def peak_shape_qc(window_values, quality_flags):
"""
Apply a QC on the peak shape to determine if it is of an acceptable shape
:param window_values:
:param quality_flags:
:return:
"""
first_slopes = []
second_slopes = []
for x in window_values:
median = npmedian(x)
normalised = [y / median for y in x]
fit = polyfit(range(len(x)), normalised, 2)
first_slopes.append(fit[0])
second_slopes.append(fit[1])
for i, x in enumerate(first_slopes):
if abs(x) > 0.005:
quality_flags[i] = 5
elif abs(x) > 0.005 and quality_flags[i] == 5:
quality_flags[i] = 1
elif abs(second_slopes[i]) > 0.009:
quality_flags[i] = 5
elif abs(second_slopes[i]) < 0.009 and quality_flags[i] == 5:
quality_flags[i] = 1
for i, x in enumerate(window_values):
if npmedian(x) < 3800:
quality_flags[i] = 1
return quality_flags
def lightcurve_moments(ftimes, fmags, ferrs):
'''This calculates the weighted mean, stdev, median, MAD, percentiles, skew,
kurtosis, fraction of LC beyond 1-stdev, and IQR.
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
Returns
-------
dict
A dict with all of the light curve moments calculated.
'''
ndet = len(fmags)
if ndet > 9:
# now calculate the various things we need
series_median = npmedian(fmags)
series_wmean = (npsum(fmags * (1.0 / (ferrs * ferrs))) /
npsum(1.0 / (ferrs * ferrs)))
series_mad = npmedian(npabs(fmags - series_median))
series_stdev = 1.483 * series_mad
series_skew = spskew(fmags)
series_kurtosis = spkurtosis(fmags)
# get the beyond1std fraction
series_above1std = len(fmags[fmags > (series_median + series_stdev)])
series_below1std = len(fmags[fmags < (series_median - series_stdev)])
# this is the fraction beyond 1 stdev
series_beyond1std = (series_above1std + series_below1std) / float(ndet)
# get the magnitude percentiles
series_mag_percentiles = nppercentile(
fmags, [5.0, 10, 17.5, 25, 32.5, 40, 60, 67.5, 75, 82.5, 90, 95])
return {
'median': series_median,
'wmean': series_wmean,
'mad': series_mad,
'stdev': series_stdev,
'skew': series_skew,
'kurtosis': series_kurtosis,
'beyond1std': series_beyond1std,
'mag_percentiles': series_mag_percentiles,
'mag_iqr': series_mag_percentiles[8] - series_mag_percentiles[3],
}
else:
LOGERROR('not enough detections in this magseries '
'to calculate light curve moments')
return None
def stetson_jindex(ftimes, fmags, ferrs, weightbytimediff=False):
'''This calculates the Stetson index for the magseries, based on consecutive
pairs of observations. Based on Nicole Loncke's work for her Planets and
Life certificate at Princeton.
This requires finite times, mags, and errs.
If weightbytimediff is True, the Stetson index for any pair of mags will be
reweighted by the difference in times between them using the scheme in
Fruth+ 2012 and Zhange+ 2003 (as seen in Sokolovsky+ 2017).
w_i = exp(- (t_i+1 - t_i)/ delta_t )
'''
ndet = len(fmags)
if ndet > 9:
# get the median and ndet
medmag = npmedian(fmags)
# get the stetson index elements
delta_prefactor = (ndet / (ndet - 1))
sigma_i = delta_prefactor * (fmags - medmag) / ferrs
sigma_j = nproll(sigma_i,
1) # Nicole's clever trick to advance indices
# by 1 and do x_i*x_(i+1)
if weightbytimediff:
time_i = ftimes
time_j = nproll(ftimes, 1)
difft = npdiff(ftimes)
deltat = npmedian(difft)
weights_i = npexp(-difft / deltat)
products = (weights_i * sigma_i[1:] * sigma_j[1:])
else:
# ignore first elem since it's actually x_0*x_n
products = (sigma_i * sigma_j)[1:]
stetsonj = (npsum(npsign(products) * npsqrt(npabs(products)))) / ndet
return stetsonj
else:
LOGERROR('not enough detections in this magseries '
'to calculate stetson J index')
return npnan
def stetson_kindex(fmags, ferrs):
'''
This calculates the Stetson K index (robust measure of the kurtosis).
Requires finite mags and errs.
'''
# use a fill in value for the errors if they're none
if ferrs is None:
ferrs = npfull_like(mags, 0.005)
ndet = len(fmags)
if ndet > 9:
# get the median and ndet
medmag = npmedian(fmags)
# get the stetson index elements
delta_prefactor = (ndet / (ndet - 1))
sigma_i = delta_prefactor * (fmags - medmag) / ferrs
stetsonk = (npsum(npabs(sigma_i)) /
(npsqrt(npsum(sigma_i * sigma_i))) * (ndet**(-0.5)))
return stetsonk
else:
LOGERROR('not enough detections in this magseries '
'to calculate stetson K index')
return npnan
def median(numbers):
if numpy:
return npmedian(numbers)
elif py3statistics:
return p3median(numbers)
quotient, remainder = divmod(len(numbers), 2)
if remainder:
return sorted(numbers)[quotient]
return sum(sorted(numbers)[quotient - 1:quotient + 1]) / 2.
def median_along_line(critical_points, line):
v = list()
for p in critical_points:
v.append(line.direction_value_on_point(p))
med_value = npmedian(array(v))
codirect = line.direc
perp_line = Line2D(None, None,
coefs=[codirect.get_x(), codirect.get_y(), med_value])
result = perp_line.intersection(line)
assert (result is not None)
assert (line.is_point_on_line(result))
return result
def pwd_phasebin(phases, mags, binsize=0.002, minbin=9):
'''
This bins the phased mag series using the given binsize.
'''
bins = np.arange(0.0, 1.0, binsize)
binnedphaseinds = npdigitize(phases, bins)
binnedphases, binnedmags = [], []
for x in npunique(binnedphaseinds):
thisbin_inds = binnedphaseinds == x
thisbin_phases = phases[thisbin_inds]
thisbin_mags = mags[thisbin_inds]
if thisbin_inds.size > minbin:
binnedphases.append(npmedian(thisbin_phases))
binnedmags.append(npmedian(thisbin_mags))
return np.array(binnedphases), np.array(binnedmags)
def window_medians(window_values):
"""
Calculates the window medians for each peak window
:param window_values:
:return: window_medians
"""
window_medians_temp_array = nparray(window_values)
window_medians_temp_array = npmedian(window_medians_temp_array, axis=1)
window_medians = list(window_medians_temp_array)
return window_medians
def _fourier_func(fourierparams, phase, mags):
'''This returns a summed Fourier cosine series.
Parameters
----------
fourierparams : list
This MUST be a list of the following form like so::
[period,
epoch,
[amplitude_1, amplitude_2, amplitude_3, ..., amplitude_X],
[phase_1, phase_2, phase_3, ..., phase_X]]
where X is the Fourier order.
phase,mags : np.array
The input phase and magnitude areas to use as the basis for the cosine
series. The phases are used directly to generate the values of the
function, while the mags array is used to generate the zeroth order
amplitude coefficient.
Returns
-------
np.array
The Fourier cosine series function evaluated over `phase`.
'''
# figure out the order from the length of the Fourier param list
order = int(len(fourierparams) / 2)
# get the amplitude and phase coefficients
f_amp = fourierparams[:order]
f_pha = fourierparams[order:]
# calculate all the individual terms of the series
f_orders = [
f_amp[x] * npcos(2.0 * pi_value * x * phase + f_pha[x])
for x in range(order)
]
# this is the zeroth order coefficient - a constant equal to median mag
total_f = npmedian(mags)
# sum the series
for fo in f_orders:
total_f += fo
return total_f
def stetson_kindex(fmags, ferrs):
'''This calculates the Stetson K index (a robust measure of the kurtosis).
Parameters
----------
fmags,ferrs : np.array
The input mag/flux time-series to process. Must have no non-finite
elems.
Returns
-------
float
The Stetson K variability index.
'''
# use a fill in value for the errors if they're none
if ferrs is None:
ferrs = npfull_like(fmags, 0.005)
ndet = len(fmags)
if ndet > 9:
# get the median and ndet
medmag = npmedian(fmags)
# get the stetson index elements
delta_prefactor = (ndet/(ndet - 1))
sigma_i = delta_prefactor*(fmags - medmag)/ferrs
stetsonk = (
npsum(npabs(sigma_i))/(npsqrt(npsum(sigma_i*sigma_i))) *
(ndet**(-0.5))
)
return stetsonk
else:
LOGERROR('not enough detections in this magseries '
'to calculate stetson K index')
return npnan
def sine_series_sum(fourierparams, times, mags, errs):
'''This generates a sinusoidal light curve using a sine series.
The series is generated using the coefficients provided in
fourierparams. This is a sequence like so:
[period,
epoch,
[ampl_1, ampl_2, ampl_3, ..., ampl_X],
[pha_1, pha_2, pha_3, ..., pha_X]]
where X is the Fourier order.
'''
period, epoch, famps, fphases = fourierparams
# figure out the order from the length of the Fourier param list
forder = len(famps)
# phase the times with this period
iphase = (times - epoch) / period
iphase = iphase - npfloor(iphase)
phasesortind = npargsort(iphase)
phase = iphase[phasesortind]
ptimes = times[phasesortind]
pmags = mags[phasesortind]
perrs = errs[phasesortind]
# calculate all the individual terms of the series
fseries = [
famps[x] * npsin(2.0 * MPI * x * phase + fphases[x])
for x in range(forder)
]
# this is the zeroth order coefficient - a constant equal to median mag
modelmags = npmedian(mags)
# sum the series
for fo in fseries:
modelmags += fo
return modelmags, phase, ptimes, pmags, perrs
def get_median_bisector(angle_to_bisector_map):
"""
:type angle_to_bisector_map: dict
:rtype : Line2D
"""
med_angle = npmedian(array(angle_to_bisector_map.keys()))
if len(angle_to_bisector_map) % 2 == 1:
bisector = angle_to_bisector_map[med_angle]
return bisector
else:
angle_below = max([a for a in angle_to_bisector_map.keys() if a < med_angle])
angle_above = min([a for a in angle_to_bisector_map.keys() if a > med_angle])
line_below = angle_to_bisector_map[angle_below]
line_above = angle_to_bisector_map[angle_above]
def invert_if_x_less_zero(vector):
return vector.inverted() if vector.get_x() < 0.0 else vector
direction_below = invert_if_x_less_zero(line_below.direc.normalized())
direction_above = invert_if_x_less_zero(line_above.direc.normalized())
med_line_direction = direction_above.sum(direction_below)
return Line2D(point1=Vector2D(0.0, 0.0), point2=med_line_direction)
def _fourier_func(fourierparams, phase, mags):
'''
This returns a summed Fourier series generated using fourierparams.
fourierparams is a sequence like so:
[ampl_1, ampl_2, ampl_3, ..., ampl_X,
pha_1, pha_2, pha_3, ..., pha_X]
where X is the Fourier order.
mags and phase MUST NOT have any nans.
'''
# figure out the order from the length of the Fourier param list
order = int(len(fourierparams) / 2)
# get the amplitude and phase coefficients
f_amp = fourierparams[:order]
f_pha = fourierparams[order:]
# calculate all the individual terms of the series
f_orders = [
f_amp[x] * npcos(2.0 * MPI * x * phase + f_pha[x])
for x in range(order)
]
# this is the zeroth order coefficient - a constant equal to median mag
total_f = npmedian(mags)
# sum the series
for fo in f_orders:
total_f += fo
return total_f
def epd_magseries(times, mags, errs,
fsv, fdv, fkv, xcc, ycc, bgv, bge, iha, izd,
magsarefluxes=False,
epdsmooth_sigclip=3.0,
epdsmooth_windowsize=21,
epdsmooth_func=smooth_magseries_savgol,
epdsmooth_extraparams=None):
'''Detrends a magnitude series using External Parameter Decorrelation.
The HAT light-curve-specific external parameters are:
S: the 'fsv' column,
D: the 'fdv' column,
K: the 'fkv' column,
x coords: the 'xcc' column,
y coords: the 'ycc' column,
background: the 'bgv' column,
background error: the 'bge' column
hour angle: the 'iha' column
zenith distance: the 'izd' column
epdsmooth_windowsize is the number of points to smooth over to generate a
smoothed light curve to train the regressor against.
epdsmooth_func sets the smoothing filter function to use. A Savitsky-Golay
filter is used to smooth the light curve by default.
epdsmooth_extraparams is a dict of any extra filter params to supply to the
smoothing function.
NOTE: The errs are completely ignored and returned unchanged (except for
sigclip and finite filtering).
'''
finind = np.isfinite(times) & np.isfinite(mags) & np.isfinite(errs)
ftimes, fmags, ferrs = times[::][finind], mags[::][finind], errs[::][finind]
ffsv, ffdv, ffkv, fxcc, fycc, fbgv, fbge, fiha, fizd = (
fsv[::][finind],
fdv[::][finind],
fkv[::][finind],
xcc[::][finind],
ycc[::][finind],
bgv[::][finind],
bge[::][finind],
iha[::][finind],
izd[::][finind],
)
stimes, smags, serrs, separams = sigclip_magseries_with_extparams(
times, mags, errs,
[fsv, fdv, fkv, xcc, ycc, bgv, bge, iha, izd],
sigclip=epdsmooth_sigclip,
magsarefluxes=magsarefluxes
)
sfsv, sfdv, sfkv, sxcc, sycc, sbgv, sbge, siha, sizd = separams
# smooth the signal
if isinstance(epdsmooth_extraparams, dict):
smoothedmags = epdsmooth_func(smags,
epdsmooth_windowsize,
**epdsmooth_extraparams)
else:
smoothedmags = epdsmooth_func(smags, epdsmooth_windowsize)
# initial fit coeffs
initcoeffs = np.zeros(22)
# fit the smoothed mags and find the EPD function coefficients
leastsqfit = leastsq(_epd_residual,
initcoeffs,
args=(smoothedmags,
sfsv, sfdv, sfkv, sxcc,
sycc, sbgv, sbge, siha, sizd),
full_output=True)
# if the fit succeeds, then get the EPD mags
if leastsqfit[-1] in (1,2,3,4):
fitcoeffs = leastsqfit[0]
epdfit = _epd_function(fitcoeffs,
ffsv, ffdv, ffkv, fxcc, fycc,
fbgv, fbge, fiha, fizd)
epdmags = npmedian(fmags) + fmags - epdfit
retdict = {'times':ftimes,
'mags':epdmags,
'errs':ferrs,
'fitcoeffs':fitcoeffs,
'fitinfo':leastsqfit,
'fitmags':epdfit}
return retdict
# if the solution fails, return nothing
else:
LOGERROR('EPD fit did not converge')
return None
def plot_phased_mag_series(times,
mags,
period,
errs=None,
epoch='min',
outfile=None,
sigclip=30.0,
phasewrap=True,
phasesort=True,
phasebin=None,
plotphaselim=[-0.8,0.8],
yrange=None):
'''This plots a phased magnitude time series using the period provided.
If epoch is None, uses the min(times) as the epoch.
If epoch is a string 'min', then fits a cubic spline to the phased light
curve using min(times), finds the magnitude minimum from the fitted light
curve, then uses the corresponding time value as the epoch.
If epoch is a float, then uses that directly to phase the light curve and as
the epoch of the phased mag series plot.
If outfile is none, then plots to matplotlib interactive window. If outfile
is a string denoting a filename, uses that to write a png/eps/pdf figure.
'''
if errs is not None:
# remove nans
find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
ftimes, fmags, ferrs = times[find], mags[find], errs[find]
# get the median and stdev = 1.483 x MAD
median_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483
# sigclip next
if sigclip:
sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
LOGINFO('sigclip = %s: before = %s observations, '
'after = %s observations' %
(sigclip, len(times), len(stimes)))
else:
stimes = ftimes
smags = fmags
serrs = ferrs
else:
# remove nans
find = npisfinite(times) & npisfinite(mags)
ftimes, fmags, ferrs = times[find], mags[find], None
# get the median and stdev = 1.483 x MAD
median_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483
# sigclip next
if sigclip:
sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = None
LOGINFO('sigclip = %s: before = %s observations, '
'after = %s observations' %
(sigclip, len(times), len(stimes)))
else:
stimes = ftimes
smags = fmags
serrs = None
# figure out the epoch, if it's None, use the min of the time
if epoch is None:
epoch = npmin(stimes)
# if the epoch is 'min', then fit a spline to the light curve phased
# using the min of the time, find the fit mag minimum and use the time for
# that as the epoch
elif isinstance(epoch,str) and epoch == 'min':
spfit = spline_fit_magseries(stimes, smags, serrs, period)
epoch = spfit['fitepoch']
# now phase (and optionally, phase bin the light curve)
if errs is not None:
# phase the magseries
phasedlc = phase_magseries_with_errs(stimes,
smags,
serrs,
period,
epoch,
wrap=phasewrap,
sort=phasesort)
plotphase = phasedlc['phase']
plotmags = phasedlc['mags']
ploterrs = phasedlc['errs']
# if we're supposed to bin the phases, do so
if phasebin:
binphasedlc = phase_bin_magseries_with_errs(plotphase,
plotmags,
ploterrs,
binsize=phasebin)
plotphase = binphasedlc['binnedphases']
plotmags = binphasedlc['binnedmags']
ploterrs = binphasedlc['binnederrs']
else:
# phase the magseries
phasedlc = phase_magseries(stimes,
smags,
period,
epoch,
wrap=phasewrap,
sort=phasesort)
plotphase = phasedlc['phase']
plotmags = phasedlc['mags']
ploterrs = None
# if we're supposed to bin the phases, do so
if phasebin:
binphasedlc = phase_bin_magseries(plotphase,
plotmags,
binsize=phasebin)
plotphase = binphasedlc['binnedphases']
plotmags = binphasedlc['binnedmags']
ploterrs = None
# finally, make the plots
# initialize the plot
fig = plt.figure()
fig.set_size_inches(7.5,4.8)
plt.errorbar(plotphase, plotmags, fmt='bo', yerr=ploterrs,
markersize=2.0, markeredgewidth=0.0, ecolor='#B2BEB5',
capsize=0)
# make a grid
plt.grid(color='#a9a9a9',
alpha=0.9,
zorder=0,
linewidth=1.0,
linestyle=':')
# make lines for phase 0.0, 0.5, and -0.5
plt.axvline(0.0,alpha=0.9,linestyle='dashed',color='g')
plt.axvline(-0.5,alpha=0.9,linestyle='dashed',color='g')
plt.axvline(0.5,alpha=0.9,linestyle='dashed',color='g')
# fix the ticks to use no offsets
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
# get the yrange
if yrange and isinstance(yrange,list) and len(yrange) == 2:
ymin, ymax = yrange
else:
ymin, ymax = plt.ylim()
plt.ylim(ymax,ymin)
# set the x axis limit
if not plotphaselim:
plot_xlim = plt.xlim()
plt.xlim((npmin(plotphase)-0.1,
npmax(plotphase)+0.1))
else:
plt.xlim((plotphaselim[0],plotphaselim[1]))
# set up the labels
plt.xlabel('phase')
plt.ylabel('magnitude')
plt.title('using period: %.6f d and epoch: %.6f' % (period, epoch))
# make the figure
if outfile and isinstance(outfile, str):
plt.savefig(outfile,bbox_inches='tight')
plt.close()
return os.path.abspath(outfile)
else:
plt.show()
plt.close()
return
def aov_theta(times, mags, errs, frequency,
binsize=0.05, minbin=9):
'''Calculates the Schwarzenberg-Czerny AoV statistic at a test frequency.
