_by_10_mask = ";".join(["{},{}".format(x, y)
for x in range(-4, 6, 1)
for y in range(-4, 6, 1)])
df = pd.read_csv("isolated-stars.csv")
with open("custom-isolated-stars.txt", "w") as out:
out.write("Category: Isolated_Stars\n")
#out.write("id|channel|refcol|refrow|delta\n")
for idx, row in df.iterrows():
ch, col, row = fov.getChannelColRow(row["ra"], row["dec"])
skygroup = CHANNEL_2_SKYGROUP[int(ch)]
out.write("NEW|{:d}|GO9002_LC,TAD_NO_HALO,TAD_NO_UNDERSHOOT_COLUMN,NO_SOC_PHOTOMETRY|{}|{}|{}\n".format(skygroup, int(row), int(col), delta_10_by_10_mask))
df = pd.read_csv("dark-clouds.csv")
def write(self, output_fn):
"""Write the custom apertures to the format required by Kepler."""
with open(output_fn, "w") as out:
out.write('Category: Ongoing_Microlens_Events_C9a\n')
for mask in self.masks:
out.write('NEW|{skygroup:d}|{flags}|{row}|{col}|{mask}\n'.format(**mask))
def write_json(self, output_fn):
"""Dump the mask definitions into a json file."""
json.dump({'masks': self.masks}, open(output_fn, 'w'), indent=True)
if __name__ == '__main__':
apfile = CustomApertureFile()
fov = fields.getKeplerFov(9)
targetlist = pd.read_fwf('input/late_targets_K2C9a_v3.dat',
names=('ra', 'dec', 'channel', 'col', 'row',
'peakmag', 'name', 'name2'))
for idx, target in targetlist.iterrows():
channel, col, row = fov.getChannelColRow(target.ra, target.dec)
channel = int(channel)
# Sanity check: is our ch/col/row derivation consistent with Radek's target list?
assert(channel == target.channel)
assert(np.abs(col - target.col) < 0.1)
assert(np.abs(row - target.row) < 0.1)
apfile.add_target(channel, col, row, name=target['name'])
apfile.write('output/custom-late-target-masks-c9a.txt')
apfile.write_json('output/k2-c9a-late-targets.json')
# Print a little summary
