def getbandpass(M):
feature = '%s:%s' % ('bandpass', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
self.extracted_feature[feature] = normalize(downsample(profs.sum(0).sum(1),M).ravel())
return self.extracted_feature[feature]
def getsubbands(M):
feature = '%s:%s' % ('subbands', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(0))
#U,S,V = svd(img)
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
#self.extracted_feature[feature] = normalize(downsample(img, M, align=self.align).ravel())
self.extracted_feature[feature] = normalize(
downsample(img, M, align=self.align)).ravel()
pfd_data = pfddata(os.path.join(input_dir, fn))
fig = plt.figure()
#fig.set_size_inches(5,5)
#ax = plt.Axes(fig, [0., 0., 1., 1.])
ax = plt.gca()
ax.set_axis_off()
#fig.add_axes(ax)
plt.imsave("%s_subbands.png" % os.path.join(output_dir, fn),
img,
origin='lower',
cmap=plt.cm.gray_r)
#plt.imshow(img, origin='lower', aspect='auto', interpolation='bilinear', cmap=plt.cm.gray_r) #aspect='auto'plt.cm.Greys) #interpolation='bilinear'
#plt.savefig("%s_subbands.png" % os.path.join(output_dir, fn))
return self.extracted_feature[feature]
def getfreqprofs(M):
feature = '%s:%s' % ('freqbins', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
self.extracted_feature[feature] = normalize(
downsample(profs.sum(1).sum(0), M).ravel())
return self.extracted_feature[feature]
def getsumprofs(M):
feature = '%s:%s' % ('phasebins', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
data = profs.sum(0).sum(0)
self.extracted_feature[feature] = normalize(downsample(data,M,align=self.align).ravel())
return self.extracted_feature[feature]
def getsumprofs(M):
feature = '%s:%s' % ('phasebins', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
data = profs.sum(0).sum(0)
self.extracted_feature[feature] = normalize(
downsample(data, M, align=self.align).ravel())
return self.extracted_feature[feature]
def getDMcurve(
M): # return the normalized DM curve downsampled to M points
feature = '%s:%s' % ('DMbins', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
ddm = (self.dms.max() - self.dms.min()) / 2.
loDM, hiDM = (self.bestdm - ddm, self.bestdm + ddm)
loDM = max((0, loDM)) #make sure cut off at 0 DM
hiDM = max((ddm, hiDM)) #make sure cut off at 0 DM
N = 100
interp = False
sumprofs = self.profs.sum(0)
if not interp:
profs = sumprofs
else:
profs = np.zeros(np.shape(sumprofs), dtype='d')
DMs = psr_utils.span(loDM, hiDM, N)
chis = np.zeros(N, dtype='f')
subdelays_bins = self.subdelays_bins.copy()
for ii, DM in enumerate(DMs):
subdelays = psr_utils.delay_from_DM(DM, self.barysubfreqs)
hifreqdelay = subdelays[-1]
subdelays = subdelays - hifreqdelay
delaybins = subdelays * self.binspersec - subdelays_bins
if interp:
interp_factor = 16
for jj in range(self.nsub):
profs[jj] = psr_utils.interp_rotate(
sumprofs[jj],
delaybins[jj],
zoomfact=interp_factor)
# Note: Since the interpolation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
avgprof = (profs / self.proflen).sum()
else:
new_subdelays_bins = np.floor(delaybins + 0.5)
for jj in range(self.nsub):
#profs[jj] = psr_utils.rotate(profs[jj], new_subdelays_bins[jj])
delay_bins = int(new_subdelays_bins[jj] %
len(profs[jj]))
if not delay_bins == 0:
profs[jj] = np.concatenate(
(profs[jj][delay_bins:],
profs[jj][:delay_bins]))
subdelays_bins += new_subdelays_bins
avgprof = self.avgprof
sumprof = profs.sum(0)
chis[ii] = self.calc_redchi2(prof=sumprof, avg=avgprof)
DMcurve = normalize(downsample(chis, M))
self.extracted_feature[feature] = DMcurve
return self.extracted_feature[feature]
def getsubbands(M):
feature = '%s:%s' % ('subbands', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(0))
#U,S,V = svd(img)
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
self.extracted_feature[feature] = normalize(downsample(img, M).ravel())
return self.extracted_feature[feature]
def getDMcurve(M): # return the normalized DM curve downsampled to M points
feature = '%s:%s' % ('DMbins', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
ddm = (self.dms.max() - self.dms.min())/2.