Parameters
----------
times,mags,errs : np.array
The input time-series and associated errors.
frequency : float
The test frequency to calculate the theta statistic at.
binsize : float
The phase bin size to use.
minbin : int
The minimum number of items in a phase bin to consider in the
calculation of the statistic.
Returns
-------
theta_aov : float
The value of the AoV statistic at the specified `frequency`.
'''
period = 1.0/frequency
fold_time = times[0]
phased = phase_magseries(times,
mags,
period,
fold_time,
wrap=False,
sort=True)
phases = phased['phase']
pmags = phased['mags']
bins = nparange(0.0, 1.0, binsize)
ndets = phases.size
binnedphaseinds = npdigitize(phases, bins)
bin_s1_tops = []
bin_s2_tops = []
binndets = []
goodbins = 0
all_xbar = npmedian(pmags)
for x in npunique(binnedphaseinds):
thisbin_inds = binnedphaseinds == x
thisbin_mags = pmags[thisbin_inds]
if thisbin_mags.size > minbin:
thisbin_ndet = thisbin_mags.size
thisbin_xbar = npmedian(thisbin_mags)
# get s1
thisbin_s1_top = (
thisbin_ndet *
(thisbin_xbar - all_xbar) *
(thisbin_xbar - all_xbar)
)
# get s2
thisbin_s2_top = npsum((thisbin_mags - all_xbar) *
(thisbin_mags - all_xbar))
bin_s1_tops.append(thisbin_s1_top)
bin_s2_tops.append(thisbin_s2_top)
binndets.append(thisbin_ndet)
goodbins = goodbins + 1
# turn the quantities into arrays
bin_s1_tops = nparray(bin_s1_tops)
bin_s2_tops = nparray(bin_s2_tops)
binndets = nparray(binndets)
# calculate s1 first
s1 = npsum(bin_s1_tops)/(goodbins - 1.0)
# then calculate s2
s2 = npsum(bin_s2_tops)/(ndets - goodbins)
theta_aov = s1/s2
return theta_aov
def gaussianeb_fit_magseries(
times,
mags,
errs,
ebparams,
param_bounds=None,
scale_errs_redchisq_unity=True,
sigclip=10.0,
plotfit=False,
magsarefluxes=False,
verbose=True,
curve_fit_kwargs=None,
):
'''This fits a double inverted gaussian EB model to a magnitude time series.
Parameters
----------
times,mags,errs : np.array
The input mag/flux time-series to fit the EB model to.
period : float
The period to use for EB fit.
ebparams : list of float
This is a list containing the eclipsing binary parameters::
ebparams = [period (time),
epoch (time),
pdepth (mags),
pduration (phase),
psdepthratio,
secondaryphase]
`period` is the period in days.
`epoch` is the time of primary minimum in JD.
`pdepth` is the depth of the primary eclipse:
- for magnitudes -> `pdepth` should be < 0
- for fluxes -> `pdepth` should be > 0
`pduration` is the length of the primary eclipse in phase.
`psdepthratio` is the ratio of the secondary eclipse depth to that of
the primary eclipse.
`secondaryphase` is the phase at which the minimum of the secondary
eclipse is located. This effectively parameterizes eccentricity.
If `epoch` is None, this function will do an initial spline fit to find
an approximate minimum of the phased light curve using the given period.
The `pdepth` provided is checked against the value of
`magsarefluxes`. if `magsarefluxes = True`, the `ebdepth` is forced to
be > 0; if `magsarefluxes = False`, the `ebdepth` is forced to be < 0.
param_bounds : dict or None
This is a dict of the upper and lower bounds on each fit
parameter. Should be of the form::
{'period': (lower_bound_period, upper_bound_period),
'epoch': (lower_bound_epoch, upper_bound_epoch),
'pdepth': (lower_bound_pdepth, upper_bound_pdepth),
'pduration': (lower_bound_pduration, upper_bound_pduration),
'psdepthratio': (lower_bound_psdepthratio,
upper_bound_psdepthratio),
'secondaryphase': (lower_bound_secondaryphase,
upper_bound_secondaryphase)}
- To indicate that a parameter is fixed, use 'fixed' instead of a tuple
providing its lower and upper bounds as tuple.
- To indicate that a parameter has no bounds, don't include it in the
param_bounds dict.
If this is None, the default value of this kwarg will be::
{'period':(0.0,np.inf), # period is between 0 and inf
'epoch':(0.0, np.inf), # epoch is between 0 and inf
'pdepth':(-np.inf,np.inf), # pdepth is between -np.inf and np.inf
'pduration':(0.0,1.0), # pduration is between 0.0 and 1.0
'psdepthratio':(0.0,1.0), # psdepthratio is between 0.0 and 1.0
'secondaryphase':(0.0,1.0), # secondaryphase is between 0.0 and 1.0
scale_errs_redchisq_unity : bool
If True, the standard errors on the fit parameters will be scaled to
make the reduced chi-sq = 1.0. This sets the ``absolute_sigma`` kwarg
for the ``scipy.optimize.curve_fit`` function to False.
sigclip : float or int or sequence of two floats/ints or None
If a single float or int, a symmetric sigma-clip will be performed using
the number provided as the sigma-multiplier to cut out from the input
time-series.
If a list of two ints/floats is provided, the function will perform an
'asymmetric' sigma-clip. The first element in this list is the sigma
value to use for fainter flux/mag values; the second element in this
list is the sigma value to use for brighter flux/mag values. For
example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
dimmings and greater than 3-sigma brightenings. Here the meaning of
"dimming" and "brightening" is set by *physics* (not the magnitude
system), which is why the `magsarefluxes` kwarg must be correctly set.
If `sigclip` is None, no sigma-clipping will be performed, and the
time-series (with non-finite elems removed) will be passed through to
the output.
magsarefluxes : bool
If True, will treat the input values of `mags` as fluxes for purposes of
plotting the fit and sig-clipping.
plotfit : str or False
If this is a string, this function will make a plot for the fit to the
mag/flux time-series and writes the plot to the path specified here.
ignoreinitfail : bool
If this is True, ignores the initial failure to find a set of optimized
Fourier parameters using the global optimization function and proceeds
to do a least-squares fit anyway.
verbose : bool
If True, will indicate progress and warn of any problems.
curve_fit_kwargs : dict or None
If not None, this should be a dict containing extra kwargs to pass to
the scipy.optimize.curve_fit function.
Returns
-------
dict
This function returns a dict containing the model fit parameters, the
minimized chi-sq value and the reduced chi-sq value. The form of this
dict is mostly standardized across all functions in this module::
{
'fittype':'gaussianeb',
'fitinfo':{
'initialparams':the initial EB params provided,
'finalparams':the final model fit EB params,
'finalparamerrs':formal errors in the params,
'fitmags': the model fit mags,
'fitepoch': the epoch of minimum light for the fit,
},
'fitchisq': the minimized value of the fit's chi-sq,
'fitredchisq':the reduced chi-sq value,
'fitplotfile': the output fit plot if fitplot is not None,
'magseries':{
'times':input times in phase order of the model,
'phase':the phases of the model mags,
'mags':input mags/fluxes in the phase order of the model,
'errs':errs in the phase order of the model,
'magsarefluxes':input value of magsarefluxes kwarg
}
}
'''
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
sigclip=sigclip,
magsarefluxes=magsarefluxes)
# get rid of zero errs
nzind = npnonzero(serrs)
stimes, smags, serrs = stimes[nzind], smags[nzind], serrs[nzind]
# check the ebparams
ebperiod, ebepoch, ebdepth = ebparams[0:3]
# check if we have a ebepoch to use
if ebepoch is None:
if verbose:
LOGWARNING('no ebepoch given in ebparams, '
'trying to figure it out automatically...')
# do a spline fit to figure out the approximate min of the LC
try:
spfit = spline_fit_magseries(times,
mags,
errs,
ebperiod,
sigclip=sigclip,
magsarefluxes=magsarefluxes,
verbose=verbose)
ebepoch = spfit['fitinfo']['fitepoch']
# if the spline-fit fails, try a savgol fit instead
except Exception:
sgfit = savgol_fit_magseries(times,
mags,
errs,
ebperiod,
sigclip=sigclip,
magsarefluxes=magsarefluxes,
verbose=verbose)
ebepoch = sgfit['fitinfo']['fitepoch']
# if everything failed, then bail out and ask for the ebepoch
finally:
if ebepoch is None:
LOGERROR("couldn't automatically figure out the eb epoch, "
"can't continue. please provide it in ebparams.")
# assemble the returndict
returndict = {
'fittype': 'gaussianeb',
'fitinfo': {
'initialparams': ebparams,
'finalparams': None,
'finalparamerrs': None,
'fitmags': None,
'fitepoch': None,
},
'fitchisq': npnan,
'fitredchisq': npnan,
'fitplotfile': None,
'magseries': {
'phase': None,
'times': None,
'mags': None,
'errs': None,
'magsarefluxes': magsarefluxes,
},
}
return returndict
else:
if ebepoch.size > 1:
if verbose:
LOGWARNING('could not auto-find a single minimum '
'for ebepoch, using the first one returned')
ebparams[1] = ebepoch[0]
else:
if verbose:
LOGWARNING(
'using automatically determined ebepoch = %.5f' %
ebepoch)
ebparams[1] = ebepoch.item()
# next, check the ebdepth and fix it to the form required
if magsarefluxes:
if ebdepth < 0.0:
ebparams[2] = -ebdepth[2]
else:
if ebdepth > 0.0:
ebparams[2] = -ebdepth[2]
# finally, do the fit
try:
# set up the fit parameter bounds
if param_bounds is None:
curvefit_bounds = (nparray([0.0, 0.0, -npinf, 0.0, 0.0, 0.0]),
nparray([npinf, npinf, npinf, 1.0, 1.0, 1.0]))
fitfunc_fixed = {}
else:
# figure out the bounds
lower_bounds = []
upper_bounds = []
fitfunc_fixed = {}
for ind, key in enumerate(
('period', 'epoch', 'pdepth', 'pduration', 'psdepthratio',
'secondaryphase')):
# handle fixed parameters
if (key in param_bounds and isinstance(param_bounds[key], str)
and param_bounds[key] == 'fixed'):
lower_bounds.append(ebparams[ind] - 1.0e-7)
upper_bounds.append(ebparams[ind] + 1.0e-7)
fitfunc_fixed[key] = ebparams[ind]
# handle parameters with lower and upper bounds
elif key in param_bounds and isinstance(
param_bounds[key], (tuple, list)):
lower_bounds.append(param_bounds[key][0])
upper_bounds.append(param_bounds[key][1])
# handle no parameter bounds
else:
lower_bounds.append(-npinf)
upper_bounds.append(npinf)
# generate the bounds sequence in the required format
curvefit_bounds = (nparray(lower_bounds), nparray(upper_bounds))
#
# set up the curve fit function
#
curvefit_func = partial(eclipses.invgauss_eclipses_curvefit_func,
zerolevel=npmedian(smags),
fixed_params=fitfunc_fixed)
#
# run the fit
#
if curve_fit_kwargs is not None:
finalparams, covmatrix = curve_fit(
curvefit_func,
stimes,
smags,
p0=ebparams,
sigma=serrs,
bounds=curvefit_bounds,
absolute_sigma=(not scale_errs_redchisq_unity),
**curve_fit_kwargs)
else:
finalparams, covmatrix = curve_fit(
curvefit_func,
stimes,
smags,
p0=ebparams,
sigma=serrs,
bounds=curvefit_bounds,
absolute_sigma=(not scale_errs_redchisq_unity),
)
except Exception:
LOGEXCEPTION("curve_fit returned an exception")
finalparams, covmatrix = None, None
# if the fit succeeded, then we can return the final parameters
if finalparams is not None and covmatrix is not None:
# calculate the chisq and reduced chisq
fitmags, phase, ptimes, pmags, perrs = eclipses.invgauss_eclipses_func(
finalparams, stimes, smags, serrs)
fitchisq = npsum(
((fitmags - pmags) * (fitmags - pmags)) / (perrs * perrs))
fitredchisq = fitchisq / (len(pmags) - len(finalparams) -
len(fitfunc_fixed))
stderrs = npsqrt(npdiag(covmatrix))
if verbose:
LOGINFO('final fit done. chisq = %.5f, reduced chisq = %.5f' %
(fitchisq, fitredchisq))
# get the fit epoch
fperiod, fepoch = finalparams[:2]
# assemble the returndict
returndict = {
'fittype': 'gaussianeb',
'fitinfo': {
'initialparams': ebparams,
'finalparams': finalparams,
'finalparamerrs': stderrs,
'fitmags': fitmags,
'fitepoch': fepoch,
},
'fitchisq': fitchisq,
'fitredchisq': fitredchisq,
'fitplotfile': None,
'magseries': {
'phase': phase,
'times': ptimes,
'mags': pmags,
'errs': perrs,
'magsarefluxes': magsarefluxes,
},
}
# make the fit plot if required
if plotfit and isinstance(plotfit, str):
make_fit_plot(phase,
pmags,
perrs,
fitmags,
fperiod,
ptimes.min(),
fepoch,
plotfit,
magsarefluxes=magsarefluxes)
returndict['fitplotfile'] = plotfit
return returndict
# if the leastsq fit failed, return nothing
else:
LOGERROR('eb-fit: least-squared fit to the light curve failed!')
# assemble the returndict
returndict = {
'fittype': 'gaussianeb',
'fitinfo': {
'initialparams': ebparams,
'finalparams': None,
'finalparamerrs': None,
'fitmags': None,
'fitepoch': None,
},
'fitchisq': npnan,
'fitredchisq': npnan,
'fitplotfile': None,
'magseries': {
'phase': None,
'times': None,
'mags': None,
'errs': None,
'magsarefluxes': magsarefluxes,
},
}
return returndict
def rfepd_magseries(times, mags, errs,
externalparam_arrs,
magsarefluxes=False,
epdsmooth=True,
epdsmooth_sigclip=3.0,
epdsmooth_windowsize=201,
epdsmooth_func=smooth_magseries_savgol,
epdsmooth_extraparams=None,
rf_subsample=1.0,
rf_ntrees=300,
rf_extraparams={'criterion':'mse',
'oob_score':False,
'n_jobs':-1}):
'''This uses a RandomForestRegressor to de-correlate the given magseries.
times, mags, errs are ndarrays of time and magnitude values to filter.
externalparam_arrs is a list of ndarrays of external parameters to
decorrelate against. These should all be the same size as times, mags, errs.
epdsmooth = True sets the training light curve to be a smoothed version of
the sigma-clipped light curve.
epdsmooth_windowsize is the number of points to smooth over to generate a
smoothed light curve to train the regressor against.
epdsmooth_func sets the smoothing filter function to use. A Savitsky-Golay
filter is used to smooth the light curve by default.
epdsmooth_extraparams is a dict of any extra filter params to supply to the
smoothing function.
rf_subsample is the fraction of the size of the mags array to use for
training the random forest regressor.
rf_ntrees is the number of trees to use for the RandomForestRegressor.
rf_extraparams is any extra params to provide to the RandomForestRegressor
instance as a dict.
Returns a dict with decorrelated mags and the usual info from the
RandomForestRegressor: variable importances, etc.
'''
# get finite times, mags, errs
finind = np.isfinite(times) & np.isfinite(mags) & np.isfinite(errs)
ftimes, fmags, ferrs = times[::][finind], mags[::][finind], errs[::][finind]
finalparam_arrs = []
for ep in externalparam_arrs:
finalparam_arrs.append(ep[::][finind])
stimes, smags, serrs, eparams = sigclip_magseries_with_extparams(
times, mags, errs,
externalparam_arrs,
sigclip=epdsmooth_sigclip,
magsarefluxes=magsarefluxes
)
# smoothing is optional for RFR because we train on a fraction of the mag
# series and so should not require a smoothed input to fit a function to
if epdsmooth:
# smooth the signal
if isinstance(epdsmooth_extraparams, dict):
smoothedmags = epdsmooth_func(smags,
epdsmooth_windowsize,
**epdsmooth_extraparams)
else:
smoothedmags = epdsmooth_func(smags,
epdsmooth_windowsize)
else:
smoothedmags = smags
# set up the regressor
if isinstance(rf_extraparams, dict):
RFR = RandomForestRegressor(n_estimators=rf_ntrees,
**rf_extraparams)
else:
RFR = RandomForestRegressor(n_estimators=rf_ntrees)
# collect the features
features = np.column_stack(eparams)
# fit, then generate the predicted values, then get corrected values
# we fit on a randomly selected subsample of all the mags
if rf_subsample < 1.0:
featureindices = np.arange(smoothedmags.size)
# these are sorted because time-order should be important
training_indices = np.sort(
npr.choice(featureindices,
size=int(rf_subsample*smoothedmags.size),
replace=False)
)
else:
training_indices = np.arange(smoothedmags.size)
RFR.fit(features[training_indices,:], smoothedmags[training_indices])
# predict on the full feature set
flux_corrections = RFR.predict(np.column_stack(finalparam_arrs))
corrected_fmags = npmedian(fmags) + fmags - flux_corrections
retdict = {'times':ftimes,
'mags':corrected_fmags,
'errs':ferrs,
'feature_importances':RFR.feature_importances_,
'regressor':RFR}
return retdict
def aov_periodfind(times,
mags,
errs,
magsarefluxes=False,
startp=None,
endp=None,
stepsize=1.0e-4,
autofreq=True,
normalize=True,
phasebinsize=0.05,
mindetperbin=9,
nbestpeaks=5,
periodepsilon=0.1,
sigclip=10.0,
nworkers=None,
verbose=True):
'''This runs a parallelized Analysis-of-Variance (AoV) period search.
