/
pulsarTools.py
143 lines (132 loc) · 4.44 KB
/
pulsarTools.py
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from __future__ import division, print_function
def initializer(dataSet):
import numpy as np
dt = 6.54761904761905e-05
if dataSet == 'Set1':
values = np.fromfile('dataFiles/ts_B0523+11_DM79.30.dat','float32')
if dataSet == 'Set2':
values = np.fromfile('dataFiles/ts_B1737+13_DM48.70.dat','float32')
if dataSet == 'Set3':
values = np.fromfile('dataFiles/ts_B1848+12_DM70.60.dat','float32')
times = np.linspace(0,dt*len(values),len(values))
return times,values
def fft(times,values):
import numpy as np
dt = times[1]-times[0]
N = len(times)
freqs = np.fft.fftfreq(N,d=dt)
fourier = np.fft.fft(values)
freqs = np.fft.fftshift(freqs)
fourier = np.fft.fftshift(fourier)
return freqs,fourier
def folder(times,values,trialPeriod):
import numpy as np
dt = float(times[1]-times[0])
nTimes = int(np.ceil(trialPeriod/dt))
excess = nTimes*dt-trialPeriod
accumulatedExcess = 0
nRemain = len(values)
foldedValues = []
j = 0
while nRemain > nTimes:
accumulatedExcess += excess
if accumulatedExcess < dt:
foldedValues.append(values[j:j+nTimes])
nRemain -= nTimes
j += nTimes
else:
accumulatedExcess = 0
nRemain += 1
j -= 1
foldedValues.append(values[j:j+nTimes])
nRemain -= nTimes
j += nTimes
return np.array(foldedValues)
def significance(foldedValues):
import numpy as np
collapsed = np.zeros(len(foldedValues[0]))
for i in range(len(foldedValues)):
collapsed += foldedValues[i]
collapsed /= len(foldedValues)
mean = np.mean(collapsed)
X2 = 0
for i in range(len(collapsed)):
X2 += (collapsed[i]-mean)**2
X2 /= float(len(collapsed))
return X2
def periodSearch(times,values,periodLB,periodUB,nPeriod):
import numpy as np
X2s = []
trialPeriods = np.linspace(periodLB,periodUB,nPeriod)
for i in range(nPeriod):
foldedValues = folder(times,values,trialPeriods[i])
X2s.append(significance(foldedValues))
print str(100*float(i+1)/nPeriod)+'% complete'
return trialPeriods,X2s
def plot_powerSpectrum(freqs,fourier):
import numpy as np
import pylab as py
powerSpectrum = np.real(fourier*np.conj(fourier))
for i in range(len(freqs)):
if np.abs(freqs[i]) < 0.3:
powerSpectrum[i] = 0
fig = py.figure()
ax = fig.add_axes([0.15,0.12,0.78,0.78])
ax.plot(freqs,powerSpectrum)
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Power [Arbitrary Units]')
ax.set_xlim(min(freqs),max(freqs))
py.show()
def plot_folded(trialPeriod,foldedValues):
import numpy as np
import pylab as py
nPeriod = len(foldedValues)
nTimes = len(foldedValues[0])
fig = py.figure()
ax = fig.add_axes([0.15,0.12,0.78,0.78])
ax.pcolormesh(foldedValues)
ax.set_xlim(0,nTimes)
ax.set_ylim(0,nPeriod)
ax.set_xlabel('Phase')
ax.set_ylabel('Time [s]')
xtick_locs = np.linspace(0,nTimes,5)
xtick_lbls = np.linspace(0,1,5)
for i in range(len(xtick_lbls)):
xtick_lbls[i] = float(str(xtick_lbls[i]))
ytick_locs = np.linspace(0,nPeriod,5)
ytick_lbls = np.linspace(0,nPeriod*trialPeriod,5)
for i in range(len(ytick_lbls)):
ytick_lbls[i] = float(str(ytick_lbls[i])[0:min(6,len(str(ytick_lbls[i]))-1)])
py.xticks(xtick_locs,xtick_lbls)
py.yticks(ytick_locs,ytick_lbls)
py.show()
def plot_collapsed(foldedValues):
import numpy as np
import pylab as py
nPeriod = len(foldedValues)
nTimes = len(foldedValues[0])
collapsed = np.zeros(len(foldedValues[0]))
for i in range(len(foldedValues)):
collapsed += foldedValues[i]
collapsed /= len(foldedValues)
fig = py.figure()
ax = fig.add_axes([0.15,0.12,0.78,0.78])
ax.plot(collapsed)
ax.set_xlim(0,nPeriod)
ax.set_ylabel('Intensity [Arbitrary Units]')
ax.set_xlabel('Phase')
xtick_locs = np.linspace(0,nTimes,5)
xtick_lbls = np.linspace(0,1,5)
for i in range(len(xtick_lbls)):
xtick_lbls[i] = float(str(xtick_lbls[i])[0:min(6,len(str(xtick_lbls[i]))-1)])
py.xticks(xtick_locs,xtick_lbls)
py.show()
def plot_X2s(periods,X2s):
import pylab as py
fig = py.figure()
ax = fig.add_axes([0.15,0.12,0.78,0.78])
ax.plot(periods,X2s)
ax.set_xlim(min(periods),max(periods))
ax.set_xlabel('Period [s]')
ax.set_ylabel('Significance')
py.show()