def __init__(self,
flux,
inputs,
mask=None,
p0=None,
kernel=None,
splits=[],
tr_nrandom=200,
tr_bspan=50,
tr_nblocks=6):
self.data = DtData(flux, inputs, mask)
self.kernel = kernel or BasicKernel()
self.gp = SplitGP(self.kernel,
splits) if splits is not None else GeorgeGP(
self.kernel)
self.tr_data = self.data.create_training_set(tr_nrandom, tr_bspan,
tr_nblocks)
self.gp.set_inputs(self.tr_data.masked_inputs)
def __init__(self, flux, inputs, mask=None, p0=None, kernel=None, splits=[], tr_nrandom=200, tr_bspan=50, tr_nblocks=6):
self.data = DtData(flux, inputs, mask)
self.kernel = kernel or BasicKernel()
self.gp = SplitGP(self.kernel, splits) if splits is not None else GeorgeGP(self.kernel)
self.tr_data = self.data.create_training_set(tr_nrandom, tr_bspan, tr_nblocks)
self.gp.set_inputs(self.tr_data.masked_inputs)
class Detrender(object):
def __init__(self,
flux,
inputs,
mask=None,
p0=None,
kernel=None,
splits=[],
tr_nrandom=200,
tr_bspan=50,
tr_nblocks=6):
self.data = DtData(flux, inputs, mask)
self.kernel = kernel or BasicKernel()
self.gp = SplitGP(self.kernel,
splits) if splits is not None else GeorgeGP(
self.kernel)
self.tr_data = self.data.create_training_set(tr_nrandom, tr_bspan,
tr_nblocks)
self.gp.set_inputs(self.tr_data.masked_inputs)
## ======================
## Convenience routines
## ======================
@property
def flux(self):
return self.data.masked_flux
@property
def time(self):
return self.data.masked_time
## =====================
## Detrending routines
## =====================
def covariance_matrix(self, pv=None, inputs=None, separate=False):
inputs = inputs if inputs is not None else self.tr_data.masked_inputs
self.gp.compute(inputs, pv)
return self.gp._covariance_matrix(inputs, separate=separate)
def neglnposterior(self, pv, training=True):
if any(pv < self.kernel.lims[0]) or any(self.kernel.lims[1] < pv):
return inf
ds = self.tr_data if training else self.data
return -(self.kernel.ln_prior(pv) + self.gp.lnlikelihood(
pv, ds.masked_normalised_flux, ds.masked_inputs))
def train(self, pv0=None, disp=False):
pv0 = pv0 if pv0 is not None else self.kernel.pv0
mres = minimize(self.neglnposterior, pv0, method='Powell')
self.tr_pv = mres.x.copy()
return self.tr_pv, mres.success
def predict(self, pv, inputs=None, components=False, mean_only=True):
inputs = inputs if inputs is not None else self.data.unmasked_inputs
self.gp.compute(self.data.masked_inputs, pv)
self.gp._compute_alpha(self.data.masked_normalised_flux)
if components:
mu_time, mu_pos = self.gp.predict_components(inputs)
return ((1. + mu_time) * self.data._fm,
(1. + mu_pos) * self.data._fm)
else:
return self.gp.predict(inputs, mean_only=mean_only)
def detrend_spatial(self, pv):
mt, mp = self.compute_components(pv)
flux = self.data.unmasked_flux.copy()
flux[self.data.mask] += -mp + median(mp)
flux[~self.data.mask] = nan
return flux
## ===================
## Plotting routines
## ===================
def plot_xy(self, pv=None, ax=None, plot_wireframe=False):
"""Plot the x and y points for the whole dataset and the training set.
"""
if ax is None:
fig, ax = subplots(1, 1, figsize=(10, 10))
if pv is None:
ax.tripcolor(self.data.mx,
self.data.my,
ones(self.data.nptm),
vmin=0,
vmax=1)
if plot_wireframe:
ax.triplot(self.data.mx, self.data.my, color='w')
else:
mt, mp = self.compute_components(pv)
ax.tripcolor(self.data.mx, self.data.my, mp)
ax.plot(self.tr_data.mx, self.tr_data.my, 'o', ms=3, c='k', mec='w')
return ax
def plot_t(self, pv=None, ax=None):
""" Plot the flux as a function of time for the whole dataset and the training set.