NOTE: `normalize = True` here as recommended by Schwarzenberg-Czerny 1996,
i.e. mags will be normalized to zero and rescaled so their variance = 1.0.
Parameters
----------
times,mags,errs : np.array
The mag/flux time-series with associated measurement errors to run the
period-finding on.
magsarefluxes : bool
If the input measurement values in `mags` and `errs` are in fluxes, set
this to True.
startp,endp : float or None
The minimum and maximum periods to consider for the transit search.
stepsize : float
The step-size in frequency to use when constructing a frequency grid for
the period search.
autofreq : bool
If this is True, the value of `stepsize` will be ignored and the
:py:func:`astrobase.periodbase.get_frequency_grid` function will be used
to generate a frequency grid based on `startp`, and `endp`. If these are
None as well, `startp` will be set to 0.1 and `endp` will be set to
`times.max() - times.min()`.
normalize : bool
This sets if the input time-series is normalized to 0.0 and rescaled
such that its variance = 1.0. This is the recommended procedure by
Schwarzenberg-Czerny 1996.
phasebinsize : float
The bin size in phase to use when calculating the AoV theta statistic at
a test frequency.
mindetperbin : int
The minimum number of elements in a phase bin to consider it valid when
calculating the AoV theta statistic at a test frequency.
nbestpeaks : int
The number of 'best' peaks to return from the periodogram results,
starting from the global maximum of the periodogram peak values.
periodepsilon : float
The fractional difference between successive values of 'best' periods
when sorting by periodogram power to consider them as separate periods
(as opposed to part of the same periodogram peak). This is used to avoid
broad peaks in the periodogram and make sure the 'best' periods returned
are all actually independent.
sigclip : float or int or sequence of two floats/ints or None
If a single float or int, a symmetric sigma-clip will be performed using
the number provided as the sigma-multiplier to cut out from the input
time-series.
If a list of two ints/floats is provided, the function will perform an
'asymmetric' sigma-clip. The first element in this list is the sigma
value to use for fainter flux/mag values; the second element in this
list is the sigma value to use for brighter flux/mag values. For
example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
dimmings and greater than 3-sigma brightenings. Here the meaning of
"dimming" and "brightening" is set by *physics* (not the magnitude
system), which is why the `magsarefluxes` kwarg must be correctly set.
If `sigclip` is None, no sigma-clipping will be performed, and the
time-series (with non-finite elems removed) will be passed through to
the output.
nworkers : int
The number of parallel workers to use when calculating the periodogram.
verbose : bool
If this is True, will indicate progress and details about the frequency
grid used for the period search.
Returns
-------
dict
This function returns a dict, referred to as an `lspinfo` dict in other
astrobase functions that operate on periodogram results. This is a
standardized format across all astrobase period-finders, and is of the
form below::
{'bestperiod': the best period value in the periodogram,
'bestlspval': the periodogram peak associated with the best period,
'nbestpeaks': the input value of nbestpeaks,
'nbestlspvals': nbestpeaks-size list of best period peak values,
'nbestperiods': nbestpeaks-size list of best periods,
'lspvals': the full array of periodogram powers,
'periods': the full array of periods considered,
'method':'aov' -> the name of the period-finder method,
'kwargs':{ dict of all of the input kwargs for record-keeping}}
'''
# get rid of nans first and sigclip
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
magsarefluxes=magsarefluxes,
sigclip=sigclip)
# make sure there are enough points to calculate a spectrum
if len(stimes) > 9 and len(smags) > 9 and len(serrs) > 9:
# get the frequencies to use
if startp:
endf = 1.0/startp
else:
# default start period is 0.1 day
endf = 1.0/0.1
if endp:
startf = 1.0/endp
else:
# default end period is length of time series
startf = 1.0/(stimes.max() - stimes.min())
# if we're not using autofreq, then use the provided frequencies
if not autofreq:
frequencies = nparange(startf, endf, stepsize)
if verbose:
LOGINFO(
'using %s frequency points, start P = %.3f, end P = %.3f' %
(frequencies.size, 1.0/endf, 1.0/startf)
)
else:
# this gets an automatic grid of frequencies to use
frequencies = get_frequency_grid(stimes,
minfreq=startf,
maxfreq=endf)
if verbose:
LOGINFO(
'using autofreq with %s frequency points, '
'start P = %.3f, end P = %.3f' %
(frequencies.size,
1.0/frequencies.max(),
1.0/frequencies.min())
)
# map to parallel workers
if (not nworkers) or (nworkers > NCPUS):
nworkers = NCPUS
if verbose:
LOGINFO('using %s workers...' % nworkers)
pool = Pool(nworkers)
# renormalize the working mags to zero and scale them so that the
# variance = 1 for use with our LSP functions
if normalize:
nmags = (smags - npmedian(smags))/npstd(smags)
else:
nmags = smags
tasks = [(stimes, nmags, serrs, x, phasebinsize, mindetperbin)
for x in frequencies]
lsp = pool.map(_aov_worker, tasks)
pool.close()
pool.join()
del pool
lsp = nparray(lsp)
periods = 1.0/frequencies
# find the nbestpeaks for the periodogram: 1. sort the lsp array by
# highest value first 2. go down the values until we find five
# values that are separated by at least periodepsilon in period
# make sure to filter out non-finite values
finitepeakind = npisfinite(lsp)
finlsp = lsp[finitepeakind]
finperiods = periods[finitepeakind]
# make sure that finlsp has finite values before we work on it
try:
bestperiodind = npargmax(finlsp)
except ValueError:
LOGERROR('no finite periodogram values '
'for this mag series, skipping...')
return {'bestperiod':npnan,
'bestlspval':npnan,
'nbestpeaks':nbestpeaks,
'nbestlspvals':None,
'nbestperiods':None,
'lspvals':None,
'periods':None,
'method':'aov',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
sortedlspind = npargsort(finlsp)[::-1]
sortedlspperiods = finperiods[sortedlspind]
sortedlspvals = finlsp[sortedlspind]
# now get the nbestpeaks
nbestperiods, nbestlspvals, peakcount = (
[finperiods[bestperiodind]],
[finlsp[bestperiodind]],
1
)
prevperiod = sortedlspperiods[0]
# find the best nbestpeaks in the lsp and their periods
for period, lspval in zip(sortedlspperiods, sortedlspvals):
if peakcount == nbestpeaks:
break
perioddiff = abs(period - prevperiod)
bestperiodsdiff = [abs(period - x) for x in nbestperiods]
# print('prevperiod = %s, thisperiod = %s, '
# 'perioddiff = %s, peakcount = %s' %
# (prevperiod, period, perioddiff, peakcount))
# this ensures that this period is different from the last
# period and from all the other existing best periods by
# periodepsilon to make sure we jump to an entire different peak
# in the periodogram
if (perioddiff > (periodepsilon*prevperiod) and
all(x > (periodepsilon*period) for x in bestperiodsdiff)):
nbestperiods.append(period)
nbestlspvals.append(lspval)
peakcount = peakcount + 1
prevperiod = period
return {'bestperiod':finperiods[bestperiodind],
'bestlspval':finlsp[bestperiodind],
'nbestpeaks':nbestpeaks,
'nbestlspvals':nbestlspvals,
'nbestperiods':nbestperiods,
'lspvals':lsp,
'periods':periods,
'method':'aov',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
else:
LOGERROR('no good detections for these times and mags, skipping...')
return {'bestperiod':npnan,
'bestlspval':npnan,
'nbestpeaks':nbestpeaks,
'nbestlspvals':None,
'nbestperiods':None,
'lspvals':None,
'periods':None,
'method':'aov',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
def invgauss_eclipses_func(ebparams, times, mags, errs):
'''This returns a double eclipse shaped function.
Suitable for first order modeling of eclipsing binaries.
ebparams = [period (time),
epoch (time),
pdepth (mags),
pduration (phase),
psdepthratio,
secondaryphase]
period is the period in days
epoch is the time of minimum in JD
pdepth is the depth of the primary eclipse
- for magnitudes -> transitdepth should be < 0
- for fluxes -> transitdepth should be > 0
pduration is the length of the primary eclipse in phase
psdepthratio is the ratio in the eclipse depths:
depth_secondary/depth_primary. This is generally the same as the ratio of
the Teffs of the two stars.
secondaryphase is the phase at which the minimum of the secondary eclipse is
located. This effectively parameterizes eccentricity.
All of these will then have fitted values after the fit is done.
'''
(period, epoch, pdepth, pduration, depthratio, secondaryphase) = ebparams
# generate the phases
iphase = (times - epoch) / period
iphase = iphase - npfloor(iphase)
phasesortind = npargsort(iphase)
phase = iphase[phasesortind]
ptimes = times[phasesortind]
pmags = mags[phasesortind]
perrs = errs[phasesortind]
zerolevel = npmedian(pmags)
modelmags = npfull_like(phase, zerolevel)
primaryecl_amp = -pdepth
secondaryecl_amp = -pdepth * depthratio
primaryecl_std = pduration / 5.0 # we use 5-sigma as full-width -> duration
secondaryecl_std = pduration / 5.0 # secondary eclipse has the same duration
halfduration = pduration / 2.0
# phase indices
primary_eclipse_ingress = ((phase >=
(1.0 - halfduration)) & (phase <= 1.0))
primary_eclipse_egress = ((phase >= 0.0) & (phase <= halfduration))
secondary_eclipse_phase = ((phase >= (secondaryphase - halfduration)) &
(phase <= (secondaryphase + halfduration)))
# put in the eclipses
modelmags[primary_eclipse_ingress] = (zerolevel + _gaussian(
phase[primary_eclipse_ingress], primaryecl_amp, 1.0, primaryecl_std))
modelmags[primary_eclipse_egress] = (zerolevel + _gaussian(
phase[primary_eclipse_egress], primaryecl_amp, 0.0, primaryecl_std))
modelmags[secondary_eclipse_phase] = (
zerolevel + _gaussian(phase[secondary_eclipse_phase], secondaryecl_amp,
secondaryphase, secondaryecl_std))
return modelmags, phase, ptimes, pmags, perrs
def aov_theta(times, mags, errs, frequency, binsize=0.05, minbin=9):
'''Calculates the Schwarzenberg-Czerny AoV statistic at a test frequency.
'''
period = 1.0 / frequency
fold_time = times[0]
phased = phase_magseries(times,
mags,
period,
fold_time,
wrap=False,
sort=True)
phases = phased['phase']
pmags = phased['mags']
bins = np.arange(0.0, 1.0, binsize)
nbins = bins.size
ndets = phases.size
binnedphaseinds = npdigitize(phases, bins)
bin_s1_tops = []
bin_s2_tops = []
binndets = []
goodbins = 0
all_xbar = npmedian(pmags)
for x in npunique(binnedphaseinds):
thisbin_inds = binnedphaseinds == x
thisbin_phases = phases[thisbin_inds]
thisbin_mags = pmags[thisbin_inds]
if thisbin_mags.size > minbin:
thisbin_ndet = thisbin_mags.size
thisbin_xbar = npmedian(thisbin_mags)
# get s1
thisbin_s1_top = (thisbin_ndet * (thisbin_xbar - all_xbar) *
(thisbin_xbar - all_xbar))
# get s2
thisbin_s2_top = npsum(
(thisbin_mags - all_xbar) * (thisbin_mags - all_xbar))
bin_s1_tops.append(thisbin_s1_top)
bin_s2_tops.append(thisbin_s2_top)
binndets.append(thisbin_ndet)
goodbins = goodbins + 1
# turn the quantities into arrays
bin_s1_tops = nparray(bin_s1_tops)
bin_s2_tops = nparray(bin_s2_tops)
binndets = nparray(binndets)
# calculate s1 first
s1 = npsum(bin_s1_tops) / (goodbins - 1.0)
# then calculate s2
s2 = npsum(bin_s2_tops) / (ndets - goodbins)
theta_aov = s1 / s2
return theta_aov
def plot_mag_series(times,
mags,
errs=None,
outfile=None,
sigclip=30.0,
timebin=None,
yrange=None):
'''This plots a magnitude time series.
If outfile is none, then plots to matplotlib interactive window. If outfile
is a string denoting a filename, uses that to write a png/eps/pdf figure.
timebin is either a float indicating binsize in seconds, or None indicating
no time-binning is required.
'''
if errs is not None:
# remove nans
find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
ftimes, fmags, ferrs = times[find], mags[find], errs[find]
# get the median and stdev = 1.483 x MAD
median_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483
# sigclip next
if sigclip:
sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
LOGINFO('sigclip = %s: before = %s observations, '
'after = %s observations' %
(sigclip, len(times), len(stimes)))
else:
stimes = ftimes
smags = fmags
serrs = ferrs
else:
# remove nans
find = npisfinite(times) & npisfinite(mags)
ftimes, fmags, ferrs = times[find], mags[find], None
# get the median and stdev = 1.483 x MAD
median_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483
# sigclip next
if sigclip:
sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = None
LOGINFO('sigclip = %s: before = %s observations, '
'after = %s observations' %
(sigclip, len(times), len(stimes)))
else:
stimes = ftimes
smags = fmags
serrs = None
# now we proceed to binning
if timebin and errs is not None:
binned = time_bin_magseries_with_errs(stimes, smags, serrs,
binsize=timebin)
btimes, bmags, berrs = (binned['binnedtimes'],
binned['binnedmags'],
binned['binnederrs'])
elif timebin and errs is None:
binned = time_bin_magseries(stimes, smags,
binsize=timebin)
btimes, bmags, berrs = binned['binnedtimes'], binned['binnedmags'], None
else:
btimes, bmags, berrs = stimes, smags, serrs
# finally, proceed with plotting
fig = plt.figure()
fig.set_size_inches(7.5,4.8)
plt.errorbar(btimes, bmags, fmt='go', yerr=berrs,
markersize=2.0, markeredgewidth=0.0, ecolor='grey',
capsize=0)
# make a grid
plt.grid(color='#a9a9a9',
alpha=0.9,
zorder=0,
linewidth=1.0,
linestyle=':')
# fix the ticks to use no offsets
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
# get the yrange
if yrange and isinstance(yrange,list) and len(yrange) == 2:
ymin, ymax = yrange
else:
ymin, ymax = plt.ylim()
plt.ylim(ymax,ymin)
plt.xlim(npmin(btimes) - 0.001*npmin(btimes),
npmax(btimes) + 0.001*npmin(btimes))
plt.xlabel('time [JD]')
plt.ylabel('magnitude')
if outfile and isinstance(outfile, str):
plt.savefig(outfile,bbox_inches='tight')
plt.close()
return os.path.abspath(outfile)
else:
plt.show()
plt.close()
return
def lightcurve_ptp_measures(ftimes, fmags, ferrs):
'''This calculates various point-to-point measures (`eta` in Kim+ 2014).
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
Returns
-------
dict
A dict with values of the point-to-point measures, including the `eta`
variability index (often used as its inverse `inveta` to have the same
sense as increasing variability index -> more likely a variable star).
'''
ndet = len(fmags)
if ndet > 9:
timediffs = npdiff(ftimes)
# get rid of stuff with time diff = 0.0
nzind = npnonzero(timediffs)
ftimes, fmags, ferrs = ftimes[nzind], fmags[nzind], ferrs[nzind]
# recalculate ndet and diffs
ndet = ftimes.size
timediffs = npdiff(ftimes)
# calculate the point to point measures
p2p_abs_magdiffs = npabs(npdiff(fmags))
p2p_squared_magdiffs = npdiff(fmags) * npdiff(fmags)
robstd = npmedian(npabs(fmags - npmedian(fmags))) * 1.483
robvar = robstd * robstd
# these are eta from the Kim+ 2014 paper - ratio of point-to-point
# difference to the variance of the entire series
# this is the robust version
eta_robust = npmedian(p2p_abs_magdiffs) / robvar
eta_robust = eta_robust / (ndet - 1.0)
# this is the usual version
eta_normal = npsum(p2p_squared_magdiffs) / npvar(fmags)
eta_normal = eta_normal / (ndet - 1.0)
timeweights = 1.0 / (timediffs * timediffs)
# this is eta_e modified for uneven sampling from the Kim+ 2014 paper
eta_uneven_normal = ((npsum(timeweights * p2p_squared_magdiffs) /
(npvar(fmags) * npsum(timeweights))) *
npmean(timeweights) *
(ftimes.max() - ftimes.min()) *
(ftimes.max() - ftimes.min()))
# this is robust eta_e modified for uneven sampling from the Kim+ 2014
# paper
eta_uneven_robust = ((npsum(timeweights * p2p_abs_magdiffs) /
(robvar * npsum(timeweights))) *
npmedian(timeweights) * (ftimes[-1] - ftimes[0]) *
(ftimes[-1] - ftimes[0]))
return {
'eta_normal': eta_normal,
'eta_robust': eta_robust,
'eta_uneven_normal': eta_uneven_normal,
'eta_uneven_robust': eta_uneven_robust
}
else:
return None
def sigclip_magseries(times,
mags,
errs,
sigclip=None,
iterative=False,
niterations=None,
meanormedian='median',
magsarefluxes=False):
'''
Select the finite times, magnitudes (or fluxes), and errors from the
passed values, and apply symmetric or asymmetric sigma clipping to them.
Returns sigma-clipped times, mags, and errs.
