def main():
msg = textwrap.dedent(
"""
Computes a spectrogram using the lomb-scargle algorithm provided by
the gatspy module. The input time series need not have evenly spaced
time-stamps. The FFT-based algorithm has complexity O[N*log(N)].
-----------------------------------------------------------------------
Examples:
* Plot the spectrum of a simple sine wave
p.linspace 0 10 100 \\
| p.df 'df["value"] = 7 * np.sin(2*np.pi*df.time / 1.5)'\\
--names time\\
| p.lomb_scargle -t time -y value --interp_exp 3\\
| p.plot -x period -y amp --xlim 0 3
* Show the annual and 59-day peaks in the sealevel spectrum
p.example_data -d sealevel\\
| p.df 'df["day"] = 365.25 * df.year'\\
'df["day"] = df.day - df.day.iloc[0]'\\
| p.lomb_scargle -t day -y sealevel_mm --interp_exp 3\\
| p.df 'df[df.period < 720]'\\
| p.plot -x period -y amp --xlim 1 400\\
--title 'Sea-surface height spectrum'\\
--xlabel 'period (days)'
-----------------------------------------------------------------------
"""
)
# read command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=msg)
arg_lib.add_args(parser, 'io_in', 'io_out')
parser.add_argument('-t', '--time_col', help='Time Column',
nargs=1, required=True, type=str)
parser.add_argument('-y', '--observation_col', help='Observation column',
nargs=1, dest='val_col', required=True, type=str)
parser.add_argument('--interp_exp', help='Interpolate by this power of 2',
nargs=1, type=int, default=[1])
parser.add_argument(
'--freq_order', action='store_true', dest='freq_order', default=False,
help='Order output by freqency instead of period')
# parse arguments
args = parser.parse_args()
# get the input dataframe
df = io_lib.df_from_input(args)
df = lomb_scargle_lib.lomb_scargle(
df, args.time_col[0], args.val_col[0], args.interp_exp[0],
args.freq_order)
# write dataframe to output
io_lib.df_to_output(args, df)
def test_freq_order(self):
t = np.linspace(0, 10, 256)
y = 7 * np.sin(2 * np.pi * t)
df_in = pd.DataFrame({'t': t, 'y': y})
df = lomb_scargle(df_in, 't', 'y', freq_order=True)
max_rec = df[df.amp == df.amp.max()].iloc[0]
self.assertTrue(all([x > 0 for x in df.freq.diff().dropna()]))
self.assertAlmostEqual(max_rec['amp'], 7, places=0)
self.assertAlmostEqual(max_rec['power'], 49, places=0)
self.assertAlmostEqual(max_rec['period'], 1, places=0)
self.assertAlmostEqual(max_rec['freq'], 1, places=0)
self.assertEqual(len(df), 256)
def refine_frequency(self, time, amplitude, simple=False, verbose=False):
# use lomb-scargle to get initial guess
dfd = pd.DataFrame(dict(t=time, amp=amplitude))
dfs = lomb_scargle(dfd, 't', 'amp', interp_exponent=1)
freq = dfs[dfs.power == dfs.power.max()].freq
# set up to do do a mininzer fit to best freq
p = ParamState(
't',
'y_true',
a=1,
b=1,
f=self.f0,
)
p.given(
t=time,
y_true=amplitude
)
def model(p):
return (
p.a * np.sin(2 * np.pi * p.f * p.t) +
p.b * np.cos(2 * np.pi * p.f * p.t)
)
def cost(args, p):
p.ingest(args)
err = model(p) - p.y_true
energy = np.sum(err ** 2)
return energy
x0 = p.array
xf = fmin_powell(cost, x0, args=(p,), disp=verbose)
p.ingest(xf)
if (p.f - self.f0) > 3 ** 2:
raise ValueError(f'Guess freq: {self.f0}, Fit Freq: {p.f} too far apart')
self.f0 = p.f
return self
def main():
msg = textwrap.dedent("""
Computes a spectrogram using the lomb-scargle algorithm provided by
the gatspy module. The input time series need not have evenly spaced
time-stamps. The FFT-based algorithm has complexity O[N*log(N)].
-----------------------------------------------------------------------
Examples:
* Plot the spectrum of a simple sine wave
p.linspace 0 10 100 \\
| p.df 'df["value"] = 7 * np.sin(2*np.pi*df.time / 1.5)'\\
--names time\\
| p.lomb_scargle -t time -y value --interp_exp 3\\
| p.plot -x period -y amp --xlim 0 3
* Show the annual and 59-day peaks in the sealevel spectrum
p.example_data -d sealevel\\
| p.df 'df["day"] = 365.25 * df.year'\\
'df["day"] = df.day - df.day.iloc[0]'\\
| p.lomb_scargle -t day -y sealevel_mm --interp_exp 3\\
| p.df 'df[df.period < 720]'\\
| p.plot -x period -y amp --xlim 1 400\\
--title 'Sea-surface height spectrum'\\
--xlabel 'period (days)'
-----------------------------------------------------------------------
""")
# read command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=msg)
arg_lib.add_args(parser, 'io_in', 'io_out')
parser.add_argument('-t',
'--time_col',
help='Time Column',
nargs=1,
required=True,
type=str)
parser.add_argument('-y',
'--observation_col',
help='Observation column',
nargs=1,
dest='val_col',
required=True,
type=str)
parser.add_argument('--interp_exp',
help='Interpolate by this power of 2',
nargs=1,
type=int,
default=[1])
parser.add_argument('--freq_order',
action='store_true',
dest='freq_order',
default=False,
help='Order output by freqency instead of period')
# parse arguments
args = parser.parse_args()
# get the input dataframe
df = io_lib.df_from_input(args)
df = lomb_scargle_lib.lomb_scargle(df, args.time_col[0], args.val_col[0],
args.interp_exp[0], args.freq_order)
# write dataframe to output
io_lib.df_to_output(args, df)