"""
if ax is None:
fig, ax = subplots(1, 1)
fm = self.data.flux_median
fmin = self.data.masked_flux.min()
fmax = self.data.masked_flux.max()
fptp = self.data.masked_flux.ptp()
ax.plot(self.data.mt, self.data.mf, c='0.75', lw=1)
ax.plot(self.tr_data.ut, self.tr_data.uf, '.k', ms=6)
setp(ax, ylim=(0.999 * fmin, 1.001 * fmax))
if pv is not None:
fd = self.detrend_spatial(pv)
fd += fm - np.nanmedian(fd)
mm = isfinite(fd)
ax.plot(self.data.unmasked_time[mm],
fd[mm] - 0.7 * fptp,
alpha=0.75,
lw=1)
setp(ax, ylim=(0.999 * (fmin - 0.7 * fptp), 1.001 * fmax))
setp(ax, xlim=self.data.mt[[0, -1]], xlabel='Time', ylabel='Flux')
return ax
def plot_report(self, pv, tid, fname=None, maxpt=350):
lmargin, rmargin = 0.12, 0.03
fig = pl.figure(figsize=(8.3, 11.7))
fig.text(0.04,
0.965,
'EPIC {:9d}'.format(tid),
va='top',
size=24,
color='w',
weight='bold')
ax = fig.add_axes([0, 0, 1, 1])
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.set_zorder(-1000)
ax.add_patch(pl.Rectangle((0, 0.92), 1, 0.08, fill=True))
ax_a = fig.add_axes([lmargin, 0.25, 1 - lmargin - rmargin, 0.3])
ax_x = fig.add_axes([lmargin, 0.05, 1 - lmargin - rmargin, 0.1])
ax_y = fig.add_axes([lmargin, 0.15, 1 - lmargin - rmargin, 0.1])
ax_c = fig.add_axes([0.55, 0.6, 0.45 - rmargin, 0.3])
ax_x.plot(self.data.masked_time, self.data.mx, lw=1)
ax_y.plot(self.data.masked_time, self.data.my, lw=1)
## Compute stuff
## -------------
fm = median(self.data.masked_flux)
fmin = self.data.masked_flux.min()
fmax = self.data.masked_flux.max()
fptp = self.data.masked_flux.ptp()
mt, mp = self.compute_components(pv)
ms = self.data.mask
fd = self.data.unmasked_flux.copy()
fd[ms] += -mp + median(mp)
fd[~ms] = nan
fd += fm - np.nanmedian(fd)
## Plot A
## ------
ax_a.plot(self.data.masked_time,
self.data.masked_flux / fm,
c='0.75',
lw=1)
ax_a.plot(self.tr_data.unmasked_time,
self.tr_data.unmasked_flux / fm,
'.k',
ms=6)
ax_a.plot(*self.data.outliers, ls='', marker='o', ms=6)
ax_a.plot(self.data.unmasked_time[ms], (fd[ms] - 0.7 * fptp) / fm,
lw=1)
ax_a.plot(self.time, (mp - 1.4 * fptp) / fm, lw=1)
samples = permutation(self.time.size)[:maxpt]
ax_c.tripcolor(self.data.mx[samples], self.data.my[samples],
mp[samples])
ax_c.plot(self.tr_data.mx,
self.tr_data.my,
'.',
ms=3,
c='w',
alpha=0.8)
ax_c.plot(self.tr_data.mx, self.tr_data.my, '.', ms=1.5, c='k')
setp(ax_a, ylim=(0.999 * (fmin - 1.4 * fptp) / fm, 1.001 * fmax / fm))
setp(ax_a.get_xticklabels() + ax_y.get_xticklabels(), visible=False)
setp(ax_x, xlabel='Time', ylabel='X')
setp(ax_c, xlabel='X', ylabel='Y')
setp([ax_a, ax_x, ax_y], xlim=self.time[[0, -1]])
setp(ax_a, ylabel='Normalised flux')
setp(ax_y, ylabel='Y')
if fname:
fig.savefig(fname)
class Detrender(object):
def __init__(self, flux, inputs, mask=None, p0=None, kernel=None, splits=[], tr_nrandom=200, tr_bspan=50, tr_nblocks=6):
self.data = DtData(flux, inputs, mask)
self.kernel = kernel or BasicKernel()
self.gp = SplitGP(self.kernel, splits) if splits is not None else GeorgeGP(self.kernel)
self.tr_data = self.data.create_training_set(tr_nrandom, tr_bspan, tr_nblocks)
self.gp.set_inputs(self.tr_data.masked_inputs)
## ======================
## Convenience routines
## ======================
@property
def flux(self):
return self.data.masked_flux
@property
def time(self):
return self.data.masked_time
## =====================
## Detrending routines
## =====================
def covariance_matrix(self, pv=None, inputs=None, separate=False):
inputs = inputs if inputs is not None else self.tr_data.masked_inputs
self.gp.compute(inputs, pv)
return self.gp._covariance_matrix(inputs, separate=separate)
def neglnposterior(self, pv, training=True):
if any(pv < self.kernel.lims[0]) or any(self.kernel.lims[1] < pv):
return inf
ds = self.tr_data if training else self.data
return -(self.kernel.ln_prior(pv) +
self.gp.lnlikelihood(pv, ds.masked_normalised_flux, ds.masked_inputs))
def train(self, pv0=None, disp=False):
pv0 = pv0 if pv0 is not None else self.kernel.pv0
mres = minimize(self.neglnposterior, pv0, method='Powell')
self.tr_pv = mres.x.copy()
return self.tr_pv, mres.success
def predict(self, pv, inputs=None, components=False, mean_only=True):
inputs = inputs if inputs is not None else self.data.unmasked_inputs
self.gp.compute(self.data.masked_inputs, pv)
self.gp._compute_alpha(self.data.masked_normalised_flux)
if components:
mu_time, mu_pos = self.gp.predict_components(inputs)
return ((1. + mu_time) * self.data._fm,
(1. + mu_pos) * self.data._fm)
else:
return self.gp.predict(inputs, mean_only=mean_only)
def detrend_spatial(self, pv):
mt, mp = self.compute_components(pv)
flux = self.data.unmasked_flux.copy()
flux[self.data.mask] += -mp + median(mp)
flux[~self.data.mask] = nan
return flux
## ===================
## Plotting routines
## ===================
def plot_xy(self, pv=None, ax=None, plot_wireframe=False):
"""Plot the x and y points for the whole dataset and the training set.
