def analysis(self):
import numpy as np
import matplotlib.pylab as mpl
from PyAstronomy.pyTiming import pyPDM
for star in self.stars:
#i = self.stars.index(star)
print star, '\t',
t, m, e = self.lightcurve_fromdb(star)
t -= min(t)
S = pyPDM.Scanner(minVal=0.1,
maxVal=10.0,
dVal=10. / 86400.,
mode="period")
P = pyPDM.PyPDM(t, m)
p1, t1 = P.pdmEquiBinCover(100, 3, S)
# For comparison, carry out PDM analysis using 10 bins equidistant
# bins (no covers).
#p2, t2 = P.pdmEquiBin(100, S)
print '%.3f\t%.2f' % (p1[np.argmin(t1)], min(t1))
# Show the result
mpl.figure(facecolor='white')
mpl.title("Result of PDM analysis")
mpl.xlabel("Period")
mpl.ylabel("Theta")
mpl.plot(p1, t1)
#mpl.plot(p2, t2, 'gp-')
#mpl.legend(["pdmEquiBinCover", "pdmEquiBin"])
#mpl.savefig('/work1/jwe/NGC1647/results/'+star+'.png')
mpl.show()
def computePDMA(npjdmag):
timedata = npjdmag[:, 0]
magdata = npjdmag[:, 1]
S = pyPDM.Scanner(minVal=0.005, maxVal=20, dVal=0.0001, mode="frequency")
P = pyPDM.PyPDM(timedata, magdata)
bindata = int(len(magdata) / 4)
#bindata = 10
f2, t2 = P.pdmEquiBin(bindata, S)
delta = np.min(t2)
pdmp = 1 / f2[np.argmin(t2)]
return pdmp, delta
def sanity_Scanner(self):
# Import PDM module
from PyAstronomy.pyTiming import pyPDM
# Get Scanner instance
scanner = pyPDM.Scanner(minVal=0.5,
maxVal=1.0,
dVal=0.05,
mode="period")
# Print the periods covered by the scanner
print("Periods: ", end=' ')
for period in scanner:
print(period, end=' ')
def pdm_period(time, magnitude, error=None, **kwargs):
# scanner creation
sparams = {"minVal": 0.01, "maxVal": 1, "dVal": 0.001}
sparams.update(kwargs)
sparams["mode"] = "period"
scanner = pyPDM.Scanner(**sparams)
pdm = pyPDM.PyPDM(time, magnitude)
periods, frequencies = pdm.pdmEquiBin(20, scanner)
period = periods[np.argmin(frequencies)]
return period
def scanner(self):
# Import PDM module
from PyAstronomy.pyTiming import pyPDM
# Get Scanner instance
scanner = pyPDM.Scanner(minVal=0.1,
maxVal=1.0,
dVal=0.01,
mode="period")
# Print the periods covered by the scanner
print "Periods: ",
for period in scanner:
print period,
def sanity_PDMAna(self):
import numpy
import matplotlib.pylab as plt
from PyAstronomy.pyTiming import pyPDM
# Create artificial data with frequency = 3,
# period = 1/3
x = numpy.arange(100) / 100.0
y = numpy.sin(x * 2.0 * numpy.pi * 3.0 + 1.7)
# Get a ``scanner'', which defines the frequency interval to be checked.
# Alternatively, also periods could be used instead of frequency.
S = pyPDM.Scanner(minVal=0.5, maxVal=5.0, dVal=0.01, mode="frequency")
# Carry out PDM analysis. Get frequency array
# (f, note that it is frequency, because the scanner's
# mode is ``frequency'') and associated Theta statistic (t).
# Use 10 phase bins and 3 covers (= phase-shifted set of bins).
P = pyPDM.PyPDM(x, y)
f1, t1 = P.pdmEquiBinCover(10, 3, S)
# For comparison, carry out PDM analysis using 10 bins equidistant
# bins (no covers).
f2, t2 = P.pdmEquiBin(10, S)
def analysis(self):
import numpy
import matplotlib.pylab as mpl
from PyAstronomy.pyTiming import pyPDM
# Create artificial data with frequency = 3,
# period = 1/3
x = self.hjd
y = self.mag
# Get a ``scanner'', which defines the frequency interval to be checked.
# Alternatively, also periods could be used instead of frequency.
S = pyPDM.Scanner(minVal=0.163,
maxVal=0.33,
dVal=0.00001,
mode="period")
