def make_circ_lc(image_list, maskmap, times, start_center, ap_radii,
output_filename, fw_box=9, ap_type="circular",
ellipse_kwargs=None):
"""
Make a lightcurve by computing aperture photometry for
all images in a target pixel file.
inputs:
-------
image_list: array-like
fluxes at every epoch from TPF
maskmap: array-like
times: array-like
start_center: array-like
initial pixel coordinates for star
ap_radii: array-like
aperture radii (for circular apertures), or
multiplicative factors (for elliptical apertures)
output_filename: string, ending in .csv
fw_box: odd integer, default=9
box size for flux-weighted centroid
ap_type: string
"circular" (default) or "elliptical"
ellipse_kwargs: dict, required for ap_type=="elliptical"
a, b, and theta for the elliptical aperture
(might be better to fit for theta every time?)
"""
# Open the output file and write the header line
f = open(output_filename,"w")
f.write("i,t,x,y")
for r in ap_radii:
f.write(",flux_{0:.1f},bkgd_{0:.1f}".format(r))
# Do aperture photometry at every step, and save the results
for i, time in enumerate(times):
f.write("\n{0},{1:.6f}".format(i,time))
# Find the actual centroid in this image, using start_center as a guess
# (I should make a flag if the centroid has moved more than a pixel or two)
#logging.debug(time)
coords = centroid.flux_weighted_centroid(image_list[i], fw_box,
init=start_center,
to_plot=False)
# Write out the centroid pixel coordinates
f.write(",{0:.6f},{1:.6f}".format(coords[0],coords[1]))
if (np.any(np.isfinite(coords[:2])==False) or
(coords[0]<0) or (coords[1]<0) or
(coords[0]>100) or (coords[1]>100)
):
logging.debug(coords[:2])
f.write(",NaN,NaN"*len(ap_radii))
else:
# Now run the aperture photometry on the image
if ap_type=="circular":
ap_fluxes, bkgd_fluxes = circ_flux(image_list[i], maskmap,
coords[::-1], ap_radii)
elif ap_type=="elliptical":
ap_fluxes, bkgd_fluxes = ellip_flux(image_list[i], maskmap,
coords, ap_radii,
**ellipse_kwargs)
# Write out the fluxes and background level for each aperture
for i, r in enumerate(ap_radii):
f.write(",{0:.6f},{1:.6f}".format(ap_fluxes[i],
bkgd_fluxes[i]))
f.close()
def plot_one(filename, ap=None, fw_box=None):
outfilename = filename.split("/")[-1][:-14]
logging.info(outfilename)
table, times, pixels, maskmap, maskheader, kpmag = tpf_io.get_data(filename)
init = centroid.init_pos(maskheader)
logging.info("init %f %f", init[0], init[1])
if fw_box is None:
min_ax = np.argmin(np.shape(maskmap))
min_box = np.shape(maskmap)[min_ax]
if min_box>=9:
min_box = 9
logging.debug("min_box set to 9")
elif min_ax==0:
for row in maskmap:
row_len = len(np.where(row>0)[0])
if row_len < min_box:
min_box = row_len
logging.debug("new min_box %d", min_box)
else:
for i in range(np.shape(maskmap)[1]):
col = maskmap[:, i]
col_len = len(np.where(col>0)[0])
if col_len < min_box:
min_box = row_len
logging.debug("new min_box %d", min_box)
fw_box = (min_box / 2) * 2 + 1
logging.info("fw box %d",fw_box)
coadd = np.sum(pixels,axis=0)
coords = centroid.flux_weighted_centroid(coadd, fw_box, init=init)
mean, median, std = sigma_clipped_stats(coadd, mask=(maskmap==0),
sigma=3.0, iters=3)
coadd_bkgd = median
if np.sqrt((coords[0] - init[0])**2 + (coords[1] - init[1])**2)>1:
logging.warning("Centroid (%f %f) far from init (%f %f)",
coords[0], coords[1], init[0], init[1])
else:
logging.debug("coords %f %f", coords[0], coords[1])
dkwargs = {"fwhm":2.5, "threshold":coadd_bkgd,
"sharplo":0.01,"sharphi":5.0}
sources, n_sources = centroid.daofind_centroid(coadd, None, dkwargs)
if n_sources==0:
logging.info("bkgd: %f max: %f", coadd_bkgd, max(coadd.flatten()))
logging.debug("sources")
logging.debug(sources)
ap_min, ap_max, ap_step = 2, 7, 0.5
radii = np.arange(ap_min, ap_max, ap_step)
epic = outfilename.split("-")[0][4:]
logging.info(epic)
# plot.lcs("lcs/{}.csv".format(outfilename), epic=epic)
plot.plot_four(epic, filename, coadd, maskmap, maskheader, init, coords,
sources, ap=ap, campaign=4)
plt.savefig("plot_outputs/ktwo{}-c04_fourby.png".format(epic))
def run_one(filename, output_f=None, extract_companions=False, fw_box=None):
"""
Extract a light curve for one star.
