def _operation_with_other_lc(self, other, operation):
"""
Helper method to codify an operation of one light curve with another (e.g. add, subtract, ...).
Takes into account the GTIs correctly, and returns a new :class:`Lightcurve` object.
Parameters
----------
other : :class:`Lightcurve` object
A second light curve object
operation : function
An operation between the :class:`Lightcurve` object calling this method, and ``other``,
operating on the ``counts`` attribute in each :class:`Lightcurve` object
Returns
-------
lc_new : Lightcurve object
The new light curve calculated in ``operation``
"""
if self.mjdref != other.mjdref:
raise ValueError("MJDref is different in the two light curves")
common_gti = cross_two_gtis(self.gti, other.gti)
mask_self = create_gti_mask(self.time, common_gti, dt=self.dt)
mask_other = create_gti_mask(other.time, common_gti, dt=other.dt)
# ValueError is raised by Numpy while asserting np.equal over arrays
# with different dimensions.
try:
diff = np.abs((self.time[mask_self] - other.time[mask_other]))
assert np.all(diff < self.dt / 100)
except (ValueError, AssertionError):
raise ValueError("GTI-filtered time arrays of both light curves "
"must be of same dimension and equal.")
new_time = self.time[mask_self]
new_counts = operation(self.counts[mask_self],
other.counts[mask_other])
if self.err_dist.lower() != other.err_dist.lower():
simon("Lightcurves have different statistics!"
"We are setting the errors to zero to avoid complications.")
new_counts_err = np.zeros_like(new_counts)
elif self.err_dist.lower() in valid_statistics:
new_counts_err = \
np.sqrt(np.add(self.counts_err[mask_self]**2,
other.counts_err[mask_other]**2))
# More conditions can be implemented for other statistics
else:
raise StingrayError("Statistics not recognized."
" Please use one of these: "
"{}".format(valid_statistics))
lc_new = Lightcurve(new_time,
new_counts,
err=new_counts_err,
gti=common_gti,
mjdref=self.mjdref)
return lc_new
def _operation_with_other_lc(self, other, operation):
"""
Helper method to codify an operation of one light curve with another (e.g. add, subtract, ...).
Takes into account the GTIs correctly, and returns a new :class:`Lightcurve` object.
Parameters
----------
other : :class:`Lightcurve` object
A second light curve object
operation : function
An operation between the :class:`Lightcurve` object calling this method, and ``other``,
operating on the ``counts`` attribute in each :class:`Lightcurve` object
Returns
-------
lc_new : Lightcurve object
The new light curve calculated in ``operation``
"""
if self.mjdref != other.mjdref:
raise ValueError("MJDref is different in the two light curves")
common_gti = cross_two_gtis(self.gti, other.gti)
mask_self = create_gti_mask(self.time, common_gti, dt=self.dt)
mask_other = create_gti_mask(other.time, common_gti, dt=other.dt)
# ValueError is raised by Numpy while asserting np.equal over arrays
# with different dimensions.
try:
diff = np.abs((self.time[mask_self] - other.time[mask_other]))
assert np.all(diff < self.dt / 100)
except (ValueError, AssertionError):
raise ValueError("GTI-filtered time arrays of both light curves "
"must be of same dimension and equal.")
new_time = self.time[mask_self]
new_counts = operation(self.counts[mask_self],
other.counts[mask_other])
if self.err_dist.lower() != other.err_dist.lower():
simon("Lightcurves have different statistics!"
"We are setting the errors to zero to avoid complications.")
new_counts_err = np.zeros_like(new_counts)
elif self.err_dist.lower() in valid_statistics:
new_counts_err = \
np.sqrt(np.add(self.counts_err[mask_self]**2,
other.counts_err[mask_other]**2))
# More conditions can be implemented for other statistics
else:
raise StingrayError("Statistics not recognized."
" Please use one of these: "
"{}".format(valid_statistics))
lc_new = Lightcurve(new_time, new_counts,
err=new_counts_err, gti=common_gti,
mjdref=self.mjdref)
return lc_new
def filter_lc_gtis(lc,
safe_interval=None,
delete=False,
min_length=0,
return_borders=False):
"""Filter a light curve for GTIs.