Args:
times (np.array): ...
mags (np.array): numpy array to sigma-clip. Does not assume all values
are finite. Does not assume anything about whether they're
positive/negative.
errs (np.array): ...
iterative (bool): True if you want iterative sigma-clipping. If
niterations is not set and this is True, sigma-clipping is iterated
until no more points are removed.
niterations (int): maximum number of iterations to perform for
sigma-clipping. If None, the iterative arg takes precedence, and
iterative=True will sigma-clip until no more points are removed.
If niterations is not None and iterative is False, niterations takes
precedence and iteration will occur.
meanormedian (string): either 'mean' for sigma-clipping based on the
mean value, or 'median' for sigma-clipping based on the median value.
Default is 'median'.
magsarefluxes (bool): True if your "mags" are in fact fluxes, i.e. if
"dimming" corresponds to your "mags" getting smaller.
sigclip (float or list): If float, apply symmetric sigma clipping. If
list, e.g., [10., 3.], will sigclip out greater than 10-sigma dimmings
and greater than 3-sigma brightenings. Here the meaning of "dimming"
and "brightening" is set by *physics* (not the magnitude system), which
is why the `magsarefluxes` kwarg must be correctly set.
Returns:
stimes, smags, serrs: (sigmaclipped values of each).
'''
returnerrs = True
# fake the errors if they don't exist
# this is inconsequential to sigma-clipping
# we don't return these dummy values if the input errs are None
if errs is None:
# assume 0.1% errors if not given
# this should work for mags and fluxes
errs = 0.001 * mags
returnerrs = False
# filter the input times, mags, errs; do sigclipping and normalization
find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
ftimes, fmags, ferrs = times[find], mags[find], errs[find]
# get the center value and stdev
if meanormedian == 'median': # stddev = 1.483 x MAD
center_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - center_mag))) * 1.483
elif meanormedian == 'mean':
center_mag = npmean(fmags)
stddev_mag = npstddev(fmags)
else:
LOGWARNING("unrecognized meanormedian value given to "
"sigclip_magseries: %s, defaulting to 'median'" %
meanormedian)
meanormedian = 'median'
center_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - center_mag))) * 1.483
# sigclip next for a single sigclip value
if sigclip and isinstance(sigclip, (float, int)):
if not iterative and niterations is None:
sigind = (npabs(fmags - center_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
else:
#
# iterative version adapted from scipy.stats.sigmaclip
#
# First, if niterations is not set, iterate until covergence
if niterations is None:
delta = 1
this_times = ftimes
this_mags = fmags
this_errs = ferrs
while delta:
if meanormedian == 'mean':
this_center = npmean(this_mags)
this_stdev = npstddev(this_mags)
elif meanormedian == 'median':
this_center = npmedian(this_mags)
this_stdev = (npmedian(
npabs(this_mags - this_center))) * 1.483
this_size = this_mags.size
# apply the sigclip
tsi = ((npabs(this_mags - this_center)) <
(sigclip * this_stdev))
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# update delta and go to the top of the loop
delta = this_size - this_mags.size
else: # If iterating only a certain number of times
this_times = ftimes
this_mags = fmags
this_errs = ferrs
iter_num = 0
delta = 1
while iter_num < niterations and delta:
if meanormedian == 'mean':
this_center = npmean(this_mags)
this_stdev = npstddev(this_mags)
elif meanormedian == 'median':
this_center = npmedian(this_mags)
this_stdev = (npmedian(
npabs(this_mags - this_center))) * 1.483
this_size = this_mags.size
# apply the sigclip
tsi = ((npabs(this_mags - this_center)) <
(sigclip * this_stdev))
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# update the number of iterations and delta and
# go to the top of the loop
delta = this_size - this_mags.size
iter_num += 1
# final sigclipped versions
stimes, smags, serrs = this_times, this_mags, this_errs
# this handles sigclipping for asymmetric +ve and -ve clip values
elif sigclip and isinstance(sigclip, list) and len(sigclip) == 2:
# sigclip is passed as [dimmingclip, brighteningclip]
dimmingclip = sigclip[0]
brighteningclip = sigclip[1]
if not iterative and niterations is None:
if magsarefluxes:
nottoodimind = ((fmags - center_mag) >
(-dimmingclip * stddev_mag))
nottoobrightind = ((fmags - center_mag) <
(brighteningclip * stddev_mag))
else:
nottoodimind = ((fmags - center_mag) <
(dimmingclip * stddev_mag))
nottoobrightind = ((fmags - center_mag) >
(-brighteningclip * stddev_mag))
sigind = nottoodimind & nottoobrightind
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
else:
#
# iterative version adapted from scipy.stats.sigmaclip
#
if niterations is None:
delta = 1
this_times = ftimes
this_mags = fmags
this_errs = ferrs
while delta:
if meanormedian == 'mean':
this_center = npmean(this_mags)
this_stdev = npstddev(this_mags)
elif meanormedian == 'median':
this_center = npmedian(this_mags)
this_stdev = (npmedian(
npabs(this_mags - this_center))) * 1.483
this_size = this_mags.size
if magsarefluxes:
nottoodimind = ((this_mags - this_center) >
(-dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_center) <
(brighteningclip * this_stdev))
else:
nottoodimind = ((this_mags - this_center) <
(dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_center) >
(-brighteningclip * this_stdev))
# apply the sigclip
tsi = nottoodimind & nottoobrightind
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# update delta and go to top of the loop
delta = this_size - this_mags.size
else: # If iterating only a certain number of times
this_times = ftimes
this_mags = fmags
this_errs = ferrs
iter_num = 0
delta = 1
while iter_num < niterations and delta:
if meanormedian == 'mean':
this_center = npmean(this_mags)
this_stdev = npstddev(this_mags)
elif meanormedian == 'median':
this_center = npmedian(this_mags)
this_stdev = (npmedian(
npabs(this_mags - this_center))) * 1.483
this_size = this_mags.size
if magsarefluxes:
nottoodimind = ((this_mags - this_center) >
(-dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_center) <
(brighteningclip * this_stdev))
else:
nottoodimind = ((this_mags - this_center) <
(dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_center) >
(-brighteningclip * this_stdev))
# apply the sigclip
tsi = nottoodimind & nottoobrightind
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# update the number of iterations and delta
# and go to top of the loop
delta = this_size - this_mags.size
iter_num += 1
# final sigclipped versions
stimes, smags, serrs = this_times, this_mags, this_errs
else:
stimes = ftimes
smags = fmags
serrs = ferrs
if returnerrs:
return stimes, smags, serrs
else:
return stimes, smags, None
def extract_features(filename, is_url=False):
'''Extracts features to be used in text image classifier.
:param filename: input image
:param is_url: is input image a url or a file path on disk
:return: tuple of features:
(average_slope, median_slope, average_tilt, median_tilt, median_differences, average_differences, nr_straight_lines)
Most relevant ones are average_slope, average_differences and nr_straight_lines.
'''
if is_url:
filedata = urllib2.urlopen(filename).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
src = cv.DecodeImageM(imagefiledata, cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
src = cv.LoadImage(filename, cv.CV_LOAD_IMAGE_GRAYSCALE)
# normalize size
normalized_size = 400
# smaller dimension will be 400, longer dimension will be proportional
orig_size = cv.GetSize(src)
max_dim_idx = max(enumerate(orig_size), key=lambda l: l[1])[0]
min_dim_idx = [idx for idx in [0, 1] if idx != max_dim_idx][0]
new_size = [0, 0]
new_size[min_dim_idx] = normalized_size
new_size[max_dim_idx] = int(
float(orig_size[max_dim_idx]) / orig_size[min_dim_idx] *
normalized_size)
dst = cv.CreateImage(new_size, 8, 1)
cv.Resize(src, dst)
# cv.SaveImage("/tmp/resized.jpg",dst)
src = dst
dst = cv.CreateImage(cv.GetSize(src), 8, 1)
color_dst = cv.CreateImage(cv.GetSize(src), 8, 3)
storage = cv.CreateMemStorage(0)
cv.Canny(src, dst, 50, 200, 3)
cv.CvtColor(dst, color_dst, cv.CV_GRAY2BGR)
slopes = []
# difference between xs or ys - variant of slope
tilts = []
# x coordinates of horizontal lines
horizontals = []
# y coordinates of vertical lines
verticals = []
if USE_STANDARD:
coords = cv.HoughLines2(dst, storage, cv.CV_HOUGH_STANDARD, 1,
pi / 180, 50, 50, 10)
lines = []
for coord in coords:
(rho, theta) = coord
a = cos(theta)
b = sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (cv.Round(x0 + 1000 * (-b)), cv.Round(y0 + 1000 * (a)))
pt2 = (cv.Round(x0 - 1000 * (-b)), cv.Round(y0 - 1000 * (a)))
lines += [(pt1, pt2)]
else:
lines = cv.HoughLines2(dst, storage, cv.CV_HOUGH_PROBABILISTIC, 1,
pi / 180, 50, 50, 10)
# eliminate duplicates - there are many especially with the standard version
# first round the coordinates to integers divisible with 5 (to eliminate different but really close ones)
# TODO
# lines = list(set(map(lambda l: tuple([int(p) - int(p)%5 for p in l]), lines)))
nr_straight_lines = 0
for line in lines:
(pt1, pt2) = line
# compute slope, rotate the line so that the slope is smallest
# (slope is either delta x/ delta y or the reverse)
# add smoothing term in denominator in case of 0
slope = min(
abs(pt1[1] - pt2[1]),
(abs(pt1[0] - pt2[0]))) / (max(abs(pt1[1] - pt2[1]),
(abs(pt1[0] - pt2[0]))) + 0.01)
# if slope < 0.1:
# if slope < 5:
if slope < 0.05:
if abs(pt1[0] - pt2[0]) < abs(pt1[1] - pt2[1]):
# means it's a horizontal line
horizontals.append(pt1[0])
else:
verticals.append(pt1[1])
if slope < 0.05:
# if slope < 5:
# if slope < 0.1:
nr_straight_lines += 1
slopes.append(slope)
tilts.append(min(abs(pt1[1] - pt2[1]), (abs(pt1[0] - pt2[0]))))
# print slope
average_slope = sum(slopes) / float(len(slopes))
median_slope = npmedian(nparray(slopes))
average_tilt = sum(tilts) / float(len(tilts))
median_tilt = npmedian(nparray(tilts))
differences = []
horizontals = sorted(horizontals)
verticals = sorted(verticals)
print "x_differences:"
for (i, x) in enumerate(horizontals):
if i > 0:
# print abs(horizontals[i] - horizontals[i-1])
differences.append(abs(horizontals[i] - horizontals[i - 1]))
print "y_differences:"
for (i, y) in enumerate(verticals):
if i > 0:
# print abs(verticals[i] - verticals[i-1])
differences.append(abs(verticals[i] - verticals[i - 1]))
print filename
print "average_slope:", average_slope
print "median_slope:", median_slope
print "average_tilt:", average_tilt
print "median_tilt:", median_tilt
median_differences = npmedian(nparray(differences))
print "median_differences:", median_differences
if not differences:
# big random number for average difference
average_differences = 50
else:
average_differences = sum(differences) / float(len(differences))
print "average_differences:", average_differences
print "nr_lines:", nr_straight_lines
# print "sorted xs:", sorted(lines)
return (average_slope, median_slope, average_tilt, median_tilt,
median_differences, average_differences, nr_straight_lines)
def stellingwerf_pdm(times,
mags,
errs,
magsarefluxes=False,
autofreq=True,
startp=None,
endp=None,
normalize=False,
stepsize=1.0e-4,
phasebinsize=0.05,
mindetperbin=9,
nbestpeaks=5,
periodepsilon=0.1, # 0.1
sigclip=10.0,
nworkers=None,
verbose=True):
'''This runs a parallel Stellingwerf PDM period search.
'''
# get rid of nans first and sigclip
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
magsarefluxes=magsarefluxes,
sigclip=sigclip)
# make sure there are enough points to calculate a spectrum
if len(stimes) > 9 and len(smags) > 9 and len(serrs) > 9:
# get the frequencies to use
if startp:
endf = 1.0/startp
else:
# default start period is 0.1 day
endf = 1.0/0.1
if endp:
startf = 1.0/endp
else:
# default end period is length of time series
startf = 1.0/(stimes.max() - stimes.min())
# if we're not using autofreq, then use the provided frequencies
if not autofreq:
frequencies = np.arange(startf, endf, stepsize)
if verbose:
LOGINFO(
'using %s frequency points, start P = %.3f, end P = %.3f' %
(frequencies.size, 1.0/endf, 1.0/startf)
)
else:
# this gets an automatic grid of frequencies to use
frequencies = get_frequency_grid(stimes,
minfreq=startf,
maxfreq=endf)
if verbose:
LOGINFO(
'using autofreq with %s frequency points, '
'start P = %.3f, end P = %.3f' %
(frequencies.size,
1.0/frequencies.max(),
1.0/frequencies.min())
)
# map to parallel workers
if (not nworkers) or (nworkers > NCPUS):
nworkers = NCPUS
if verbose:
LOGINFO('using %s workers...' % nworkers)
pool = Pool(nworkers)
# renormalize the working mags to zero and scale them so that the
# variance = 1 for use with our LSP functions
if normalize:
nmags = (smags - npmedian(smags))/npstd(smags)
else:
nmags = smags
tasks = [(stimes, nmags, serrs, x, phasebinsize, mindetperbin)
for x in frequencies]
lsp = pool.map(stellingwerf_pdm_worker, tasks)
pool.close()
pool.join()
del pool
lsp = nparray(lsp)
periods = 1.0/frequencies
# find the nbestpeaks for the periodogram: 1. sort the lsp array by
# lowest value first 2. go down the values until we find five values
# that are separated by at least periodepsilon in period
# make sure to filter out the non-finite values of lsp
finitepeakind = npisfinite(lsp)
finlsp = lsp[finitepeakind]
finperiods = periods[finitepeakind]
# finlsp might not have any finite values if the period finding
# failed. if so, argmin will return a ValueError.
try:
bestperiodind = npargmin(finlsp)
except ValueError:
LOGERROR('no finite periodogram values for '
'this mag series, skipping...')
return {'bestperiod':npnan,
'bestlspval':npnan,
'nbestpeaks':nbestpeaks,
'nbestlspvals':None,
'nbestperiods':None,
'lspvals':None,
'periods':None,
'method':'pdm',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
sortedlspind = np.argsort(finlsp)
sortedlspperiods = finperiods[sortedlspind]
sortedlspvals = finlsp[sortedlspind]
prevbestlspval = sortedlspvals[0]
# now get the nbestpeaks
nbestperiods, nbestlspvals, peakcount = (
[finperiods[bestperiodind]],
[finlsp[bestperiodind]],
1
)
prevperiod = sortedlspperiods[0]
# find the best nbestpeaks in the lsp and their periods
for period, lspval in zip(sortedlspperiods, sortedlspvals):
if peakcount == nbestpeaks:
break
perioddiff = abs(period - prevperiod)
bestperiodsdiff = [abs(period - x) for x in nbestperiods]
# print('prevperiod = %s, thisperiod = %s, '
# 'perioddiff = %s, peakcount = %s' %
# (prevperiod, period, perioddiff, peakcount))
# this ensures that this period is different from the last
# period and from all the other existing best periods by
# periodepsilon to make sure we jump to an entire different peak
# in the periodogram
if (perioddiff > (periodepsilon*prevperiod) and
all(x > (periodepsilon*prevperiod) for x in bestperiodsdiff)):
nbestperiods.append(period)
nbestlspvals.append(lspval)
peakcount = peakcount + 1
prevperiod = period
return {'bestperiod':finperiods[bestperiodind],
'bestlspval':finlsp[bestperiodind],
'nbestpeaks':nbestpeaks,
'nbestlspvals':nbestlspvals,
'nbestperiods':nbestperiods,
'lspvals':lsp,
'periods':periods,
'method':'pdm',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
else:
LOGERROR('no good detections for these times and mags, skipping...')
return {'bestperiod':npnan,
'bestlspval':npnan,
'nbestpeaks':nbestpeaks,
'nbestlspvals':None,
'nbestperiods':None,
'lspvals':None,
'periods':None,
'method':'pdm',
'kwargs':{'startp':startp,
'endp':endp,
'stepsize':stepsize,
'normalize':normalize,
'phasebinsize':phasebinsize,
'mindetperbin':mindetperbin,
'autofreq':autofreq,
'periodepsilon':periodepsilon,
'nbestpeaks':nbestpeaks,
'sigclip':sigclip}}
def autocorr_magseries(times,
mags,
errs,
maxlags=1000,
func=_autocorr_func3,
fillgaps=0.0,
filterwindow=11,
forcetimebin=None,
sigclip=3.0,
magsarefluxes=False,
verbose=True):
'''This calculates the ACF of a light curve.
This will pre-process the light curve to fill in all the gaps and normalize
everything to zero. If `fillgaps = 'noiselevel'`, fills the gaps with the
noise level obtained via the procedure above. If `fillgaps = 'nan'`, fills
the gaps with `np.nan`.
Parameters
----------
times,mags,errs : np.array
The measurement time-series and associated errors.
maxlags : int
The maximum number of lags to calculate.
func : Python function
This is a function to calculate the lags.
fillgaps : 'noiselevel' or float
This sets what to use to fill in gaps in the time series. If this is
'noiselevel', will smooth the light curve using a point window size of
`filterwindow` (this should be an odd integer), subtract the smoothed LC
from the actual LC and estimate the RMS. This RMS will be used to fill
in the gaps. Other useful values here are 0.0, and npnan.
filterwindow : int
The light curve's smoothing filter window size to use if
`fillgaps='noiselevel`'.
forcetimebin : None or float
This is used to force a particular cadence in the light curve other than
the automatically determined cadence. This effectively rebins the light
curve to this cadence. This should be in the same time units as `times`.
sigclip : float or int or sequence of two floats/ints or None
If a single float or int, a symmetric sigma-clip will be performed using
the number provided as the sigma-multiplier to cut out from the input
time-series.
If a list of two ints/floats is provided, the function will perform an
'asymmetric' sigma-clip. The first element in this list is the sigma
value to use for fainter flux/mag values; the second element in this
list is the sigma value to use for brighter flux/mag values. For
example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
dimmings and greater than 3-sigma brightenings. Here the meaning of
"dimming" and "brightening" is set by *physics* (not the magnitude
system), which is why the `magsarefluxes` kwarg must be correctly set.