# Carry out PDM analysis. Get frequency array
# (f, note that it is frequency, because the scanner's
# mode is ``frequency'') and associated Theta statistic (t).
# Use 10 phase bins and 3 covers (= phase-shifted set of bins).
P = pyPDM.PyPDM(x, y)
p1, t1 = P.pdmEquiBinCover(100, 3, S)
# For comparison, carry out PDM analysis using 10 bins equidistant
# bins (no covers).
p2, t2 = P.pdmEquiBin(100, S)
# Show the result
mpl.figure(facecolor='white')
mpl.title("Result of PDM analysis")
mpl.xlabel("Period")
mpl.ylabel("Theta")
mpl.plot(p1, t1, 'bp-')
mpl.plot(p2, t2, 'gp-')
#mpl.legend(["pdmEquiBinCover", "pdmEquiBin"])
mpl.show()
def PMD(x,
y,
dy,
pVal,
mode="period",
label=None,
figout=None,
pmd_para=[10, 3]):
'''
>x,y,dy 时间，星等，误差
>pVal:[最小周期，最大周期，周期间隔]
>label-----figout--------outdir
曲线解释---图片保存名称---图片保存目录
>作图：原始数据，周期theta图，相位图
'''
import numpy as np
import matplotlib.pyplot as plt
from PyAstronomy.pyTiming import pyPDM
if not isinstance(x, np.ndarray):
x = np.array(x)
y = np.array(y)
dy = np.array(dy)
S = pyPDM.Scanner(minVal=pVal[0], maxVal=pVal[1], dVal=pVal[2], mode=mode)
P = pyPDM.PyPDM(x, y)
time, theta = P.pdmEquiBinCover(pmd_para[0], pmd_para[1], S)
fig, ax = plt.subplots(3)
fig.set_size_inches(30, 20)
ax[0].invert_yaxis()
ax[2].invert_yaxis()
ax[0].grid()
ax[1].grid()
ax[2].grid()
ax[0].errorbar(x, y, dy, fmt='o')
ax[0].legend([label], loc='upper right', fontsize=20, frameon=False)
ax[1].set_title("Result of PDM analysis")
ax[1].set_xlabel(mode)
ax[1].set_ylabel("Theta")
ax[1].plot(time, theta, 'bp-')
best_period = time[np.argmin(theta)]
peak_theta = np.min(theta)
ax[1].plot(best_period, peak_theta, 'ro')
ax[1].legend(
['pdmEquiBinCover', 'peak is ' + str(round(best_period, 10)) + 'd'],
loc='lower right',
fontsize=15,
frameon=False)
if mode == "period":
ax[2].errorbar((x / best_period) % 1, y, dy, fmt='o')
else:
ax[2].errorbar((x * best_period) % 1, y, dy, fmt='o')
ax[2].legend(
['folded data with period=' + str(round(best_period, 10)) + 'd'],
loc='upper right',
fontsize=20)
if figout != None:
plt.savefig(figout, dpi=600)
plt.show()
return time, theta, best_period
import sys
import numpy as np
from PyAstronomy.pyTiming import pyPDM
nbins = 10
covers = 3
nd = 1000
P = 10.0
jd = np.linspace(0.0, 100.0, nd, dtype=np.double)
fs = np.sin(jd / P * 2.0 * np.pi) + np.random.randn(nd) * 0.1
S = pyPDM.Scanner(minVal=1.0 / (50.0),
maxVal=1.0e0,
dVal=1.0e-4,
mode="frequency")
P = pyPDM.PyPDM(jd, fs)
f1, t1 = P.pdmEquiBinCover(nbins, covers, S)
f2, t2 = P.pdmEquiBin(nbins, S)
#adding the noise # we use a normal distibution with a std deviation of 1.0, centered at 0 stddevs=1.0 #shape our noise to the signal's dimensions noise=numpy.random.normal(0,stddevs,x.shape) #add to the signal y1=y+noise""" #============================================================================ #============================================================================ #============================================================================ #============================================================================ # Get a ``scanner'', which defines the frequency interval to be checked. # Alternatively, also periods could be used instead of frequency. S = pyPDM.Scanner(minVal=0.5, maxVal=BinUp, dVal=.1, mode="period") # Carry out PDM analysis. Get frequency array # (f, note that it is frequency, because the scanner's # mode is ``period'') and associated Theta statistic (t). #here is where we feed te data P = pyPDM.PyPDM(fxps0, fxps1) #fixed data #P = pyPDM.PyPDM(mvps0,mvps1) #moving data f1, t1 = P.pdmEquiBinCover(10, 3, S) #PDM analysis using bins (no covers). M = 7 f2, t2 = P.pdmEquiBin(M, S) #local minima empty list to be appended