Parameters
----------
lc : :class:`Lightcurve` object
The input light curve
Returns
-------
newlc : :class:`Lightcurve` object
The output light curve
borders : [[i0_0, i0_1], [i1_0, i1_1], ...], optional
The indexes of the light curve corresponding to the borders of the
GTIs. Returned if return_borders is set to True
Other Parameters
----------------
safe_interval : float or [float, float]
Seconds to filter out at the start and end of each GTI. If single
float, these safe windows are equal, otherwise the two numbers refer
to the start and end of the GTI respectively
delete : bool
If delete is True, the intervals outside of GTIs are filtered out from
the light curve. Otherwise, they are set to zero.
min_length : float
Minimum length of GTI. GTIs below this length will be removed.
return_borders : bool
If True, return also the indexes of the light curve corresponding to
the borders of the GTIs
"""
mask, newgtis = create_gti_mask(lc.time,
lc.gti,
return_new_gtis=True,
safe_interval=safe_interval,
min_length=min_length)
nomask = np.logical_not(mask)
newlc = copy.copy(lc)
newlc.counts[nomask] = 0
newlc.gti = newgtis
if return_borders:
mask = create_gti_mask(lc.time, newgtis)
borders = contiguous_regions(mask)
return newlc, borders
else:
return newlc
def baseline(self, lam, p, niter=10, offset_correction=False):
"""Calculate the baseline of the light curve, accounting for GTIs.
Parameters
----------
lam : float
"smoothness" parameter. Larger values make the baseline stiffer
Typically ``1e2 < lam < 1e9``
p : float
"asymmetry" parameter. Smaller values make the baseline more
"horizontal". Typically ``0.001 < p < 0.1``, but not necessary.
Other parameters
----------------
offset_correction : bool, default False
by default, this method does not align to the running mean of the
light curve, but it goes below the light curve. Setting align to
True, an additional step is done to shift the baseline so that it
is shifted to the middle of the light curve noise distribution.
Returns
-------
baseline : numpy.ndarray
An array with the baseline of the light curve
"""
baseline = np.zeros_like(self.time)
for g in self.gti:
good = create_gti_mask(self.time, [g], dt=self.dt)
_, baseline[good] = \
baseline_als(self.time[good], self.counts[good], lam, p,
niter, offset_correction=offset_correction,
return_baseline=True)
return baseline
def test_gti_mask(self):
arr = np.array([0, 1, 2, 3, 4, 5, 6])
gti = np.array([[0, 2.1], [3.9, 5]])
mask, new_gtis = create_gti_mask(arr, gti, return_new_gtis=True)
# NOTE: the time bin has to be fully inside the GTI. That is why the
# bin at times 0, 2, 4 and 5 are not in.
assert np.allclose(mask, np.array([0, 1, 0, 0, 0, 0, 0], dtype=bool))
def baseline(self, lam, p, niter=10, offset_correction=False):
"""Calculate the baseline of the light curve, accounting for GTIs.
Parameters
----------
lam : float
"smoothness" parameter. Larger values make the baseline stiffer
Typically ``1e2 < lam < 1e9``
p : float
"asymmetry" parameter. Smaller values make the baseline more
"horizontal". Typically ``0.001 < p < 0.1``, but not necessary.
Other parameters
----------------
offset_correction : bool, default False
by default, this method does not align to the running mean of the
light curve, but it goes below the light curve. Setting align to
True, an additional step is done to shift the baseline so that it
is shifted to the middle of the light curve noise distribution.
Returns
-------
baseline : numpy.ndarray
An array with the baseline of the light curve
"""
baseline = np.zeros_like(self.time)
for g in self.gti:
good = create_gti_mask(self.time, [g], dt=self.dt)
_, baseline[good] = \
baseline_als(self.time[good], self.counts[good], lam, p,
niter, offset_correction=offset_correction,
return_baseline=True)
return baseline
def test_gti_mask_none_longer_than_minlen(self):
arr = np.array([0, 1, 2, 3, 4, 5, 6])
gti = np.array([[0, 2.1], [3.9, 5]])
with pytest.warns(UserWarning) as record:
mask = create_gti_mask(arr, gti, min_length=10)
assert np.any(
["No GTIs longer than" in r.message.args[0] for r in record])
assert np.all(~mask)
def plot_lc(lcfiles,
figname=None,
fromstart=False,
xlog=None,
ylog=None,
output_data_file=None):
"""Plot a list of light curve files, or a single one."""
