def imshow(self,
err=False,
cbar=False,
ax=None,
show=True,
border=False,
zorder=0,
cmap=cm.binary,
alpha=True,
cdf=True,
savefig=None,
acf=False,
ss=False,
extent=None,
log=False,
xlim=None,
ylim=None):
"""
Basic plotting of the dynamic spectrum
"""
if acf:
if self.acf is None:
self.acf2d()
data = self.acf
elif ss:
if self.ss is None:
self.secondary_spectrum(log=log)
data = self.ss
elif err:
data = self.errdata
else:
data = self.getData()
if extent is None:
if acf:
T = self.acfT
F = self.acfF
elif ss:
T = self.ssconjT
F = self.ssconjF
else:
if self.Tcenter is None:
T = self.T
else:
T = self.Tcenter
if self.Fcenter is None:
F = self.F
else:
F = self.Fcenter
if log and not ss:
data = np.log10(data)
if alpha and not (acf or ss): #or just ignore?
cmap.set_bad(alpha=0.0)
for i in range(len(data)):
for j in range(len(data[0])):
if data[i, j] <= 0.0: # or self.errdata[i][j]>3*sigma:
data[i, j] = np.nan
if cdf:
xcdf, ycdf = u.ecdf(data.flatten())
low, high = u.likelihood_evaluator(xcdf,
ycdf,
cdf=True,
values=[0.01, 0.99])
for i in range(len(data)):
for j in range(len(data[0])):
if data[i, j] <= low:
data[i, j] = low
elif data[i, j] >= high:
data[i, j] = high
if extent is None:
minT = T[0]
maxT = T[-1]
minF = F[0]
maxF = F[-1]
extent = [minT, maxT, minF, maxF]
#return np.shape(data)
#raise SystemExit
# print inds
# raise SystemExit
# spec[inds] = np.nan
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
cax = u.imshow(data, ax=ax, extent=extent, cmap=cmap, zorder=zorder)
if xlim is not None:
plt.xlim(xlim)
if ylim is not None:
plt.ylim(ylim)
#border here?
if border: # and self.extras['name']!='EFF I':
plt.plot([T[0], T[-1]], [F[0], F[0]], '0.50', zorder=zorder + 0.1)
plt.plot([T[0], T[-1]], [F[-1], F[-1]],
'0.50',
zorder=zorder + 0.1)
plt.plot([T[0], T[0]], [F[0], F[-1]], '0.50', zorder=zorder + 0.1)
plt.plot([T[-1], T[-1]], [F[0], F[-1]],
'0.50',
zorder=zorder + 0.1)
if acf:
plt.xlabel('Time Lag (%s)' % self.Tunit)
plt.ylabel('Frequency Lag (%s)' % self.Funit)
elif ss:
plt.xlabel('Conjugate Time (1/%s)' % self.Tunit)
plt.ylabel('Conjugate Frequency (1/%s)' % self.Funit)
else:
plt.xlabel('Time (%s)' % self.Tunit)
plt.ylabel('Frequency (%s)' % self.Funit)
if cbar:
plt.colorbar(cax)
#im.set_clim(0.0001,None)
if savefig is not None:
plt.savefig(savefig)
if show:
plt.show()
return ax
def imshow(self,
err=False,
cbar=False,
ax=None,
show=True,
border=False,
ZORDER=0,
cmap=cm.binary,
alpha=True,
cdf=True):
"""
Basic plotting of the dynamic spectrum
"""
if err:
spec = self.errdata
else:
spec = self.data
T = self.T
if self.T is None:
if self.Tcenter is None:
T = np.arange(len(spec))
else:
T = self.Tcenter
F = self.F
if self.F is None:
if self.Fcenter is None:
F = np.arange(len(spec[0]))
else:
F = self.Fcenter
#cmap = cm.binary#jet
if alpha:
cmap.set_bad(alpha=0.0)
if alpha: #do this?
for i in range(len(spec)):
for j in range(len(spec[0])):
if spec[i, j] <= 0.0: # or self.errdata[i][j]>3*sigma:
spec[i, j] = np.nan
minT = T[0]
maxT = T[-1]
minF = F[0]
maxF = F[-1]
if cdf:
xcdf, ycdf = u.ecdf(spec.flatten())
low, high = u.likelihood_evaluator(xcdf,
ycdf,
cdf=True,
values=[0.01, 0.99])
for i in range(len(spec)):
for j in range(len(spec[0])):
if spec[i, j] <= low:
spec[i, j] = low
elif spec[i, j] >= high:
spec[i, j] = high
# print inds
# raise SystemExit
# spec[inds] = np.nan
cax = u.imshow(spec,
ax=ax,
extent=[minT, maxT, minF, maxF],
cmap=cmap,
zorder=ZORDER)
#border here?
if border: # and self.extras['name']!='EFF I':
plt.plot([T[0], T[-1]], [F[0], F[0]], '0.50', zorder=ZORDER + 0.1)
plt.plot([T[0], T[-1]], [F[-1], F[-1]],
'0.50',
zorder=ZORDER + 0.1)
plt.plot([T[0], T[0]], [F[0], F[-1]], '0.50', zorder=ZORDER + 0.1)
plt.plot([T[-1], T[-1]], [F[0], F[-1]],
'0.50',
zorder=ZORDER + 0.1)
if cbar:
plt.colorbar(cax)
#im.set_clim(0.0001,None)
if show:
plt.show()
return ax