Inputs
------
filename: string
filename for target pixel file
output_f: string (optional)
if provided, statistics from the extraction will be printed to this file
extract_companions: boolean (optional; default=False)
if true, light curves and plots will also be generated for any other
DAOfind sources in the coadded image
fw_box: odd integer (optional)
size of the box within which to calculate the flux-weighted centroid.
Must be odd. Defaults to the shortest dimension of the pixel stamp,
or 9 if larger than 9.
"""
# Clip part of the filename for use in saving outputs
outfilename = filename.split("/")[-1][:-14]
logging.info(outfilename)
# Retrieve the data
table, times, pixels, maskmap, maskheader, kpmag = tpf_io.get_data(filename)
# Use the RA/Dec from the header as the initial position for calculation
init = centroid.init_pos(maskheader)
logging.info("init %f %f", init[0], init[1])
# If not provided, calculate the maximum possible centroid box size.
# Maximum possible box is 9, unless set larger by the user with fw_box
if fw_box is None:
min_ax = np.argmin(np.shape(maskmap))
min_box = np.shape(maskmap)[min_ax]
if min_box>=9:
min_box = 9
logging.debug("min_box set to 9")
elif min_ax==0:
for row in maskmap:
row_len = len(np.where(row>0)[0])
if row_len < min_box:
min_box = row_len
logging.debug("new min_box %d", min_box)
else:
for i in range(np.shape(maskmap)[1]):
col = maskmap[:, i]
col_len = len(np.where(col>0)[0])
if col_len < min_box:
min_box = row_len
logging.debug("new min_box %d", min_box)
fw_box = (min_box / 2) * 2 + 1
logging.info("fw box %d",fw_box)
# Co-add all the sub-images and calculate the flux-weighted centroid
coadd = np.sum(pixels,axis=0)
coords = centroid.flux_weighted_centroid(coadd, fw_box, init=init)
# Background of the co-added stampl is just the sigma-clipped median
mean, median, std = sigma_clipped_stats(coadd, mask=(maskmap==0),
sigma=3.0, iters=3)
coadd_bkgd = median
# Warn the user if the calculated initial coordinates are too far
# from the initial coordinates (indicting that there's a brighter nearby
# star pulling the centroid off of the target)
if np.sqrt((coords[0] - init[0])**2 + (coords[1] - init[1])**2)>1:
logging.warning("Centroid (%f %f) far from init (%f %f)",
coords[0], coords[1], init[0], init[1])
else:
logging.debug("coords %f %f", coords[0], coords[1])
# Set keyword arguments for DAOFIND - trying to find anything other
# real sources in the image
dkwargs = {"fwhm":2.5, "threshold":coadd_bkgd,
"sharplo":0.01,"sharphi":5.0}
sources, n_sources = centroid.daofind_centroid(coadd, None, dkwargs)
if n_sources==0:
logging.info("bkgd: %f max: %f", coadd_bkgd, max(coadd.flatten()))
logging.debug("sources")
logging.debug(sources)
# Set the minimum and maximum aperture sizes for the extraction
# and the step in aperture sizes
ap_min, ap_max, ap_step = 2, 7, 1
radii = np.arange(ap_min, ap_max, ap_step)
# Always use circular apertures, I think ap_type is now defunct actually...
ap_type = "circ"
phot.make_circ_lc(pixels, maskmap, times, init, radii,
"lcs/{}.csv".format(outfilename), fw_box)
# get the EPIC ID
epic = outfilename.split("-")[0][4:]
logging.info(epic)
# If an output filename is provided, save the output
if output_f is not None:
output_f.write("\n{},{}".format(outfilename,epic))
output_f.write(",{0:.6f},{1:.6f}".format(maskheader["RA_OBJ"],
maskheader["DEC_OBJ"]))
print type(init[0]), type(init[1])
print init[0].dtype, init[1].dtype
print init[0], init[1]
output_f.write(",{0:.6f},{1:.6f}".format(np.float64(init[0]),
np.float64(init[1])))
output_f.write(",{0:.2f},{1}".format(kpmag, n_sources))
output_f.write(",{0:.2f},{1:.6f}".format(dkwargs["fwhm"],
dkwargs["threshold"]))
output_f.write(",{0:.2f},{1:.2f}".format(dkwargs["sharplo"],
dkwargs["sharphi"]))
output_f.write(",{0:.2f},{1}".format(fw_box,ap_type))
output_f.write(",{0:.2f},{1:.2f},{2:.2f}".format(ap_min, ap_max,
ap_step))