if is_string(lcfiles):
lcfiles = [lcfiles]
figlabel = lcfiles[0]
plt.figure('LC ' + figlabel)
for lcfile in lcfiles:
logging.info('Loading %s...' % lcfile)
lcdata = load_data(lcfile)
time = lcdata['time']
lc = lcdata['counts']
gti = lcdata['gti']
instr = lcdata['instr']
if fromstart:
time -= lcdata['Tstart']
gti -= lcdata['Tstart']
if instr == 'PCA':
# If RXTE, plot per PCU count rate
npcus = lcdata['nPCUs']
lc /= npcus
for g in gti:
plt.axvline(g[0], ls='-', color='red')
plt.axvline(g[1], ls='--', color='red')
good = create_gti_mask(time, gti)
plt.plot(time, lc, drawstyle='steps-mid', color='grey')
plt.plot(time[good], lc[good], drawstyle='steps-mid', label=lcfile)
if 'base' in lcdata:
plt.plot(time, lcdata['base'], color='r')
if output_data_file is not None:
outqdpdata = [time[good], lc[good]]
if 'base' in lcdata:
outqdpdata.append(lcdata['base'][good])
save_as_qdp(outqdpdata, filename=output_data_file, mode='a')
plt.xlabel('Time (s)')
if instr == 'PCA':
plt.ylabel('light curve (Ct/bin/PCU)')
else:
plt.ylabel('light curve (Ct/bin)')
plt.legend()
if figname is not None:
plt.savefig(figname)
def test_lc_baseline(self):
times = np.arange(0, 100, 0.01)
counts = np.random.normal(100, 0.1, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01)
assert np.all(lc.counts - baseline < 1)
def test_lc_baseline(self):
times = np.arange(0, 100, 0.01)
counts = np.random.normal(100, 0.1, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01)
assert np.all(lc.counts - baseline < 1)
def test_gti_mask_compare2(self):
arr = np.array([0.5, 1.5, 2.5, 3.5])
gti = np.array([[0, 4]])
mask_c, new_gtis_c = \
create_gti_mask_complete(arr, gti, return_new_gtis=True,
safe_interval=[1, 1])
mask, new_gtis = create_gti_mask(arr, gti, return_new_gtis=True,
safe_interval=[1, 1])
assert np.allclose(mask, mask_c)
assert np.allclose(new_gtis, new_gtis_c)
def _operation_with_other_lc(self, other, operation):
if self.mjdref != other.mjdref:
raise ValueError("MJDref is different in the two light curves")
common_gti = cross_two_gtis(self.gti, other.gti)
mask_self = create_gti_mask(self.time, common_gti)
mask_other = create_gti_mask(other.time, common_gti)
# ValueError is raised by Numpy while asserting np.equal over arrays
# with different dimensions.
try:
assert np.all(
np.equal(self.time[mask_self], other.time[mask_other]))
except (ValueError, AssertionError):
raise ValueError("GTI-filtered time arrays of both light curves "
"must be of same dimension and equal.")
new_time = self.time[mask_self]
new_counts = operation(self.counts[mask_self],
other.counts[mask_other])
if self.err_dist.lower() != other.err_dist.lower():
simon("Lightcurves have different statistics!"
"We are setting the errors to zero to avoid complications.")
new_counts_err = np.zeros_like(new_counts)
elif self.err_dist.lower() in valid_statistics:
new_counts_err = np.sqrt(
np.add(self.counts_err[mask_self]**2,
other.counts_err[mask_other]**2))
# More conditions can be implemented for other statistics
else:
raise StingrayError("Statistics not recognized."
" Please use one of these: "
"{}".format(valid_statistics))
lc_new = Lightcurve(new_time,
new_counts,
err=new_counts_err,
gti=common_gti,
mjdref=self.mjdref)
return lc_new
def test_lc_baseline_offset(self):
times = np.arange(0, 100, 0.01)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01, offset_correction=True)
assert np.isclose(np.std(lc.counts - baseline), input_stdev, rtol=0.1)
def _plot_corrected_light_curve(time, lc, expo, gti=None, outroot="expo"):
import matplotlib.pyplot as plt
good = create_gti_mask(time, gti)
fig = plt.figure("Exposure-corrected lc")
plt.plot(time[good], expo[good] / np.max(expo) * np.max(lc[good]),
label="Exposure (arbitrary units)", zorder=10)
plt.plot(time[good], lc[good], label="Light curve", zorder=20)
plt.plot(time[good], lc[good] / expo[good],
label="Exposure-corrected Light curve")
plt.legend()
fig.savefig(outroot + "_corr_lc.png")
def test_lc_baseline_offset(self):
times = np.arange(0, 100, 0.01)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01, offset_correction=True)
assert np.isclose(np.std(lc.counts - baseline), input_stdev, rtol=0.1)
def test_gti_mask_compare(self):
arr = np.array([0.5, 1.5, 2.5, 3.5])
gti = np.array([[0, 4]])
mask_c, new_gtis_c = \
create_gti_mask_complete(arr, gti, return_new_gtis=True,
safe_interval=1)
mask, new_gtis = create_gti_mask(arr,
gti,
return_new_gtis=True,
safe_interval=1)
assert np.all(mask == mask_c)
assert np.all(new_gtis == new_gtis_c)
def _operation_with_other_lc(self, other, operation):
if self.mjdref != other.mjdref:
raise ValueError("MJDref is different in the two light curves")
common_gti = cross_two_gtis(self.gti, other.gti)
mask_self = create_gti_mask(self.time, common_gti)
mask_other = create_gti_mask(other.time, common_gti)
# ValueError is raised by Numpy while asserting np.equal over arrays
# with different dimensions.
try:
assert np.all(np.equal(self.time[mask_self],
other.time[mask_other]))
except (ValueError, AssertionError):
raise ValueError("GTI-filtered time arrays of both light curves "
"must be of same dimension and equal.")