If `sigclip` is None, no sigma-clipping will be performed, and the
time-series (with non-finite elems removed) will be passed through to
the output.
magsarefluxes : bool
If your input measurements in `mags` are actually fluxes instead of
mags, set this is True.
verbose : bool
If True, will indicate progress and report errors.
Returns
-------
dict
A dict of the following form is returned::
{'itimes': the interpolated time values after gap-filling,
'imags': the interpolated mag/flux values after gap-filling,
'ierrs': the interpolated mag/flux values after gap-filling,
'cadence': the cadence of the output mag/flux time-series,
'minitime': the minimum value of the interpolated times array,
'lags': the lags used to calculate the auto-correlation function,
'acf': the value of the ACF at each lag used}
'''
# get the gap-filled timeseries
interpolated = fill_magseries_gaps(times,
mags,
errs,
fillgaps=fillgaps,
forcetimebin=forcetimebin,
sigclip=sigclip,
magsarefluxes=magsarefluxes,
filterwindow=filterwindow,
verbose=verbose)
if not interpolated:
print('failed to interpolate light curve to minimum cadence!')
return None
itimes, imags = interpolated['itimes'], interpolated['imags'],
# calculate the lags up to maxlags
if maxlags:
lags = nparange(0, maxlags)
else:
lags = nparange(itimes.size)
series_stdev = 1.483 * npmedian(npabs(imags))
if func != _autocorr_func3:
# get the autocorrelation as a function of the lag of the mag series
autocorr = nparray(
[func(imags, x, imags.size, 0.0, series_stdev) for x in lags])
# this doesn't need a lags array
else:
autocorr = _autocorr_func3(imags, lags[0], imags.size, 0.0,
series_stdev)
# return only the maximum number of lags
if maxlags is not None:
autocorr = autocorr[:maxlags]
interpolated.update({
'minitime': itimes.min(),
'lags': lags,
'acf': autocorr
})
return interpolated
def prewhiten_magseries(times,
mags,
errs,
whitenperiod,
whitenparams,
sigclip=3.0,
magsarefluxes=False,
plotfit=None,
plotfitphasedlconly=True,
rescaletomedian=True):
'''Removes a periodic sinusoidal signal generated using whitenparams from
the input magnitude time series.
whitenparams are the Fourier amplitude and phase coefficients:
[ampl_1, ampl_2, ampl_3, ..., ampl_X,
pha_1, pha_2, pha_3, ..., pha_X]
where X is the Fourier order. These are usually the output of a previous
Fourier fit to the light curve (from varbase.lcfit.fourier_fit_magseries for
example).
if rescaletomedian is True, then we add back the constant median term of the
magnitudes to the final pre-whitened mag series.
'''
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
sigclip=sigclip,
magsarefluxes=magsarefluxes)
median_mag = npmedian(smags)
# phase the mag series using the given period and epoch = min(stimes)
mintime = npmin(stimes)
# calculate the unsorted phase, then sort it
iphase = ((stimes - mintime) / whitenperiod - npfloor(
(stimes - mintime) / whitenperiod))
phasesortind = npargsort(iphase)
# these are the final quantities to use for the Fourier fits
phase = iphase[phasesortind]
pmags = smags[phasesortind]
perrs = serrs[phasesortind]
# get the times sorted in phase order (useful to get the fit mag minimum
# with respect to phase -- the light curve minimum)
ptimes = stimes[phasesortind]
# get the Fourier order
fourierorder = int(len(whitenparams) / 2)
# now subtract the harmonic series from the phased LC
# these are still in phase order
wmags = pmags - _fourier_func(whitenparams, phase, pmags)
# resort everything by time order
wtimeorder = npargsort(ptimes)
wtimes = ptimes[wtimeorder]
wphase = phase[wtimeorder]
wmags = wmags[wtimeorder]
werrs = perrs[wtimeorder]
if rescaletomedian:
wmags = wmags + median_mag
# prepare the returndict
returndict = {
'wtimes': wtimes, # these are in the new time order
'wphase': wphase,
'wmags': wmags,
'werrs': werrs,
'whitenparams': whitenparams,
'whitenperiod': whitenperiod
}
# make the fit plot if required
if plotfit and (isinstance(plotfit, str) or isinstance(plotfit, strio)):
if plotfitphasedlconly:
plt.figure(figsize=(10, 4.8))
else:
plt.figure(figsize=(16, 9.6))
if plotfitphasedlconly:
# phased series before whitening
plt.subplot(121)
plt.plot(phase,
pmags,
marker='.',
color='k',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('phase')
plt.title('phased LC before pre-whitening')
# phased series after whitening
plt.subplot(122)
plt.plot(wphase,
wmags,
marker='.',
color='g',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('phase')
plt.title('phased LC after pre-whitening')
else:
# time series before whitening
plt.subplot(221)
plt.plot(stimes,
smags,
marker='.',
color='k',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('JD')
plt.title('LC before pre-whitening')
# time series after whitening
plt.subplot(222)
plt.plot(wtimes,
wmags,
marker='.',
color='g',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('JD')
plt.title('LC after pre-whitening with period: %.6f' %
whitenperiod)
# phased series before whitening
plt.subplot(223)
plt.plot(phase,
pmags,
marker='.',
color='k',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('phase')
plt.title('phased LC before pre-whitening')
# phased series after whitening
plt.subplot(224)
plt.plot(wphase,
wmags,
marker='.',
color='g',
linestyle='None',
markersize=2.0,
markeredgewidth=0)
if not magsarefluxes:
plt.gca().invert_yaxis()
plt.ylabel('magnitude')
else:
plt.ylabel('fluxes')
plt.xlabel('phase')
plt.title('phased LC after pre-whitening')
plt.tight_layout()
plt.savefig(plotfit, format='png', pad_inches=0.0)
plt.close()
if isinstance(plotfit, str) or isinstance(plotfit, strio):
returndict['fitplotfile'] = plotfit
return returndict
def lightcurve_flux_measures(ftimes, fmags, ferrs, magsarefluxes=False):
'''This calculates percentiles and percentile ratios of the flux.
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
magsarefluxes : bool
If the `fmags` array actually contains fluxes, will not convert `mags`
to fluxes before calculating the percentiles.
Returns
-------
dict
A dict with all of the light curve flux percentiles and percentile
ratios calculated.
'''
ndet = len(fmags)
if ndet > 9:
# get the fluxes
if magsarefluxes:
series_fluxes = fmags
else:
series_fluxes = 10.0**(-0.4 * fmags)
series_flux_median = npmedian(series_fluxes)
# get the percent_amplitude for the fluxes
series_flux_percent_amplitude = (npmax(npabs(series_fluxes)) /
series_flux_median)
# get the flux percentiles
series_flux_percentiles = nppercentile(
series_fluxes,
[5.0, 10, 17.5, 25, 32.5, 40, 60, 67.5, 75, 82.5, 90, 95])
series_frat_595 = (series_flux_percentiles[-1] -
series_flux_percentiles[0])
series_frat_1090 = (series_flux_percentiles[-2] -
series_flux_percentiles[1])
series_frat_175825 = (series_flux_percentiles[-3] -
series_flux_percentiles[2])
series_frat_2575 = (series_flux_percentiles[-4] -
series_flux_percentiles[3])
series_frat_325675 = (series_flux_percentiles[-5] -
series_flux_percentiles[4])
series_frat_4060 = (series_flux_percentiles[-6] -
series_flux_percentiles[5])
# calculate the flux percentile ratios
series_flux_percentile_ratio_mid20 = series_frat_4060 / series_frat_595
series_flux_percentile_ratio_mid35 = series_frat_325675 / series_frat_595
series_flux_percentile_ratio_mid50 = series_frat_2575 / series_frat_595
series_flux_percentile_ratio_mid65 = series_frat_175825 / series_frat_595
series_flux_percentile_ratio_mid80 = series_frat_1090 / series_frat_595
# calculate the ratio of F595/median flux
series_percent_difference_flux_percentile = (series_frat_595 /
series_flux_median)
series_percentile_magdiff = -2.5 * nplog10(
series_percent_difference_flux_percentile)
return {
'flux_median': series_flux_median,
'flux_percent_amplitude': series_flux_percent_amplitude,
'flux_percentiles': series_flux_percentiles,
'flux_percentile_ratio_mid20': series_flux_percentile_ratio_mid20,
'flux_percentile_ratio_mid35': series_flux_percentile_ratio_mid35,
'flux_percentile_ratio_mid50': series_flux_percentile_ratio_mid50,
'flux_percentile_ratio_mid65': series_flux_percentile_ratio_mid65,
'flux_percentile_ratio_mid80': series_flux_percentile_ratio_mid80,
'percent_difference_flux_percentile': series_percentile_magdiff,
}
else:
LOGERROR('not enough detections in this magseries '
'to calculate flux measures')
return None
def find_redundant_points(points):
bisector_to_pair = make_bisector_to_pair_map(points)
angle_to_bisector = make_angle_to_bisector_map(bisector_to_pair)
median_bisector = get_median_bisector(angle_to_bisector)
lines_pairs = form_lines_pairs(angle_to_bisector, median_bisector)
intersection_to_lines_pair = make_intersection_to_pair_map(lines_pairs)
ys_critical = [p.get_both()['y'] for p in intersection_to_lines_pair.keys()] # form_pairs_intersections_values(lines_pairs)
y_med = npmedian(array(ys_critical))
y_separation_line = get_y_separation_line(median_bisector, y_med)
aim_centre_y_side = determine_enclosure_centre_side(points, y_separation_line)
crucial_points = find_crucial_points(intersection_to_lines_pair.keys(), y_separation_line, aim_centre_y_side)
if not crucial_points:
return []
xs_critucal = [p.get_both()['x'] for p in crucial_points]
x_med = npmedian(array(xs_critucal))
x_separation_line = Line2D(point1=Vector2D(x_med, 0.0), point2=Vector2D(x_med, 1.0))
aim_centre_x_side = determine_enclosure_centre_side(points, x_separation_line)
final_points = find_crucial_points(crucial_points, x_separation_line, aim_centre_x_side)
south_vector, west_vector = Vector2D(0.0, -1.0), Vector2D(-1.0, 0.0)
south_vector = define_side_direction_vector(y_separation_line, south_vector)
south_point = y_separation_line.first.sum(south_vector)
south = y_separation_line.define_point_side(south_point) * aim_centre_y_side > 0
west_vector = define_side_direction_vector(x_separation_line, west_vector)
west_point = x_separation_line.first.sum(west_vector)
west = x_separation_line.define_point_side(west_point) * aim_centre_x_side > 0
final_lines = list()
for intersection in final_points:
line_pair = intersection_to_lines_pair[intersection]
angle0 = abs(y_axis.angle(line_pair[0]))
angle1 = abs(y_axis.angle(line_pair[1]))
print angle0, angle1
if (south and west) or (not south and not west):
if angle0 > angle1:
final_lines.append(line_pair[0])
else:
final_lines.append(line_pair[1])
else:
# south-east or north-west
if angle0 < angle1:
final_lines.append(line_pair[0])
else:
final_lines.append(line_pair[1])
assert final_lines
rejected_points = set()
comparison_point = point_in_centre_containing_area(x_separation_line,
y_separation_line,
Vector2D(x_med, y_med),
south, west)
for line in final_lines:
comparison_side = line.define_point_side(comparison_point)
points = bisector_to_pair[line]
side0 = line.define_point_side(points[0])
side1 = line.define_point_side(points[1])
if side0 * comparison_side > 0:
rejected_points.add(points[0])
elif side1 * comparison_side > 0:
rejected_points.add(points[1])
else:
print "x-med, y-med point side: ", comparison_side
assert False
return rejected_points
def stetson_jindex(ftimes, fmags, ferrs, weightbytimediff=False):
'''This calculates the Stetson index for the magseries, based on consecutive
pairs of observations.
Based on Nicole Loncke's work for her Planets and Life certificate at
Princeton in 2014.
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
weightbytimediff : bool
If this is True, the Stetson index for any pair of mags will be
reweighted by the difference in times between them using the scheme in
Fruth+ 2012 and Zhange+ 2003 (as seen in Sokolovsky+ 2017)::
w_i = exp(- (t_i+1 - t_i)/ delta_t )
Returns
-------
float
The calculated Stetson J variability index.
'''
ndet = len(fmags)
if ndet > 9:
# get the median and ndet
medmag = npmedian(fmags)
# get the stetson index elements
delta_prefactor = (ndet / (ndet - 1))
sigma_i = delta_prefactor * (fmags - medmag) / ferrs
# Nicole's clever trick to advance indices by 1 and do x_i*x_(i+1)
sigma_j = nproll(sigma_i, 1)
if weightbytimediff:
difft = npdiff(ftimes)
deltat = npmedian(difft)
weights_i = npexp(-difft / deltat)
products = (weights_i * sigma_i[1:] * sigma_j[1:])
else:
# ignore first elem since it's actually x_0*x_n
products = (sigma_i * sigma_j)[1:]
stetsonj = (npsum(npsign(products) * npsqrt(npabs(products)))) / ndet
return stetsonj
else:
LOGERROR('not enough detections in this magseries '
'to calculate stetson J index')
return npnan
def bls_snr(blsdict,
times,
mags,
errs,
magsarefluxes=False,
sigclip=10.0,
perioddeltapercent=10,
npeaks=None,
assumeserialbls=False,
verbose=True):
'''Calculates the signal to noise ratio for each best peak in the BLS
periodogram.
This calculates two values of SNR:
SNR = transit model depth / RMS of light curve with transit model subtracted
altSNR = transit model depth / RMS of light curve inside transit
blsdict is the output of either bls_parallel_pfind or bls_serial_pfind.
times, mags, errs are ndarrays containing the magnitude series.
perioddeltapercent controls the period interval used by a bls_serial_pfind
run around each peak period to figure out the transit depth, duration, and
ingress/egress bins for eventual calculation of the SNR of the peak.
npeaks controls how many of the periods in blsdict['nbestperiods'] to find
the SNR for. If it's None, then this will calculate the SNR for all of
them. If it's an integer between 1 and len(blsdict['nbestperiods']), will
calculate for only the specified number of peak periods, starting from the
best period.
If assumeserialbls is True, will not rerun bls_serial_pfind to figure out
the transit depth, duration, and ingress/egress bins for eventual
calculation of the SNR of the peak. This is normally False because we assume
that the user will be using bls_parallel_pfind, which works on chunks of
frequency space so returns multiple values of transit depth, duration,
ingress/egress bin specific to those chunks. These may not be valid for the
global best peaks in the periodogram, so we need to rerun bls_serial_pfind
around each peak in blsdict['nbestperiods'] to get correct values for these.
FIXME: for now, we're only doing simple RMS. Need to calculate red and
white-noise RMS as outlined below:
- calculate the white noise rms and the red noise rms of the residual.
- the white noise rms is just the rms of the residual
- the red noise rms = sqrt(binnedrms^2 - expectedbinnedrms^2)
- calculate the SNR using:
sqrt(delta^2 / ((sigma_w ^2 / nt) + (sigma_r ^2 / Nt))))
where:
delta = transit depth
sigma_w = white noise rms
sigma_r = red noise rms
nt = number of in-transit points
Nt = number of distinct transits sampled
'''
# figure out how many periods to work on
if (npeaks and (0 < npeaks < len(blsdict['nbestperiods']))):
nperiods = npeaks
else:
if verbose:
LOGWARNING('npeaks not specified or invalid, '
'getting SNR for all %s BLS peaks' %
len(blsdict['nbestperiods']))
nperiods = len(blsdict['nbestperiods'])
nbestperiods = blsdict['nbestperiods'][:nperiods]
# get rid of nans first and sigclip
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
magsarefluxes=magsarefluxes,
sigclip=sigclip)
# make sure there are enough points to calculate a spectrum
if len(stimes) > 9 and len(smags) > 9 and len(serrs) > 9:
nbestsnrs = []
nbestasnrs = []
transitdepth, transitduration = [], []
# get these later
whitenoise, rednoise = [], []
nphasebins, transingressbin, transegressbin = [], [], []
# keep these around for diagnostics
allsubtractedmags = []
allphasedmags = []
allphases = []
allblsmodels = []
for ind, period in enumerate(nbestperiods):
# get the period interval
startp = period - perioddeltapercent * period / 100.0
endp = period + perioddeltapercent * period / 100.0
# see if we need to rerun bls_serial_pfind
if not assumeserialbls:
# run bls_serial_pfind with the kwargs copied over from the
# initial run. replace only the startp, endp, and verbose kwarg
# values
prevkwargs = blsdict['kwargs'].copy()
prevkwargs['verbose'] = verbose
prevkwargs['startp'] = startp
prevkwargs['endp'] = endp
blsres = bls_serial_pfind(times, mags, errs, **prevkwargs)
else:
blsres = blsdict
thistransdepth = blsres['blsresult']['transdepth']
thistransduration = blsres['blsresult']['transduration']
thisbestperiod = blsres['bestperiod']
thistransingressbin = blsres['blsresult']['transingressbin']
thistransegressbin = blsres['blsresult']['transegressbin']
thisnphasebins = blsdict['kwargs']['nphasebins']
# get the minimum light epoch using a spline fit
try:
spfit = spline_fit_magseries(times,
mags,
errs,
thisbestperiod,
magsarefluxes=magsarefluxes,
verbose=verbose)
thisminepoch = spfit['fitinfo']['fitepoch']
except ValueError:
LOGEXCEPTION('could not fit a spline to find a minimum of '
'the phased LC, trying SavGol fit instead...')