"""
if ax is None:
fig,ax = subplots(1,1, figsize=(10,10))
if pv is None:
ax.tripcolor(self.data.mx, self.data.my, ones(self.data.nptm), vmin=0, vmax=1)
if plot_wireframe:
ax.triplot(self.data.mx, self.data.my, color='w')
else:
mt, mp = self.compute_components(pv)
ax.tripcolor(self.data.mx, self.data.my, mp)
ax.plot(self.tr_data.mx, self.tr_data.my, 'o', ms=3, c='k', mec='w')
return ax
def plot_t(self, pv=None, ax=None):
""" Plot the flux as a function of time for the whole dataset and the training set.
"""
if ax is None:
fig, ax = subplots(1,1)
fm = self.data.flux_median
fmin = self.data.masked_flux.min()
fmax = self.data.masked_flux.max()
fptp = self.data.masked_flux.ptp()
ax.plot(self.data.mt, self.data.mf, c='0.75', lw=1)
ax.plot(self.tr_data.ut, self.tr_data.uf, '.k', ms=6)
setp(ax, ylim=(0.999*fmin,1.001*fmax))
if pv is not None:
fd = self.detrend_spatial(pv)
fd += fm - np.nanmedian(fd)
mm = isfinite(fd)
ax.plot(self.data.unmasked_time[mm], fd[mm] - 0.7*fptp, alpha=0.75, lw=1)
setp(ax, ylim=(0.999*(fmin-0.7*fptp), 1.001*fmax))
setp(ax, xlim=self.data.mt[[0,-1]], xlabel='Time', ylabel='Flux')
return ax
def plot_report(self, pv, tid, fname=None, maxpt=350):
lmargin, rmargin = 0.12, 0.03
fig = pl.figure(figsize=(8.3,11.7))
fig.text(0.04, 0.965, 'EPIC {:9d}'.format(tid), va='top', size=24, color='w', weight='bold')
ax = fig.add_axes([0,0,1,1])
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.set_zorder(-1000)
ax.add_patch(pl.Rectangle((0,0.92), 1, 0.08, fill=True))
ax_a = fig.add_axes([lmargin,0.25,1-lmargin-rmargin,0.3])
ax_x = fig.add_axes([lmargin,0.05,1-lmargin-rmargin,0.1])
ax_y = fig.add_axes([lmargin,0.15,1-lmargin-rmargin,0.1])
ax_c = fig.add_axes([0.55,0.6,0.45-rmargin, 0.3])
ax_x.plot(self.data.masked_time, self.data.mx, lw=1)
ax_y.plot(self.data.masked_time, self.data.my, lw=1)
## Compute stuff
## -------------
fm = median(self.data.masked_flux)
fmin = self.data.masked_flux.min()
fmax = self.data.masked_flux.max()
fptp = self.data.masked_flux.ptp()
mt, mp = self.compute_components(pv)
ms = self.data.mask
fd = self.data.unmasked_flux.copy()
fd[ms] += -mp + median(mp)
fd[~ms] = nan
fd += fm - np.nanmedian(fd)
## Plot A
## ------
ax_a.plot(self.data.masked_time, self.data.masked_flux/fm, c='0.75', lw=1)
ax_a.plot(self.tr_data.unmasked_time, self.tr_data.unmasked_flux/fm, '.k', ms=6)
ax_a.plot(*self.data.outliers, ls='', marker='o', ms=6)
ax_a.plot(self.data.unmasked_time[ms], (fd[ms] - 0.7*fptp)/fm, lw=1)
ax_a.plot(self.time, (mp-1.4*fptp)/fm, lw=1)
samples = permutation(self.time.size)[:maxpt]
ax_c.tripcolor(self.data.mx[samples], self.data.my[samples], mp[samples])
ax_c.plot(self.tr_data.mx, self.tr_data.my, '.', ms=3, c='w', alpha=0.8)
ax_c.plot(self.tr_data.mx, self.tr_data.my, '.', ms=1.5, c='k')
setp(ax_a, ylim=(0.999*(fmin-1.4*fptp)/fm, 1.001*fmax/fm))
setp(ax_a.get_xticklabels()+ax_y.get_xticklabels(), visible=False)
setp(ax_x, xlabel='Time', ylabel='X')
setp(ax_c, xlabel='X', ylabel='Y')
setp([ax_a,ax_x,ax_y], xlim=self.time[[0,-1]])
setp(ax_a, ylabel='Normalised flux')
setp(ax_y, ylabel='Y')
if fname:
fig.savefig(fname)