num_params_psd_per = 3
else:
num_params_psd_per = 0
num_params_psd = (num_params_psd_sto+num_params_psd_per)
freq_limit = parset["FreqLimit"]
V = parset['V']
W = parset['W']
load_lcdata()
load_sample()
tspan = lc[-1, 0] - lc[0, 0]
print "Tspan:", tspan/365.0
ls_freq = np.logspace(np.log10(1.0/tpmin), np.log10(1.0/(tspan/ncycle)), 500)
pdm_scan = pyPDM.Scanner(minVal=tpmin, maxVal=tspan/ncycle, dVal=(tspan/ncycle-tpmin)/500, mode="period")
if num_params_psd_per == 0:
nd_sim = sample.shape[1] - num_params_psd
else:
nd_sim = ((sample.shape[1] - num_params_psd) * 2)//3
print sample.shape[1], nd_sim, num_params_psd
#test_genlc()
pbTR, TR, TR_obs = pb_TR(doplot=True)
pbTLS, TLS, TLS_obs = pb_TLS(doplot=True)
pbTPDM, TPDM, TPDM_obs = pb_TPDM(doplot=True)
np.savetxt("TR.txt", np.stack((TR, TR_obs), axis=-1), fmt="%15.5f %15.5f")
def LS(kic, kernerforca, Pbeg, Pend, PeriodMod, path='TEMP/'):
path = path + str(kic)
data = np.loadtxt(path + '\TS_' + str(kic) + '.txt')
time = data[:, 0]
flux = data[:, 1]
time = time / (60 * 60 * 24)
g = Gaussian1DKernel(kernerforca, mode='oversample', factor=5000)
z = convolve(flux, g, boundary='wrap', normalize_kernel=True)
plt.plot(time, z, '-b')
plt.plot(time, flux, '-k', alpha=0.5)
plt.show()
print("Entering the analysis by the method: ", PeriodMod)
if PeriodMod == 'LS':
f = np.linspace(1 / Pend, 1 / Pbeg, 50000)
p = LombScargle(time, z).power(f)
y = p**2 / f
fig = plt.figure(figsize=(8, 5), dpi=130)
plt.plot(1. / f, y, 'k', lw=0.5)
sig1 = np.percentile(y, [1.0])
sig5 = np.percentile(y, [1])
xlim = (1. / f[0], 1. / f[-1])
plt.plot(xlim, [sig1, sig1],
':',
c='r',
alpha=1,
lw=0.5,
label='FAP 1\%')
plt.xlabel("Period[days]")
plt.ylabel("Power")
i = peakutils.indexes(y, thres=0.99)
prot = 1. / f[i]
plt.legend(loc='best')
plt.savefig(path + '/' + str(kic) + '_LS.pdf')
plt.show()
if PeriodMod == 'PDM':
x = time
y = z
S = pyPDM.Scanner(minVal=1 / Pend,
maxVal=1 / Pbeg,
dVal=0.00001,
mode="frequency")
P = pyPDM.PyPDM(x, y)
f1, t1 = P.pdmEquiBinCover(5, 1, S)
f2, t2 = P.pdmEquiBin(10, S)
fig = plt.figure(figsize=(8, 5), dpi=130)
plt.xlabel("Frequency [days$^{-1}$]")
plt.ylabel("Theta")
plt.plot(f1, t1, 'b-', lw=0.5)
plt.plot(f2, t2, lw=0.5, alpha=0.5)
j = peakutils.indexes((-t2), thres=0.95)
aa = f2[j]
bb = t2[j]
labels = np.array(np.round(1 / f2[j], 2))
texts = []
for x, y, s in zip(aa, bb, labels):
texts.append(plt.text(x, y, s, fontsize=7))
plt.legend(["Multiple sequences", "Equidistant bins"])
plt.grid(alpha=0.5)
plt.show()
def periodogram(lightcurve_in,
name="",
retparam="",
fixperiod="",
pdm=0,
double=0,
talk=0,
plot=0,
location="",
limits=None):
if name == "": name = 'test'
if retparam == "": retparam = 'N'
if talk == "": talk = 0
minperiod = 0.01 # minimum period to check
maxperiod = 2.0 # maximum period to check
f = np.linspace(0.01, 10, 1000000) # checks 10 to 100 days
#f = np.linspace(0.26, 0.28, 1000000) # checks 10 to 100 days
lightcurve = []
lightcurve.append(lightcurve_in[0][np.argsort(lightcurve_in[0])])
lightcurve.append(lightcurve_in[1][np.argsort(lightcurve_in[0])])
lightcurve.append(lightcurve_in[2][np.argsort(lightcurve_in[0])])
lightcurve = np.array(lightcurve)
if pdm == 0:
pgram = LombScargle(lightcurve[0], lightcurve[1]).power(f)
maxper = np.argmax(pgram[np.where(1.0 / f > minperiod)])
period = fixperiod