# fit a Savitsky-Golay instead and get its minimum
savfit = savgol_fit_magseries(times,
mags,
errs,
thisbestperiod,
magsarefluxes=magsarefluxes,
verbose=verbose)
thisminepoch = savfit['fitinfo']['fitepoch']
if isinstance(thisminepoch, np.ndarray):
if verbose:
LOGWARNING('minimum epoch is actually an array:\n'
'%s\n'
'instead of a float, '
'are there duplicate time values '
'in the original input? '
'will use the first value in this array.' %
repr(thisminepoch))
thisminepoch = thisminepoch[0]
# phase using this epoch
phased_magseries = phase_magseries_with_errs(stimes,
smags,
serrs,
thisbestperiod,
thisminepoch,
wrap=False,
sort=True)
tphase = phased_magseries['phase']
tmags = phased_magseries['mags']
terrs = phased_magseries['errs']
# use the transit depth and duration to subtract the BLS transit
# model from the phased mag series. we're centered about 0.0 as the
# phase of the transit minimum so we need to look at stuff from
# [0.0, transitphase] and [1.0-transitphase, 1.0]
transitphase = thistransduration / 2.0
transitindices = ((tphase < transitphase) | (tphase >
(1.0 - transitphase)))
# this is the BLS model
# constant = median(tmags) outside transit
# constant = thistransitdepth inside transit
blsmodel = npfull_like(tmags, npmedian(tmags))
if magsarefluxes:
blsmodel[transitindices] = (blsmodel[transitindices] +
thistransdepth)
else:
blsmodel[transitindices] = (blsmodel[transitindices] -
thistransdepth)
# this is the residual of mags - model
subtractedmags = tmags - blsmodel
# calculate the rms of this residual
subtractedrms = npstd(subtractedmags)
# the SNR is the transit depth divided by the rms of the residual
thissnr = npabs(thistransdepth / subtractedrms)
# alt SNR = expected transit depth / rms of timeseries in transit
altsnr = npabs(thistransdepth / npstd(tmags[transitindices]))
# tell user about stuff if verbose = True
if verbose:
LOGINFO('peak %s: new best period: %.6f, '
'fit center of transit: %.5f' %
(ind + 1, thisbestperiod, thisminepoch))
LOGINFO('transit ingress phase = %.3f to %.3f' %
(1.0 - transitphase, 1.0))
LOGINFO('transit egress phase = %.3f to %.3f' %
(0.0, transitphase))
LOGINFO('npoints in transit: %s' % tmags[transitindices].size)
LOGINFO('transit depth (delta): %.5f, '
'frac transit length (q): %.3f, '
' SNR: %.3f, altSNR: %.3f' %
(thistransdepth, thistransduration, thissnr, altsnr))
# update the lists with results from this peak
nbestsnrs.append(thissnr)
nbestasnrs.append(altsnr)
transitdepth.append(thistransdepth)
transitduration.append(thistransduration)
transingressbin.append(thistransingressbin)
transegressbin.append(thistransegressbin)
nphasebins.append(thisnphasebins)
# update the diagnostics
allsubtractedmags.append(subtractedmags)
allphasedmags.append(tmags)
allphases.append(tphase)
allblsmodels.append(blsmodel)
# update these when we figure out how to do it
# nphasebins.append(thisnphasebins)
# transingressbin.append(thisingressbin)
# transegressbin.append(thisegressbin)
# done with working on each peak
# if there aren't enough points in the mag series, bail out
else:
LOGERROR('no good detections for these times and mags, skipping...')
nbestsnrs, whitenoise, rednoise = None, None, None
transitdepth, transitduration = None, None
nphasebins, transingressbin, transegressbin = None, None, None
allsubtractedmags, allphases, allphasedmags = None, None, None
return {
'npeaks': npeaks,
'period': nbestperiods,
'snr': nbestsnrs,
'altsnr': nbestasnrs,
'whitenoise': whitenoise,
'rednoise': rednoise,
'transitdepth': transitdepth,
'transitduration': transitduration,
'nphasebins': nphasebins,
'transingressbin': transingressbin,
'transegressbin': transegressbin,
'allblsmodels': allblsmodels,
'allsubtractedmags': allsubtractedmags,
'allphasedmags': allphasedmags,
'allphases': allphases
}
def fourier_fit_magseries(
times,
mags,
errs,
period,
fourierorder=None,
fourierparams=None,
fix_period=True,
scale_errs_redchisq_unity=True,
sigclip=3.0,
magsarefluxes=False,
plotfit=False,
ignoreinitfail=True,
verbose=True,
curve_fit_kwargs=None,
):
'''This fits a Fourier series to a mag/flux time series.
Parameters
----------
times,mags,errs : np.array
The input mag/flux time-series to fit a Fourier cosine series to.
period : float
The period to use for the Fourier fit.
fourierorder : None or int
If this is an int, will be interpreted as the Fourier order of the
series to fit to the input mag/flux times-series. If this is None and
`fourierparams` is specified, `fourierparams` will be used directly to
generate the fit Fourier series. If `fourierparams` is also None, this
function will try to fit a Fourier cosine series of order 3 to the
mag/flux time-series.
fourierparams : list of floats or None
If this is specified as a list of floats, it must be of the form below::
[fourier_amp1, fourier_amp2, fourier_amp3,...,fourier_ampN,
fourier_phase1, fourier_phase2, fourier_phase3,...,fourier_phaseN]
to specify a Fourier cosine series of order N. If this is None and
`fourierorder` is specified, the Fourier order specified there will be
used to construct the Fourier cosine series used to fit the input
mag/flux time-series. If both are None, this function will try to fit a
Fourier cosine series of order 3 to the input mag/flux time-series.
fix_period : bool
If True, will fix the period with fitting the sinusoidal function to the
phased light curve.
scale_errs_redchisq_unity : bool
If True, the standard errors on the fit parameters will be scaled to
make the reduced chi-sq = 1.0. This sets the ``absolute_sigma`` kwarg
for the ``scipy.optimize.curve_fit`` function to False.
sigclip : float or int or sequence of two floats/ints or None
If a single float or int, a symmetric sigma-clip will be performed using
the number provided as the sigma-multiplier to cut out from the input
time-series.
If a list of two ints/floats is provided, the function will perform an
'asymmetric' sigma-clip. The first element in this list is the sigma
value to use for fainter flux/mag values; the second element in this
list is the sigma value to use for brighter flux/mag values. For
example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
dimmings and greater than 3-sigma brightenings. Here the meaning of
"dimming" and "brightening" is set by *physics* (not the magnitude
system), which is why the `magsarefluxes` kwarg must be correctly set.
If `sigclip` is None, no sigma-clipping will be performed, and the
time-series (with non-finite elems removed) will be passed through to
the output.
magsarefluxes : bool
If True, will treat the input values of `mags` as fluxes for purposes of
plotting the fit and sig-clipping.
plotfit : str or False
If this is a string, this function will make a plot for the fit to the
mag/flux time-series and writes the plot to the path specified here.
ignoreinitfail : bool
If this is True, ignores the initial failure to find a set of optimized
Fourier parameters using the global optimization function and proceeds
to do a least-squares fit anyway.
verbose : bool
If True, will indicate progress and warn of any problems.
curve_fit_kwargs : dict or None
If not None, this should be a dict containing extra kwargs to pass to
the scipy.optimize.curve_fit function.
Returns
-------
dict
This function returns a dict containing the model fit parameters, the
minimized chi-sq value and the reduced chi-sq value. The form of this
dict is mostly standardized across all functions in this module::
{
'fittype':'fourier',
'fitinfo':{
'finalparams': the list of final model fit params,
'finalparamerrs': list of errs for each model fit param,
'fitmags': the model fit mags,
'fitperiod': the fit period if this wasn't set to fixed,
'fitepoch': this is times.min() for this fit type,
'actual_fitepoch': time of minimum light from fit model
... other fit function specific keys ...
},
'fitchisq': the minimized value of the fit's chi-sq,
'fitredchisq':the reduced chi-sq value,
'fitplotfile': the output fit plot if fitplot is not None,
'magseries':{
'times':input times in phase order of the model,
'phase':the phases of the model mags,
'mags':input mags/fluxes in the phase order of the model,
'errs':errs in the phase order of the model,
'magsarefluxes':input value of magsarefluxes kwarg
}
}
NOTE: the returned value of 'fitepoch' in the 'fitinfo' dict returned by
this function is the time value of the first observation since this is
where the LC is folded for the fit procedure. To get the actual time of
minimum epoch as calculated by a spline fit to the phased LC, use the
key 'actual_fitepoch' in the 'fitinfo' dict.
'''
stimes, smags, serrs = sigclip_magseries(times,
mags,
errs,
sigclip=sigclip,
magsarefluxes=magsarefluxes)
# get rid of zero errs
nzind = npnonzero(serrs)
stimes, smags, serrs = stimes[nzind], smags[nzind], serrs[nzind]
phase, pmags, perrs, ptimes, mintime = (get_phased_quantities(
stimes, smags, serrs, period))
# get the fourier order either from the scalar order kwarg...
if fourierorder and fourierorder > 0 and not fourierparams:
fourieramps = [0.6] + [0.2] * (fourierorder - 1)
fourierphas = [0.1] + [0.1] * (fourierorder - 1)
fourierparams = fourieramps + fourierphas
# or from the fully specified coeffs vector
elif not fourierorder and fourierparams:
fourierorder = int(len(fourierparams) / 2)
else:
LOGWARNING('specified both/neither Fourier order AND Fourier coeffs, '
'using default Fourier order of 3')
fourierorder = 3
fourieramps = [0.6] + [0.2] * (fourierorder - 1)
fourierphas = [0.1] + [0.1] * (fourierorder - 1)
fourierparams = fourieramps + fourierphas
if verbose:
LOGINFO('fitting Fourier series of order %s to '
'mag series with %s observations, '
'using period %.6f, folded at %.6f' %
(fourierorder, len(phase), period, mintime))
# initial minimize call to find global minimum in chi-sq
initialfit = spminimize(_fourier_chisq,
fourierparams,
args=(phase, pmags, perrs))
# make sure this initial fit succeeds before proceeding
if initialfit.success or ignoreinitfail:
if verbose:
LOGINFO('initial fit done, refining...')
leastsqparams = initialfit.x
try:
curvefit_params = npconcatenate((nparray([period]), leastsqparams))
# set up the bounds for the fit parameters
if fix_period:
curvefit_bounds = ([period - 1.0e-7] +
[-npinf] * fourierorder +
[-npinf] * fourierorder,
[period + 1.0e-7] + [npinf] * fourierorder +
[npinf] * fourierorder)
else:
curvefit_bounds = ([0.0] + [-npinf] * fourierorder +
[-npinf] * fourierorder,
[npinf] + [npinf] * fourierorder +
[npinf] * fourierorder)
curvefit_func = partial(
sinusoidal.fourier_curvefit_func,
zerolevel=npmedian(smags),
epoch=mintime,
fixed_period=period if fix_period else None,
)
if curve_fit_kwargs is not None:
finalparams, covmatrix = curve_fit(
curvefit_func,
stimes,
smags,
p0=curvefit_params,
sigma=serrs,
bounds=curvefit_bounds,
absolute_sigma=(not scale_errs_redchisq_unity),
**curve_fit_kwargs)
else:
finalparams, covmatrix = curve_fit(
curvefit_func,
stimes,
smags,
p0=curvefit_params,
sigma=serrs,
bounds=curvefit_bounds,
absolute_sigma=(not scale_errs_redchisq_unity),
)
except Exception:
LOGEXCEPTION("curve_fit returned an exception")
finalparams, covmatrix = None, None
# if the fit succeeded, then we can return the final parameters
if finalparams is not None and covmatrix is not None:
# this is the fit period
fperiod = finalparams[0]
phase, pmags, perrs, ptimes, mintime = (get_phased_quantities(
stimes, smags, serrs, fperiod))
# calculate the chisq and reduced chisq
fitmags = _fourier_func(finalparams[1:], phase, pmags)
fitchisq = npsum(
((fitmags - pmags) * (fitmags - pmags)) / (perrs * perrs))
n_free_params = len(pmags) - len(finalparams)
if fix_period:
n_free_params -= 1
fitredchisq = fitchisq / n_free_params
stderrs = npsqrt(npdiag(covmatrix))
if verbose:
LOGINFO('final fit done. chisq = %.5f, reduced chisq = %.5f' %
(fitchisq, fitredchisq))
# figure out the time of light curve minimum (i.e. the fit epoch)
# this is when the fit mag is maximum (i.e. the faintest)
# or if magsarefluxes = True, then this is when fit flux is minimum
if not magsarefluxes:
fitmagminind = npwhere(fitmags == npmax(fitmags))
else:
fitmagminind = npwhere(fitmags == npmin(fitmags))
if len(fitmagminind[0]) > 1:
fitmagminind = (fitmagminind[0][0], )
# assemble the returndict
returndict = {
'fittype': 'fourier',
'fitinfo': {
'fourierorder': fourierorder,
# return coeffs only for backwards compatibility with
# existing functions that use the returned value of
# fourier_fit_magseries
'finalparams': finalparams[1:],
'finalparamerrs': stderrs,
'initialfit': initialfit,
'fitmags': fitmags,
'fitperiod': finalparams[0],
# the 'fitepoch' is just the minimum time here
'fitepoch': mintime,
# the actual fit epoch is calculated as the time of minimum
# light OF the fit model light curve
'actual_fitepoch': ptimes[fitmagminind]
},
'fitchisq': fitchisq,
'fitredchisq': fitredchisq,
'fitplotfile': None,
'magseries': {
'times': ptimes,
'phase': phase,
'mags': pmags,
'errs': perrs,
'magsarefluxes': magsarefluxes
},
}
# make the fit plot if required
if plotfit and isinstance(plotfit, str):
make_fit_plot(phase,
pmags,
perrs,
fitmags,
fperiod,
mintime,
mintime,
plotfit,
magsarefluxes=magsarefluxes)
returndict['fitplotfile'] = plotfit
return returndict
# if the leastsq fit did not succeed, return Nothing
else:
LOGERROR(
'fourier-fit: least-squared fit to the light curve failed')
return {
'fittype': 'fourier',
'fitinfo': {
'fourierorder': fourierorder,
'finalparams': None,
'finalparamerrs': None,
'initialfit': initialfit,
'fitmags': None,
'fitperiod': None,
'fitepoch': None,
'actual_fitepoch': None,
},
'fitchisq': npnan,
'fitredchisq': npnan,
'fitplotfile': None,
'magseries': {
'times': ptimes,
'phase': phase,
'mags': pmags,
'errs': perrs,
'magsarefluxes': magsarefluxes
}
}
# if the fit didn't succeed, we can't proceed
else:
LOGERROR('initial Fourier fit did not succeed, '
'reason: %s, returning scipy OptimizeResult' %
initialfit.message)
return {
'fittype': 'fourier',
'fitinfo': {
'fourierorder': fourierorder,
'finalparams': None,
'finalparamerrs': None,
'initialfit': initialfit,
'fitmags': None,
'fitperiod': None,
'fitepoch': None,
'actual_fitepoch': None,
},
'fitchisq': npnan,
'fitredchisq': npnan,
'fitplotfile': None,
'magseries': {
'times': ptimes,
'phase': phase,
'mags': pmags,
'errs': perrs,
'magsarefluxes': magsarefluxes
}
}
def run(dataset,
k,
it_max=1000,
min_variation=1.0e-4,
labels=None,
full_output=True):
# Init centers_coord
centers_id = random.randint(dataset.shape[0], size=k)
centers_coord = dataset[centers_id, :]
# Each instance will be put on a initial random cluster
inst_cluster_id = random.randint(k, size=dataset.shape[0])
# Auxiliary vectors to keep code cleaner
auxvec_cur_distances = array([0.0] * k)
prev_variation = 1.0 + min_variation
it = 0
while it < it_max and prev_variation >= min_variation:
it += 1
for inst_id in range(dataset.shape[0]):
for center_id in range(k):
# For each instance, calculate the distance between
# each center
auxvec_cur_distances[center_id] = \
Kmedians.__euclideandist__(
dataset[inst_id, :],
centers_coord[center_id, :])
# For each instance, let it be part of the nearest
# cluster
inst_cluster_id[inst_id] = argmin(auxvec_cur_distances)
# For each cluster, calculate the new center coordinates
# Using Median
for center_id in range(k):
new_cur_cluster_coords = npmedian(
dataset[inst_cluster_id == center_id, :], axis=0)
# Calculate variation between previous centers_coord and
# new ones (using infinite norm)
prev_variation = max(
prev_variation,
max(abs(centers_coord[center_id] -
new_cur_cluster_coords)))
centers_coord[center_id] = new_cur_cluster_coords
# Build up answer
ans = {
"inst_cluster_id": inst_cluster_id,
"centers_coord": centers_coord,
}
if full_output:
ans = {
"clustering_method":
"K-Medians",
"k":
k,
**ans,
**ClusterMetrics.runall(dataset=dataset,
centers_coord=centers_coord,
inst_cluster_id=inst_cluster_id,
labels=labels),
}
return ans
def sigclip_magseries_with_extparams(times,
mags,
errs,
extparams,
sigclip=None,
iterative=False,
magsarefluxes=False):
'''Select the finite times, magnitudes (or fluxes), and errors from the passed
values, and apply symmetric or asymmetric sigma clipping to them. Returns
sigma-clipped times, mags, and errs. Uses the same indices to filter out the
values of all arrays in the extparams list.
Args:
times (np.array): ...
mags (np.array): numpy array to sigma-clip. Does not assume all values
are finite. Does not assume anything about whether they're
positive/negative.
errs (np.array): ...
extparams (list of np.arrays): external parameters to also filter using
the same indices as used for sigma-clipping.
iterative (bool): True if you want iterative sigma-clipping.
magsarefluxes (bool): True if your "mags" are in fact fluxes, i.e. if
"dimming" corresponds to your "mags" getting smaller.
sigclip (float or list): If float, apply symmetric sigma clipping. If
list, e.g., [10., 3.], will sigclip out greater than 10-sigma dimmings
and greater than 3-sigma brightenings. Here the meaning of "dimming"
and "brightening" is set by *physics* (not the magnitude system), which
is why the `magsarefluxes` kwarg must be correctly set.
Returns:
stimes, smags, serrs: (sigmaclipped values of each).