loDM, hiDM = (self.bestdm - ddm , self.bestdm + ddm)
loDM = max((0, loDM)) #make sure cut off at 0 DM
hiDM = max((ddm, hiDM)) #make sure cut off at 0 DM
N = 100
interp = False
sumprofs = self.profs.sum(0)
if not interp:
profs = sumprofs
else:
profs = np.zeros(np.shape(sumprofs), dtype='d')
DMs = psr_utils.span(loDM, hiDM, N)
chis = np.zeros(N, dtype='f')
subdelays_bins = self.subdelays_bins.copy()
for ii, DM in enumerate(DMs):
subdelays = psr_utils.delay_from_DM(DM, self.barysubfreqs)
hifreqdelay = subdelays[-1]
subdelays = subdelays - hifreqdelay
delaybins = subdelays*self.binspersec - subdelays_bins
if interp:
interp_factor = 16
for jj in range(self.nsub):
profs[jj] = psr_utils.interp_rotate(sumprofs[jj], delaybins[jj],
zoomfact=interp_factor)
# Note: Since the interpolation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
avgprof = (profs/self.proflen).sum()
else:
new_subdelays_bins = np.floor(delaybins+0.5)
for jj in range(self.nsub):
#profs[jj] = psr_utils.rotate(profs[jj], new_subdelays_bins[jj])
delay_bins = int(new_subdelays_bins[jj] % len(profs[jj]))
if not delay_bins==0:
profs[jj] = np.concatenate((profs[jj][delay_bins:], profs[jj][:delay_bins]))
subdelays_bins += new_subdelays_bins
avgprof = self.avgprof
sumprof = profs.sum(0)
chis[ii] = self.calc_redchi2(prof=sumprof, avg=avgprof)
DMcurve = normalize(downsample(chis, M))
self.extracted_feature[feature] = DMcurve
return self.extracted_feature[feature]
def getsubbands(M):
feature = '%s:%s' % ('subbands', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(0))
#U,S,V = svd(img)
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
#self.extracted_feature[feature] = normalize(downsample(img, M, align=self.align).ravel())
self.extracted_feature[feature] = normalize(
downsample(img, M, align=self.align)).ravel()
return self.extracted_feature[feature]
def getintervals(M):
feature = '%s:%s' % ('intervals', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(1))
#U,S,V = svd(img)
#imshow(img)
#m,n = img.shape
#S = resize(S,[m,1]) * eye(m,n)
#k = 6
#imshow(np.dot(U[:,1:k], dot(S[1:k,1:k],V[1:k,:])))
#show()
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
self.extracted_feature[feature] = normalize(downsample(img, M).ravel())
return self.extracted_feature[feature]
def getintervals(M):
feature = '%s:%s' % ('intervals', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(1))
#U,S,V = svd(img)
#imshow(img)
#m,n = img.shape
#S = resize(S,[m,1]) * eye(m,n)
#k = 6
#imshow(np.dot(U[:,1:k], dot(S[1:k,1:k],V[1:k,:])))
#show()
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
#self.extracted_feature[feature] = normalize(downsample(img, M, align=self.align).ravel())#wrong!
self.extracted_feature[feature] = normalize(
downsample(img, M, align=self.align)).ravel()
return self.extracted_feature[feature]
def getintervals(M):
feature = '%s:%s' % ('intervals', M)
if M == 0:
return np.array([])
if not feature in self.extracted_feature:
img = greyscale(self.profs.sum(1))
#U,S,V = svd(img)
#imshow(img)
#m,n = img.shape
#S = resize(S,[m,1]) * eye(m,n)
#k = 6
#imshow(np.dot(U[:,1:k], dot(S[1:k,1:k],V[1:k,:])))
#show()
#if M <= len(S):
#return S[:M]
#else:
#while len(S) < M:
#np.append(S, 0.)
#return S
#self.extracted_feature[feature] = normalize(downsample(img, M, align=self.align).ravel())#wrong!
self.extracted_feature[feature] = normalize(
downsample(img, M, align=self.align)).ravel()
#self.extracted_feature[feature] = img.ravel()
fig = plt.figure()
#fig.set_size_inches(5,5)
#ax = plt.Axes(fig, [0., 0., 1., 1.])
ax = plt.gca()
ax.set_axis_off()
#fig.add_axes(ax)
plt.imsave("%s_intervals.png" % os.path.join(output_dir, fn),
img,
origin='lower',
cmap=plt.cm.gray_r)
#plt.imshow(img, origin='lower', aspect='auto', interpolation='bilinear', cmap=plt.cm.gray_r) #plt.cm.Greys) #interpolation='bilinear'
#plt.savefig("%s_intervals.png" % os.path.join(output_dir, fn))
return self.extracted_feature[feature]