else:
start = time.time()
s = pyPDM.Scanner(minVal=0.01, maxVal=10, dVal=1e-5, mode="frequency")
p = pyPDM.PyPDM(lightcurve[0], lightcurve[1])
#f, theta = p.pdmEquiBinCover(10, 3, s)
f, theta = p.pdmEquiBin(10, s)
minTheta = np.argmin(theta)
period = 1 / (f[minTheta])
pgram = 1 - theta
fixperiod = 0
end = time.time()
print(end - start)
if fixperiod == "":
period = 1.0 * 1. / f[maxper]
elif (float(fixperiod) < 0.0):
fixperiod = float(fixperiod)
check = np.where((1.0 / f > -0.9 * fixperiod)
& (1.0 / f < -1.1 * fixperiod))
f_cut = f[check[0]]
pgram_cut = pgram[check[0]]
maxper = np.argmax(pgram_cut)
period = 1.0 / f_cut[maxper]
try:
fixperiod = -float(fixperiod)
except ValueError:
pass
if (talk == 1):
print("period: ", period)
if plot == 1:
# plot the periodogram
plt.clf()
# plt.figure(figsize=(6, 4))
# plt.rcParams.update({'font.size': 8})
# rectangle = [0.1, 0.55, 0.35, 0.35]
# ax = plt.axes(rectangle)
days = 1 / f
plt.plot(
days[np.where(days <= (period * 2 if not double else period * 4))],
pgram[np.where(
days <= (period * 2 if not double else period * 4))], 'k-')
#plt.xlim(0, 3.0 * period)
# plt.xlim(0,6.0)
plt.xlabel('Time (d)')
if pdm == 0:
plt.ylabel('Frequency')
else:
plt.ylabel(r'$1-\theta$')
if limits:
plt.xlim(limits)
#plt.legend(loc='upper center', handletextpad=0.01)
# # plot the lightcurve
# rectangle = [0.55, 0.55, 0.35, 0.35]
# ax = plt.axes(rectangle)
# plt.plot(lightcurve[0] - 2400000.5, lightcurve[1], color='0.7', marker='.',
# linestyle='solid', markersize=0.1)
# plt.plot(lightcurve[0] - 2400000.5, lightcurve[1], color='Red', marker='o',
# linestyle='none', markersize=1)
# plt.ylim(np.median(lightcurve[1]) + 5.0 * np.std(lightcurve[1]),
# np.median(lightcurve[1]) - 5.0 * np.std(lightcurve[1]))
# plt.locator_params(axis='x', nbins=4)
# plt.locator_params(axis='y', nbins=5)
# plt.xlabel('Time, MJD (d)')
# plt.ylabel('Magnitude {} (mag)'.format('R'))
if not os.path.exists(location + name + '/'):
os.mkdir(location + name + '/')
plt.savefig(location + name + '/' + name + '_Periodogram' +
('(LS) ' if pdm == 0 else '(PDM)') + '.pdf',
format='pdf',
bbox_inches='tight',
dpi=600)
plt.savefig(location + name + '/' + name + '_Periodogram' +
('(LS) ' if pdm == 0 else '(PDM)') + '.png',
format='png',
bbox_inches='tight',
dpi=600)
plt.show()
if double:
return period * 2
else:
return period
# pull the date and magnitude into lists
afoev = pd.read_excel('ZUMa_recent_AFOEV_noNTS.xlsx', sheetname='Sheet1')
mod_jul = afoev['Modified Julian Date']
mag = afoev['Visual magnitude']
#we need to see the fairweather observers
#use counter to determine the number of times
#observer has taken measurements
#occur=Counter(afoev['Observer'])
#print (occur)
#this allows us to remove inconsistent observers
# Get a ``scanner'', which defines the frequency interval to be checked.
# Alternatively, also periods could be used instead of frequency.
S = pyPDM.Scanner(minVal=0.5, maxVal=250.0, dVal=.1, mode="frequency")
# Carry out PDM analysis. Get frequency array
# (f, note that it is frequency, because the scanner's
# mode is ``frequency'') and associated Theta statistic (t).
# Use 10 phase bins and 3 covers (= phase-shifted set of bins).
P = pyPDM.PyPDM(mod_jul, mag)
#f1, t1 = P.pdmEquiBinCover(3, 6, S)
# For comparison, carry out PDM analysis using bins (no covers).
f2, t2 = P.pdmEquiBin(10, S)
#plot the cleaned up data
plt.subplot(2, 1, 1)
plt.plot(mod_jul, mag, 'r.')
plt.xlabel('modified julian date')
plt.ylabel('approx vis magnitude')
