import matplotlib.pyplot as plt
plt.style.use("ggplot")
from mtspec import mtspec, sine_psd
from mtspec.util import _load_mtdata
data = _load_mtdata('PASC.dat.gz')
plt.subplot(311)
plt.plot(data, color='black')
plt.xlim(0, len(data))
spec, freq = mtspec(data, 1.0, 1.5, number_of_tapers=1)
plt.subplot(323)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="Single Taper",
transform=plt.gca().transAxes, ha="center")
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5)
plt.subplot(324)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="5 Tapers Multitaper",
transform=plt.gca().transAxes, ha="center")
spec, freq = sine_psd(data, 1.0)
plt.subplot(325)
import matplotlib.pyplot as plt
plt.style.use("ggplot")
import numpy as np
from mtspec import mtspec
from mtspec.util import _load_mtdata
data = _load_mtdata("v22_174_series.dat.gz")
spec, freq, jackknife, fstatistics, _ = mtspec(data=data,
delta=4930.,
time_bandwidth=3.5,
number_of_tapers=5,
nfft=312,
statistics=True,
rshape=0,
fcrit=0.9)
# Convert to million years.
freq *= 1E6
plt.subplot(211)
plt.plot(freq, fstatistics, color="black")
plt.xlim(freq[0], freq[-1])
plt.xlabel("Frequency [c Ma$^{-1}]$")
plt.ylabel("F-statistics for periodic lines")
# Plot the confidence intervals.
for p in [90, 95, 99]:
y = np.percentile(fstatistics, p)
import matplotlib.pyplot as plt
import numpy as np
import scipy.fftpack
from mtspec import mt_deconvolve, mtspec
from mtspec.util import _load_mtdata
plt.style.use("ggplot")
# Load and demean data.
pasc = _load_mtdata('PASC.dat.gz')
ado = _load_mtdata('ADO.dat.gz')
pasc -= pasc.mean()
ado -= ado.mean()
r = mt_deconvolve(pasc, ado, delta=1.0,
time_bandwidth=4.0,
number_of_tapers=7,
nfft=len(pasc), demean=True,
weights="adaptive")
deconvolved = r["deconvolved"]
Pdeconv = deconvolved[-500:][::-1]
Pdeconv /= Pdeconv.max()
nfft = 2 * len(pasc)
pasc = scipy.fftpack.fft(pasc, n=nfft)
ado = scipy.fftpack.fft(ado, n=nfft)
cc = pasc * ado.conj()
import matplotlib.pyplot as plt
plt.style.use("ggplot")
import numpy as np
from mtspec import mtspec
from mtspec.util import _load_mtdata
data = _load_mtdata('v22_174_series.dat.gz')
# Calculate the spectral estimation.
spec, freq, jackknife, _, _ = mtspec(
data=data, delta=4930.0, time_bandwidth=3.5,
number_of_tapers=5, nfft=312, statistics=True)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
# Plot in thousands of years.
ax1.plot(np.arange(len(data)) * 4.930, data, color='black')
ax1.set_xlim(0, 800)
ax1.set_ylim(-1.0, 1.0)
ax1.set_xlabel("Time [1000 years]")
ax1.set_ylabel("Change in $\delta^{18}O$")
ax2 = fig.add_subplot(2, 1, 2)
ax2.set_yscale('log')
# Convert frequency to Ma.
freq *= 1E6
ax2.plot(freq, spec, color='black')
ax2.fill_between(freq, jackknife[:, 0], jackknife[:, 1],
color="red", alpha=0.3)
import matplotlib.pyplot as plt
import numpy as np
import scipy.fftpack
from mtspec import mt_deconvolve, mtspec
from mtspec.util import _load_mtdata
plt.style.use("ggplot")
# Load and demean data.
pasc = _load_mtdata('PASC.dat.gz')
ado = _load_mtdata('ADO.dat.gz')
pasc -= pasc.mean()
ado -= ado.mean()
r = mt_deconvolve(pasc,
ado,
delta=1.0,
time_bandwidth=4.0,
number_of_tapers=7,
nfft=len(pasc),
demean=True,
weights="adaptive")
deconvolved = r["deconvolved"]
Pdeconv = deconvolved[-500:][::-1]
Pdeconv /= Pdeconv.max()
nfft = 2 * len(pasc)
pasc = scipy.fftpack.fft(pasc, n=nfft)
import matplotlib.pyplot as plt
plt.style.use("ggplot")
import numpy as np
from mtspec import mtspec
from mtspec.util import _load_mtdata
data = _load_mtdata("v22_174_series.dat.gz")
spec, freq, jackknife, fstatistics, _ = mtspec(
data=data, delta=4930., time_bandwidth=3.5, number_of_tapers=5,
nfft=312, statistics=True, rshape=0, fcrit=0.9)
# Convert to million years.
freq *= 1E6
plt.subplot(211)
plt.plot(freq, fstatistics, color="black")
plt.xlim(freq[0], freq[-1])
plt.xlabel("Frequency [c Ma$^{-1}]$")
plt.ylabel("F-statistics for periodic lines")
# Plot the confidence intervals.
for p in [90, 95, 99]:
y = np.percentile(fstatistics, p)
plt.hlines(y, freq[0], freq[-1], linestyles="--", color="0.2")
plt.text(x=99, y=y + 0.2, s="%i %%" % p, ha="right")
plt.subplot(212)
plt.semilogy(freq, spec, color="black")
plt.xlim(freq[0], freq[-1])
import matplotlib.pyplot as plt
plt.style.use("ggplot")
import numpy as np
from mtspec import mtspec
from mtspec.util import _load_mtdata
data = _load_mtdata('v22_174_series.dat.gz')
# Calculate the spectral estimation.
spec, freq, jackknife, _, _ = mtspec(data=data,
delta=4930.0,
time_bandwidth=3.5,
number_of_tapers=5,
nfft=312,
statistics=True)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
# Plot in thousands of years.
ax1.plot(np.arange(len(data)) * 4.930, data, color='black')
ax1.set_xlim(0, 800)
ax1.set_ylim(-1.0, 1.0)
ax1.set_xlabel("Time [1000 years]")
ax1.set_ylabel("Change in $\delta^{18}O$")
ax2 = fig.add_subplot(2, 1, 2)
ax2.set_yscale('log')
# Convert frequency to Ma.