'''
returnerrs = True
# fake the errors if they don't exist
# this is inconsequential to sigma-clipping
# we don't return these dummy values if the input errs are None
if errs is None:
# assume 0.1% errors if not given
# this should work for mags and fluxes
errs = 0.001 * mags
returnerrs = False
# filter the input times, mags, errs; do sigclipping and normalization
find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
ftimes, fmags, ferrs = times[find], mags[find], errs[find]
# apply the same indices to the external parameters
for epi, eparr in enumerate(extparams):
extparams[epi] = eparr[find]
# get the median and stdev = 1.483 x MAD
median_mag = npmedian(fmags)
stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483
# sigclip next for a single sigclip value
if sigclip and isinstance(sigclip, (float, int)):
if not iterative:
sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
# apply the same indices to the external parameters
for epi, eparr in enumerate(extparams):
extparams[epi] = eparr[sigind]
else:
#
# iterative version adapted from scipy.stats.sigmaclip
#
delta = 1
this_times = ftimes
this_mags = fmags
this_errs = ferrs
while delta:
this_median = npmedian(this_mags)
this_stdev = (npmedian(npabs(this_mags - this_median))) * 1.483
this_size = this_mags.size
# apply the sigclip
tsi = (npabs(this_mags - this_median)) < (sigclip * this_stdev)
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# apply the same indices to the external parameters
for epi, eparr in enumerate(extparams):
extparams[epi] = eparr[tsi]
# update delta and go to the top of the loop
delta = this_size - this_mags.size
# final sigclipped versions
stimes, smags, serrs = this_times, this_mags, this_errs
# this handles sigclipping for asymmetric +ve and -ve clip values
elif sigclip and isinstance(sigclip, list) and len(sigclip) == 2:
# sigclip is passed as [dimmingclip, brighteningclip]
dimmingclip = sigclip[0]
brighteningclip = sigclip[1]
if not iterative:
if magsarefluxes:
nottoodimind = ((fmags - median_mag) >
(-dimmingclip * stddev_mag))
nottoobrightind = ((fmags - median_mag) <
(brighteningclip * stddev_mag))
else:
nottoodimind = ((fmags - median_mag) <
(dimmingclip * stddev_mag))
nottoobrightind = ((fmags - median_mag) >
(-brighteningclip * stddev_mag))
sigind = nottoodimind & nottoobrightind
stimes = ftimes[sigind]
smags = fmags[sigind]
serrs = ferrs[sigind]
# apply the same indices to the external parameters
for epi, eparr in enumerate(extparams):
extparams[epi] = eparr[sigind]
else:
#
# iterative version adapted from scipy.stats.sigmaclip
#
delta = 1
this_times = ftimes
this_mags = fmags
this_errs = ferrs
while delta:
this_median = npmedian(this_mags)
this_stdev = (npmedian(npabs(this_mags - this_median))) * 1.483
this_size = this_mags.size
if magsarefluxes:
nottoodimind = ((this_mags - this_median) >
(-dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_median) <
(brighteningclip * this_stdev))
else:
nottoodimind = ((this_mags - this_median) <
(dimmingclip * this_stdev))
nottoobrightind = ((this_mags - this_median) >
(-brighteningclip * this_stdev))
# apply the sigclip
tsi = nottoodimind & nottoobrightind
# update the arrays
this_times = this_times[tsi]
this_mags = this_mags[tsi]
this_errs = this_errs[tsi]
# apply the same indices to the external parameters
for epi, eparr in enumerate(extparams):
extparams[epi] = eparr[tsi]
# update delta and go to top of the loop
delta = this_size - this_mags.size
# final sigclipped versions
stimes, smags, serrs = this_times, this_mags, this_errs
else:
stimes = ftimes
smags = fmags
serrs = ferrs
if returnerrs:
return stimes, smags, serrs, extparams
else:
return stimes, smags, None, extparams
def L2(input_path_L1, input_path_Compensate, output_path, E0_const):
try:
input_fp_Compensate = open(input_path_Compensate, 'r')
except IOError:
print "IO error;Check the input File: ", input_path_Compensate
except:
print "Unexpected Open Error: ", input_path_Compensate
input_fp_Compensate.close()
try:
input_fp_L1 = open(input_path_L1, 'r')
except IOError:
print "IO error;Check the input File: ", input_path_L1
except Error:
print "Unexpected Open Error: ", input_path_L1
input_fp_L1.close()
#output file path
output_file_path = os.path.join(output_path, 'ResultL2.csv')
try:
output_fp = open(output_file_path, 'w+')
except IOError:
print "IO error;Check the output File: ", output_file_path
return 'L2 failed'
#output_plot_1�� Reynold-Taylor Equation����
output_plot_2_file_path = os.path.join(output_path, 'Plot_L2_2.csv')
try:
output_plot_2_fp = open(output_plot_2_file_path, 'w+')
except IOError:
print "IO error;Check the output File: ", output_plot_2_file_path
return 'L2 failed'
try:
Compensate_csv = csv.reader(input_fp_Compensate, delimiter = ',')
except csv.Error:
print "Parse ErrorCheck the input File: ", input_path_Compensate
except StandardError:
print "Unexpected Read Error: ", input_path_Compensate
try:
L1_csv = csv.reader(input_fp_L1, delimiter = ',')
except csv.Error:
print "Parse ErrorCheck the input File: ", input_path_L1
except StandardError:
print "Unexpected Read Error: ", input_path_L1
n_Compensate = 0
n_L1 = 0
data_Compensate = []
data_L1 = []
for row in Compensate_csv:
data_Compensate.append(row)
n_Compensate = n_Compensate + 1
for row in L1_csv:
data_L1.append(row)
n_L1 = n_L1 + 1
#Data count check
if(n_Compensate != n_L1):
print 'Count Error;Process count dismatch between Compensate and L1'
return 'L2 failed'
#initialize
date = []
rsdn = npzeros(n_Compensate)
Ta = npzeros(n_Compensate)
h2o = npzeros(n_Compensate)
#press = npzeros(n_Compensate)
#Read Input Data
i = 0
for row in data_Compensate:
rsdn[i] = float(row[0])
Ta[i] = float(row[1])
h2o[i] = float(row[2])
i = i + 1
press = 998.0
#initialize
Fs = npzeros(n_L1)
Fc = npzeros(n_L1)
Fsc = npzeros(n_L1)
Hs = npzeros(n_L1)
Hc = npzeros(n_L1)
Hsc = npzeros(n_L1)
LEs = npzeros(n_L1)
LEc = npzeros(n_L1)
LEsc = npzeros(n_L1)
co2 = npzeros(n_L1)
ustar = npzeros(n_L1)
itime = npzeros(n_L1)
iustar = npzeros(n_L1)
date = []
i = 0
for row in data_L1:
date.append(row[0])
Fs[i] = float(row[1])
Fc[i] = float(row[2])
Fsc[i] = float(row[3])
Hs[i] = float(row[4])
Hc[i] = float(row[5])
Hsc[i] = float(row[6])
LEs[i] = float(row[7])
LEc[i] = float(row[8])
LEsc[i] = float(row[9])
co2[i] = float(row[10])
ustar[i] = float(row[14])
itime[i] = float(row[15])
iustar[i] = float(row[16])
i = i + 1
# Define constants and parameters for gap filling
#--------------------------------------------------------------------------
num_day = 28
ni = 36
nd = 10
n1 = 2 # how many the largest points are considered for respiration
# DO NOT Modify!
#num_point_per_day = 24 # number of data points per day (48 -> 30 min avg time)
#avgtime = 30
#determine num_point_per_day automatically . using datetime module
date_1st = datetime.datetime.strptime(date[0], "%Y-%m-%d %H:%M")
date_2nd = datetime.datetime.strptime(date[1], "%Y-%m-%d %H:%M")
date_diff = date_2nd - date_1st
avgtime = int(date_diff.seconds / 60) # averaging time (minutes)
num_point_per_day = 1440 / avgtime # number of data points per day (1440 : minutes of a day)
num_segment = num_point_per_day * num_day
num_avg = int(n_L1 / num_segment)
num_day_2 = 7
# nday_re = 20
# noverlap = 5
num_day_re = 20
noverlap = 5
ni = int(num_point_per_day * 3 / 4) # the data point that night starts
nd = 300 / avgtime # how many the largest points are considered for respiration (300 : minitues of 5 hours)
#--------------------------------------------------------------------------
#E0_const = True # Do you want to use constant E0 for one year? Y/N
beta0 = nparray([2, 200])
Tref = 10.0
T0 = -46.02
gap_limit = 0.025
ustar_limit = 0.5
upper_Fc = 0.35 # upper limit of nighttime CO2 flux (mg/m2/s)
Fc_limit = 0.005
## Information for MLT
drsdn = 50.0 # W/m2
dta = 2.5 # oC
dvpd = 5.0 # 5 hPa
rv = 461.51
#--------------------------------------------------------------------------
upper_co2 = 1000.0 # upper limit of CO2 concent.(mg/m3)
upper_h2o = 60.0 # upper limit of H2O concent. (g/m3)
upper_Ta = 60.0 # upper limit of air temperature (oC)
lower_Fc = -3.0 # lower limit of daytime CO2 flux (mg/m2/s)
lower_LE = -200 # lower limit of LE (W/m2)
lower_H = -300 # lower limit of H (W/m2)
upper_Fc = 3 # upper limit of nighttime CO2 flux (mg/m2/s)
upper_LE = 800 # upper limit of LE (W/m2)
upper_H = 800 # upper limit of H (W/m2)
upper_agc = 95.0 # upper limit of AGC value
ustar_limit = 0.03 # minimum ustar for filtering out nighttime fluxes
Fc_limit = 0.005 # lower limit of Re (ecosystem respiration) (mg/m2/s)
gap_limit = 0.025 # 0.025 --> 95# confidence interval
Tak = npzeros(len(Ta))
tr = npzeros(len(Ta))
ea = npzeros(len(Ta))
es = npzeros(len(Ta))
vpd = npzeros(len(Ta))
#--------------------------------------------------------------------------
# calculation of vapor pressure deficit
a = [13.3185, 1.9760, 0.6445, 0.1299]
for i in range(n_Compensate):
Tak[i] = Ta[i] + 273.15
tr[i] = 1.0-(373.15/Tak[i])
es[i] = 1013.25*exp(a[0]*tr[i]-a[1]*(tr[i]**2)-(a[2]*(tr[i]**3))-a[3]*(tr[i]**4)) # hPa
for i in range(n_L1):
ea[i] = h2o[i]
vpd[i]= float(es[i]) - float(ea[i]) #unit is hPa
Fc_filled = copy.deepcopy(Fsc)
print 'Gap Filling Process'
print 'Before running this program, '
print ' please make sure that you correctly set all parameters'
#print 'E0_const'. E0_const
#print 'nn', nn
#print 'num_point_per_day', num_point_per_day
#print 'num_day_2', num_day_2
#print 'num_day_re', num_day_re
#print 'noverlap', noverlap
#print 'drsdn', drsdn
#print 'dta', dta
#print 'dvpd', dvpd
#print '-------------------------------------------------------------------'
index = []
for main_j in range(num_avg): # loop for gap-filling of CO2 fluxes
seg_start_i = main_j * num_segment
seg_fin_i = seg_start_i + num_segment
if((seg_start_i + 2 * num_segment) > n_L1):
seg_fin_i = n_L1
x2 = []
x3 = []
#--------------------------------------------------------------------------
if(main_j == 0):
print 'Application of modified lookup table method'
#--------------------------------------------------------------------------
for i in range(seg_start_i, seg_fin_i):
if(itime[i] == 1):
ii = 0
if(isnan(Fsc[i]) == True):
jj = 0
while ((ii < 1) and (jj <= 4)):
ta_f = Ta[i]
rsdn_f = rsdn[i]
vpd_f = vpd[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day+1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day+1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day
i1 = n_L1
if(i0 < 1):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(vpd_f - vpd[j]) < dvpd) and \
(fabs(rsdn_f - rsdn[j]) < drsdn) and \
(fabs(ta_f - Ta[j]) < dta) and \
(isnan(Fsc[j]) == False)):
ks = ks + 1
x3_temp = []
x2.append(Fsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
x3.append(x3_temp)
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
Fc_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
if(ii < 1):
jj = 0
while(ii < 1):
rsdn_f = rsdn[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day+1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day+1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day
i1 = n_L1
if(i0 < 0):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(rsdn_f - rsdn[j]) < drsdn) and \
(isnan(Fsc[j]) == False)):
ks = ks + 1
x3_temp = []
x2.append(Fsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
x3.append(x3_temp)
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
Fc_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
x2 = []
x3 = []
ks = 0
d = npzeros((n_L1, 5))
d2 = npzeros((n_L1, 5))
dd = npzeros((n_L1, 5))
x4 = []
#Regression to Lloyd-Taylor equation
print 'Regression to Lloyd-Taylor equation'
if(E0_const == True):
for i in range(ni-1, n_Compensate, num_point_per_day):
t1 = npzeros(nd)
for j in range(nd):
t1[j] = Fsc[i + j]
#Set to 'descend'
t2, IX = Common.matlab_sort(t1)
k2 = 0
for k in range(nd-1):
if((isnan(t2[k]) == False) and \
(t2[k] < upper_Fc) and \
(t2[k+1] > Fc_limit)):
k2 = k2 + 1
if(k2 >= 2):
for j in range(nd-1):
if((itime[i+1 + IX[j]] == 0) and \
(isnan(t2[j]) == False) and \
(isnan(Ta[i+1 + IX[j]]) == False) and \
(t2[j] < upper_Fc) and \
(t2[j + 1] > Fc_limit) and \
(iustar[i + IX[j]] == 0) and \
(iustar[i + IX[j+1]] == 0)):
x3.append(t2[j])
x3.append(t2[j+1])
x2.append(Ta[i + IX[j]])
x2.append(Ta[i + IX[j+1]])
x4.append(date[i + IX[j]])
x4.append(date[i + IX[j+1]])
ks = ks + n1
break
TC = copy.deepcopy(nparray(x2))
PV = copy.deepcopy(nparray(x3))
betafit = spfmin(Common.Reco, beta0, args = (TC, PV), disp=False)
A = betafit[0]
B = betafit[1]
yfit = npzeros(len(TC))
for i in range(len(TC)):
yfit[i] = A * exp(B * (1 / (10 + 46.02) - 1 / (TC[i] + 46.02)))
E0 = betafit[1]
E0l = copy.deepcopy(E0)
# figure(1)
# plot(TC][PV,'ko',TC][yfit,'or')
# grid
# xlabel('air temperature (^oC)')
# ylabel('Ecosystem respiration(mgm^{-2}s^{-1})')
# TC = x2'
# PV = x3'
#
# [beta][resnorm] = lsqcurvefit(@myfun][beta0][TC][PV)
#
# A=beta(1)
# B=beta(2)
# yfit=A.*exp(B.*(1./(10.+46.02)-1./(TC+46.02)))
# E0 = betafit(2)
# E0l = E0
#
#
# figure(5)
# plot(TC][PV,'ko',TC][yfit,'or')
# grid
# xlabel('air temperature (^oC)')
# ylabel('Ecosystem respiration(mgm^{-2}s^{-1})')
x2 = []
x3 = []
t1 = []
t2 = []
TC = []
PV = []
yfit = npzeros(len(TC))
#num_day_re = 20
#noverlap = 5
#avgtime = 30
delta = (60 / avgtime) * 24 * num_day_re
dnoverlap = (60 / avgtime) * 24 * noverlap
jj = 0
sday = []
Rref = []
RE_limit = []
stdev_E0 = []
E0v = []
REs = []
Taylor_date = []
yfit_array = []
for i in range(0, n_L1, dnoverlap):
i0 = int(i - delta / 2)
i1 = int(i + delta / 2)
if(i0 < 1):
i0 = 0
i1 = int(i0 + delta)
if(i1 >= n_L1):
i0 = int(n_L1 - delta) - 1
i1 = n_L1
ks = 1
for j in range(i0+ni-1, i1, num_point_per_day):
t1 = npzeros(nd)
for k in range(nd):
t1[k] = Fsc[j + k]
#Set to 'descend'
t2, IX = Common.matlab_sort(t1)
k2 = 1
for k in range(nd-1):
if((isnan(t2[k]) == False) and \
(t2[k] < upper_Fc) and \
(t2[k+1] > Fc_limit)):
k2 = k2 + 1
if(k2 >= n1):
for k in range(nd-1):
if((itime[j+1 +IX[k]] == 0) and \
(isnan(t2[k]) == False) and \
(isnan(Ta[j + IX[k]]) == False) and \
(t2[k] < upper_Fc) and \
(t2[k+1] > Fc_limit) and \
(iustar[j +IX[k]] == 0) and \
(iustar[j +IX[k+1]] == 0)):
x3.append(t2[k])
x3.append(t2[k+1])
x2.append(Ta[j + IX[k]])
x2.append(Ta[j + IX[k+1]])
Taylor_date.append(str(date[j + IX[k]]))
Taylor_date.append(str(date[j + IX[k+1]]))
ks = ks + n1
break
ks = ks - 1
if(ks < 6):
if(E0_const == True):
Rref.append(float('NaN'))
RE_limit.append(float('NaN'))
jj = jj + 1
else:
Rref.append(float('NaN'))
E0v.append(float('NaN'))
stdev_E0.append(float('NaN'))
RE_limit.append(float('NaN'))
jj = jj + 1
else:
TC = copy.deepcopy(nparray(x2))
PV = copy.deepcopy(nparray(x3))
if(E0_const == True):
betafit = spfmin(Common.Reco2, beta0, args = (TC, PV, E0l), disp=False)
A = betafit[0]
