)
args = parser.parse_args()
bgcounts = np.recfromtxt(args.bgfile, comments="#", delimiter="\t", dtype=int, names=True)
counts = np.recfromtxt(args.file, comments="#", delimiter="\t", names=True, dtype=int)
dp = DataPrep(counts, bgcounts, crop=args.crop, fill=args.fill, normalize=False, down_sampling=args.dsfac)
dp.gap_stats()
if args.lomb_scargle:
# calculate the LombScargle Periodigram in the
# frequency range of interessest
freq_ls = np.linspace(5.0e-7, 5.0e-4, 500)
ls = LombScargle(np.float_(dp.position), np.float_(dp.counts), freq_ls)
f, pgram = ls()
ax = plots.samples(f, pgram, window_title="Lomb-Scargle Periodigram")
ax.semilogy()
if args.welch:
plots.samples(dp.position, dp.counts, window_title="Raw data")
# xlim = (0.0, 5e-5)
fs = 1.0 / np.diff(dp.position).min()
plots.psd(
dp.counts,
xlim=args.frange,
Fs=fs,
NFFT=args.nfft,
window=mlab.window_none,
window_title="Welch Periodigram",
)
from matplotlib import mlab
from dataprep import LombScargle
import plots
f1 = 5.
f2 = 10.
pi2 = 2*np.pi
srate = 100
x = np.arange(0, 20, 1./srate)
x0 = np.random.permutation(x)[:10*srate]
x0 = np.sort(x0)
yfunc = lambda t, w1, w2: np.sin(w1*t) + np.sin(w2*t)
y = yfunc(x, f1*pi2, f2*pi2)
y0 = yfunc(x0, f1, f2)
fmin = 1./x.size # = delta_f
fmax = srate/2.0
f = np.arange(1, 50, 0.5, dtype=float)
lb = LombScargle(x0, y0, f)
f, pgram = lb()
plots.samples(x0, y0, window_title="Data Series")
plots.samples(f, pgram, window_title="Lomb-Scargle Periodigram")
plots.psd(y, Fs=srate, NFFT=y.size,
window_title="Welch Periodigram",
window=mlab.window_none)
plots.show()