Rref.append(A)
for j in range(len(TC)):
yfit = A * exp(E0 * (1.0/(10.0+46.02) - 1.0/(TC[j] + 46.02)))
yfit_array.append(yfit)
REs.append(PV[j] - yfit)
sz = nparray(REs).shape
upper = fabs(Common.tq(gap_limit, sz[0]-1 ) )
RE_limit.append(upper*Common.stdn1(nparray(REs))/sqrt(sz[0]))
jj = jj + 1
else:
betafit=spfmin(Common.Reco2, beta0, args = (TC, PV, E0))
A=betafit[0]
B=betafit[1]
Rref.append(A)
E0v.append(B)
if((B < 0) or (B > 450)):
E0v.append(float('NaN'))
for j in range(len(TC)):
yfit = A * exp(E0v[jj] * (1.0 / (10.0 + 46.02) - 1.0 / (TC[j] + 46.02)))
yfit_array.append(yfit)
REs.append(PV[j] - yfit)
sz = nparray(REs).shape
upper = abs(Common.tq(gap_limit, sz[0]-1))
stdev_E0.append(Common.stdn1(REs) / sqrt(sz[0]))
RE_limit.append(upper * Common.stdn1(nparray(REs)) / sqrt(sz[0]))
jj = jj + 1
#Regression to Lloyd-Taylor equation with 28-day segmentation
date_extracted = re.search('^(\d{4}[-]\d{2}[-]\d{2})',str(Taylor_date[0]))
#print date_extracted.group(0)
if(date_extracted != None):
fname = 'Plot_L2_1_'+str(date_extracted.group(0))+'.csv'
output_plot_1_file_path = os.path.join(output_path, fname)
try:
output_plot_1_fp = open(output_plot_1_file_path, 'w+')
except IOError:
print "IO error;Check the output File: ", output_plot_1_file_path
return 'L2 failed'
for i in range(len(TC)):
file_plot_str = StringIO()
file_plot_str.write(Taylor_date[i] + ',') #1
file_plot_str.write(str(A) + ',') #2
file_plot_str.write(str(B) + ',') #3
file_plot_str.write(str(TC[i]) + ',') #4
file_plot_str.write(str(PV[i]) + ',' ) #5
file_plot_str.write(str(yfit_array[i]) + '\n' ) #6
output_plot_string = file_plot_str.getvalue()
output_plot_1_fp.write(output_plot_string)
output_plot_1_fp.close()
sday_temp = []
sday_temp.append(i)
sday_temp.append(i0)
sday_temp.append(i1)
sday.append(sday_temp)
x2 = []
x3 = []
t1 = []
t2 = []
TC = []
PV = []
Taylor_date = []
REs = []
yfit = []
sday = nparray(sday)
if(E0_const == True):
print 'Long-term E0 '
E0s = copy.deepcopy(E0l)
else:
E0v_s = []
stdev_E0_s = []
for k in range(len(E0v)):
E0v_s.append(E0v[k]/stdev_E0[k])
stdev_E0_s.append(1/stdev_E0[k])
print 'Short-term E0 '
E0s = npnansum(E0v_s)/npnansum(stdev_E0_s)
Rref = []
#REs = []
#RE_limit = []
jj = 0
for i in range(0, n_L1, dnoverlap):
i0 = i - delta / 2
i1 = i + delta / 2
if(i0 < 1):
i0 = 0
i1 = i0 + delta
if(i1 >= n_L1):
i0 = n_L1 - delta - 1
i1 = n_L1
ks = 1
for j in range(i0+ni-1, i1, num_point_per_day):
t1 = npzeros(nd)
for k in range(nd):
t1[k] = Fsc[j + k]
#Set to 'descend'
t2, IX = Common.matlab_sort(t1)
k2 = 1
for k in range(nd-1):
if((isnan(t2[k]) == False) and \
(t2[k] < upper_Fc) and \
(t2[k+1] > Fc_limit)):
k2 = k2 + 1
if(k2 >= n1):
for k in range(nd-1):
if((itime[j+1 + IX[k]] == 0) and \
(isnan(t2[k]) == False) and \
(isnan(Ta[j + IX[k]]) == False) and \
(t2[k] < upper_Fc) and \
(t2[k+1] > Fc_limit) and \
(iustar[j + IX[k]] == 0) and \
(iustar[j + IX[k+1]] == 0)):
x3.append(t2[k])
x3.append(t2[k + 1])
x2.append(Ta[j + IX[k]])
x2.append(Ta[j + IX[k+1]])
ks = ks + n1
break
ks = ks - 1
if(ks < 6):
# Rref.append(Rref[jj])
# RE_limit.append(RE_limit[jj])
Rref.append(Rref[-1])
RE_limit.append(RE_limit[-1])
if(E0_const != True):
stdev_E0.append(stdev_E0[jj])
jj = jj + 1
else:
TC = nparray(x2)
PV = nparray(x3)
betafit = spfmin(Common.Reco2, beta0, args = (TC, PV, E0s), disp=False)
A=betafit[0]
Rref.append(A)
for j in range(len(TC)):
yfit = Rref[jj] * exp(E0s * (1 / (10 + 46.02) - 1 / (TC[j] + 46.02)))
REs.append(PV[j]-yfit)
sz = nparray(REs).shape
upper = abs(Common.tq(gap_limit, sz[0]-1))
RE_limit.append(upper*Common.stdn1(REs)/sqrt(sz[0]))
jj = jj + 1
x2 = []
x3 = []
t1 = []
t2 = []
TC = []
PV = []
#for k in REs:
# print k
REs = []
#for k in Rref:
# print k
##
ks = 0
nsp2 = npzeros((n_L1 / num_point_per_day))
RE = npzeros(n_L1)
GPP = npzeros(n_L1)
for i in range(n_L1):
RE[i] = float('NaN')
GPP[i] = float('NaN')
for i in range(0, n_L1, num_point_per_day):
i0 = i
i1 = i + num_point_per_day
if(i0 >=sday[ks][1]):
ks = ks + 1
if(i0 >= sday[len(sday)-1][1]):
ks = len(sday)-1
for j in range(i0, i1):
if(E0_const == True):
yfit=Rref[ks-1] * exp(E0l * (1.0 / (10 + 46.02) - 1.0 / (Ta[j] + 46.02)))
else:
yfit=Rref[ks-1] * exp(E0s * (1.0 / (10 + 46.02) - 1.0 / (Ta[j] + 46.02)))
RE[j] = yfit
if(itime[j]==0): # nighttime condition
RE[j] = Fc_filled[j]
if((isnan(Fsc[j]) == True) or \
((Fsc[j]-yfit) < RE_limit[ks-1]) or \
((Fsc[j]-yfit) > 1.0 * RE_limit[ks-1]) or \
(iustar[j] == 1)):
nsp2[ks-1] = nsp2[ks-1] + 1
Fc_filled[j] = yfit
RE[j] = Fc_filled[j]
GPP[j] = RE[j]- Fc_filled[j]
#figure(2)
#plot(time][Fsc][time][Fc_filled[:],'or')
#set(gca,'XTick',[year0:1/12:year0+0.9999999])
#set(gca,'xticklabel',xticks)
#ylim = [-1.5][1.5]
#set(gca,'xLim',xlim(:))
#ylabel('F_c (mgm^{-2}s^{-1})')
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
print 'Gap-filling of LE'
#--------------------------------------------------------------------------
x2 = []
x3 = []
index = []
LE_filled = copy.deepcopy(LEsc)
for main_j in range(num_avg): # loop for gap-filling of H2O fluxes
seg_start_i = main_j * num_segment
seg_fin_i = seg_start_i + num_segment
if((seg_start_i + 2 * num_segment) > n_L1):
seg_fin_i = n_L1
x2 = []
x3 = []
for i in range(seg_start_i, seg_fin_i):
ii = 0
if(isnan(LEsc[i]) == True):
jj = 0
while((ii < 1) and (jj <= 4)):
ta_f = Ta[i]
rsdn_f = rsdn[i]
vpd_f = vpd[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day+1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day+1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day - 1
i1 = n_L1
if(i0 < 1):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(vpd_f-vpd[j]) < dvpd) and \
(fabs(rsdn_f-rsdn[j]) < drsdn) and \
(fabs(ta_f-Ta[j]) < dta) and \
(isnan(LEsc[j]) == False)):
x3_temp = []
x2.append(LEsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
x3.append(x3_temp)
ks = ks + 1
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
LE_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
if(ii < 1):
jj = 0
while(ii < 1):
rsdn_f = rsdn[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day + 1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day + 1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day - 1
i1 = n_L1
if(i0 < 1):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(rsdn_f-rsdn[j]) < drsdn) and \
(isnan(LEsc[j]) == False)):
x3_temp = []
x2.append(LEsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
x3.append(x3_temp)
ks = ks + 1
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
LE_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
x2 = []
x3 = []
ks = 0
#figure(3)
#plot(time][LEsc][time][LE_filled[:],'or')
#set(gca,'XTick',[year0:1/12:year0+0.9999999])
#set(gca,'xticklabel',xticks)
#ylim = [-100][600]
#set(gca,'xLim',xlim(:))
#ylabel('LE (Wm^{-2})')
##
#--------------------------------------------------------------------------
print 'Gap-filling of H (sensible heat flux)'
#--------------------------------------------------------------------------
x2 = []
x3 = []
index = []
H_filled = copy.deepcopy(Hsc)
for main_j in range(num_avg): # loop for gap-filling of H2O fluxes
seg_start_i = main_j * num_segment
seg_fin_i = seg_start_i + num_segment
if((seg_start_i + 2 * num_segment) >= n_L1):
seg_fin_i = n_L1
x2 = []
x3 = []
for i in range(seg_start_i, seg_fin_i):
ii = 0
if(isnan(Hsc[i]) == True):
jj = 0
while ((ii < 1) and (jj <= 4)):
ta_f = Ta[i]
rsdn_f = rsdn[i]
vpd_f = vpd[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day+1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day+1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day - 1
i1 = n_L1
if(i0 < 1):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(vpd_f-vpd[j]) < dvpd) and \
(fabs(rsdn_f-rsdn[j]) < drsdn) and \
(fabs(ta_f-Ta[j]) < dta) and \
(isnan(Hsc[j]) == False)):
x3_temp = []
x2.append(Hsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
ks = ks + 1
x3.append(x3_temp)
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
H_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
if(ii < 1):
jj = 0
while(ii < 1):
rsdn_f = rsdn[i]
i0 = i - jj * num_day_2 * num_point_per_day
i1 = i + jj * num_day_2 * num_point_per_day+1
if(i0 < 1):
i0 = 0
i1 = 2 * jj * num_day_2 * num_point_per_day+1
if(i1 >= n_L1):
i0 = n_L1 - 2 * jj * num_day_2 * num_point_per_day - 1
i1 = n_L1
if(i0 < 1):
i0 = 0
ks = 0
for j in range(i0, i1):
if((fabs(rsdn_f-rsdn[j]) < drsdn) and \
(isnan(Hsc[j]) == False)):
ks = ks + 1
x3_temp = []
x2.append(Hsc[j])
x3_temp.append(j)
x3_temp.append(vpd[j])
x3_temp.append(rsdn[j])
x3_temp.append(Ta[j])
x3_temp.append(ks)
x3.append(x3_temp)
ii = ks
#index_temp = []
#index_temp.append(i)
#index_temp.append(i0)
#index_temp.append(i1)
#index_temp.append(ks)
#index_temp.append(main_j)
#index.append(index_temp)
if(ks >= 1):
H_filled[i] = npmedian(nparray(x2))
jj = jj + 1
x2 = []
x3 = []
x2 = []
x3 = []
ks = 0
#figure(4)
#plot(time,Hsc,time,H_filled[:],'or')
#set(gca,'XTick',[year0:1/12:year0+0.9999999])
#set(gca,'xticklabel',xticks)
#ylim = [-100,600]
#set(gca,'xLim',xlim(:))
#ylabel('H (Wm^{-2})')
##
print '-------------------------------------------------------------------'
print 'Calculating daily mean values'
print '-------------------------------------------------------------------'
# disp('Press any key to calculate daily mean values')
# pause
print '-------------------------------------------------------------------'
print 'calculation of daily mean. Unit seg_start_i [C g/m2/day].'
print '-------------------------------------------------------------------'
Fsc_daily = npzeros(n_L1/num_point_per_day)
GPP_daily = npzeros(n_L1/num_point_per_day)
RE_daily = npzeros(n_L1/num_point_per_day)
ET_daily = npzeros(n_L1/num_point_per_day)
H_daily = npzeros(n_L1/num_point_per_day)
LE_daily = npzeros(n_L1/num_point_per_day)
H_daily = npzeros(n_L1/num_point_per_day)
k = 0
for i in range(0, n_L1, num_point_per_day):
for j in range(i,i + num_point_per_day):
Fsc_daily[k] = Fsc_daily[k] + Fc_filled[j]
GPP_daily[k] = GPP_daily[k] + GPP[j]
RE_daily[k] = RE_daily[k] + RE[j]
ET_daily[k] = ET_daily[k] + LE_filled[j]
Fsc_daily[k] = Fsc_daily[k] * (60*float(avgtime)/1000*12/44)
GPP_daily[k] = GPP_daily[k] * (60*float(avgtime)/1000*12/44)
RE_daily[k] = RE_daily[k] * (60*float(avgtime)/1000*12/44)
ET_daily[k] = ET_daily[k] * (60*float(avgtime)/(2440)/1000)
k = k + 1
NEE_annual= npmean(Fc_filled)*float((1800*48*(n_L1/(60.0/avgtime*24))*12/44/1000.0))
GPP_annual = npmean(GPP)*float((1800*48*(n_L1/(60.0/avgtime*24))*12/44/1000.0))
RE_annual = npmean(RE)*float((1800*48*(n_L1/(60.0/avgtime*24))*12/44/1000.0))
NEE_std_annual = Common.stdn1(Fsc_daily)/sqrt(n_L1/(60.0/avgtime*24))*(n_L1/(60.0/avgtime*24))
GPP_std_annual = Common.stdn1(GPP_daily)/sqrt(n_L1/(60.0/avgtime*24))*(n_L1/(60.0/avgtime*24))
RE_std_annual = Common.stdn1(RE_daily)/sqrt(n_L1/(60.0/avgtime*24))*(n_L1/(60.0/avgtime*24))
print 'NEE_annual', NEE_annual
print 'GPP_annual', GPP_annual
print 'RE_annual', RE_annual
print 'NEE_std_annual', NEE_std_annual
print 'GPP_std_annual', GPP_std_annual
print 'RE_std_annual', RE_std_annual
print '-------------------------------------------------------------------'
print 'Calculating daily mean ETs'
print '-------------------------------------------------------------------'
print 'calculation of daily mean. Unit seg_start_i [C g/m2/day].'
print '-------------------------------------------------------------------'
k = 0
for i in range(0, n_L1, num_point_per_day):
for j in range(i,i + num_point_per_day):
LE_daily[k] = LE_daily[k] \
+ LE_filled[j]*(60*float(avgtime)/(2440*1000))
k = k + 1
# npmean(Fc_filled)
LE_annual = npmean(LE_filled)*(1800.0*48.0*float(n_L1/(60.0/avgtime*24))/2440.0/1000.0)
LE_std_annual = Common.stdn1(LE_daily)/sqrt(n_L1/float(60.0/avgtime*24.0))*float(n_L1/(60.0/avgtime*24.0))
print 'LE_annaul', LE_annual
print 'LE_std_annaul', LE_std_annual
print '-------------------------------------------------------------------'
print 'Calculating daily npmean heating rate'
print '-------------------------------------------------------------------'
print 'calculation of daily npmean heating rate. Unit seg_start_i [MJ/m2/day].'
print '-------------------------------------------------------------------'
k = 0
for i in range(0, n_L1, num_point_per_day):
for j in range(i,i + num_point_per_day):
H_daily[k] = H_daily[k] \
+ H_filled[j]*(60*float(avgtime)/(1004*1.0))/(10E6)
k = k + 1
H_annual = sum(H_daily)
H_std_annual = Common.stdn1(H_daily)
print 'H_annaul', H_annual
print 'H_std_annaul', H_std_annual
for i in range(len(Fsc)):
file_plot_str = StringIO()
file_plot_str.write(str(date[i]) + ',') #1
file_plot_str.write(str(Fsc[i]) + ',') #2
file_plot_str.write(str(Fc_filled[i]) + ',') #3
file_plot_str.write(str(LEsc[i]) + ',') #4
file_plot_str.write(str(LE_filled[i]) + ',') #5
file_plot_str.write(str(Hsc[i]) + ',') #6
file_plot_str.write(str(H_filled[i]) + '\n') #7
output_plot_string = file_plot_str.getvalue()
output_plot_2_fp.write(output_plot_string)
output_plot_2_fp.close()
#For output
#Assume data start from 0:00
output_Fsc_daily = npzeros(n_L1)
output_GPP_daily = npzeros(n_L1)
output_RE_daily = npzeros(n_L1)
output_ET_daily = npzeros(n_L1)
output_LE_daily = npzeros(n_L1)
output_H_daily = npzeros(n_L1)
j = 0
for i in range(n_L1):
output_Fsc_daily[i] = Fsc_daily[j]
output_GPP_daily[i] = GPP_daily[j]
output_RE_daily[i] = RE_daily[j]
output_ET_daily[i] = ET_daily[j]
output_H_daily[i] = H_daily[j]
output_LE_daily[i] = LE_daily[j]
if((i+1) % num_point_per_day == 0):
j = j + 1
for i in range(n_L1):
file_str = StringIO()
file_str.write(str(output_ET_daily[i]) + ',') #1
file_str.write(str(Fc_filled[i]) + ',' ) #2
file_str.write(str(output_Fsc_daily[i]) + ',' ) #3
file_str.write(str(GPP[i]) + ',' ) #4
file_str.write(str(output_GPP_daily[i]) + ',') #5
file_str.write(str(GPP_annual) + ',') #6
file_str.write(str(GPP_std_annual) + ',') #7
file_str.write(str(H_filled[i]) + ',') #8
file_str.write(str(output_H_daily[i]) + ',') #9
file_str.write(str(H_annual) + ',') #10
file_str.write(str(H_std_annual) + ',') #11
file_str.write(str(LE_filled[i]) + ',') #12
file_str.write(str(output_LE_daily[i]) + ',') #13
file_str.write(str(LE_annual) + ',') #14
file_str.write(str(LE_std_annual) + ',') #15
file_str.write(str(NEE_annual) + ',') #16
file_str.write(str(NEE_std_annual) + ',') #17
file_str.write(str(output_RE_daily[i]) + ',') #18
file_str.write(str(RE_annual) + ',') #19
file_str.write(str(RE_std_annual) + ',') #20
file_str.write(str(co2[i]) + ',') #21
file_str.write(str(rsdn[i]) + ',') #22
file_str.write(str(ea[i]) + ',') #23
file_str.write(str(h2o[i]) + ',') #24
file_str.write(str(Ta[i]) + ',') #25
file_str.write(str(vpd[i]) + '\n' ) #26
output_string = file_str.getvalue()
output_fp.write(output_string)
output_fp.close()
return 'L2 Done'