new_time = self.time[mask_self]
new_counts = operation(self.counts[mask_self],
other.counts[mask_other])
if self.err_dist.lower() != other.err_dist.lower():
simon("Lightcurves have different statistics!"
"We are setting the errors to zero to avoid complications.")
new_counts_err = np.zeros_like(new_counts)
elif self.err_dist.lower() in valid_statistics:
new_counts_err = np.sqrt(np.add(self.counts_err[mask_self]**2,
other.counts_err[mask_other]**2))
# More conditions can be implemented for other statistics
else:
raise StingrayError("Statistics not recognized."
" Please use one of these: "
"{}".format(valid_statistics))
lc_new = Lightcurve(new_time, new_counts,
err=new_counts_err, gti=common_gti,
mjdref=self.mjdref)
return lc_new
def test_rebin_with_gtis(self):
times = np.arange(0, 100, 0.1)
counts = np.random.normal(100, 0.1, size=times.shape[0])
gti = [[0, 40], [60, 100]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
lc_rebin = lc.rebin(1.0)
assert (lc_rebin.time[39] - lc_rebin.time[38]) > 1.0
def test_rebin_with_gtis(self):
times = np.arange(0, 100, 0.1)
counts = np.random.normal(100, 0.1, size=times.shape[0])
gti = [[0, 40], [60, 100]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
lc_rebin = lc.rebin(1.0)
assert (lc_rebin.time[39] - lc_rebin.time[38]) > 1.0
def _apply_gtis(self):
"""Apply GTIs to a light curve after modification."""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def baseline(self, lam, p, niter=10):
"""Calculate the baseline of the light curve, accounting for GTIs.
Parameters
----------
lam : float
"smoothness" parameter. Larger values make the baseline stiffer
Typically 1e2 < lam < 1e9
p : float
"asymmetry" parameter. Smaller values make the baseline more
"horizontal". Typically 0.001 < p < 0.1, but not necessary.
"""
baseline = np.zeros_like(self.time)
for g in self.gti:
good = create_gti_mask(self.time, [g])
baseline[good] = baseline_als(self.counts[good], lam, p, niter)
return baseline
def _apply_gtis(self):
"""
Apply GTIs to a light curve. Filters the ``time``, ``counts``, ``countrate``, ``counts_err`` and
``countrate_err`` arrays for all bins that fall into Good Time Intervals and recalculates mean
count(rate) and the number of bins.
"""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def _apply_gtis(self):
"""
Apply GTIs to a light curve. Filters the ``time``, ``counts``, ``countrate``, ``counts_err`` and
``countrate_err`` arrays for all bins that fall into Good Time Intervals and recalculates mean
count(rate) and the number of bins.
"""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def apply_gti(fname, gti, outname=None, minimum_length=0):
"""Apply a GTI list to the data contained in a file.
File MUST have a GTI extension already, and an extension called `time`.
"""
ftype, data = get_file_type(fname, raw_data=True)
try:
datagti = data['gti']
newgtis = cross_gtis([gti, datagti])
except: # pragma: no cover
logging.warning('Data have no GTI extension')
newgtis = gti
newgtis = filter_gti_by_length(newgtis, minimum_length)
data['__sr__class__type__'] = 'gti'
data['gti'] = newgtis
good = create_gti_mask(data['time'], newgtis)
data['time'] = data['time'][good]
if ftype == 'lc':
data['counts'] = data['counts'][good]
data['counts_err'] = data['counts_err'][good]
elif ftype == 'events':
data['PI'] = data['PI'][good]
if data['instr'] == 'PCA': # pragma: no cover
data['PCU'] = data['PCU'][good]
newext = '_gtifilt' + HEN_FILE_EXTENSION
outname = _assign_value_if_none(
outname,
fname.replace(HEN_FILE_EXTENSION, '') + newext)
save_data(data, outname)
return newgtis
def test_gti_mask_fails_empty_gti(self):
arr = np.array([0, 1, 2, 3, 4, 5, 6])
gti = np.array([])
with pytest.raises(ValueError) as excinfo:
create_gti_mask(arr, gti, return_new_gtis=True)
assert 'empty GTI array' in str(excinfo.value)
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
"""
Make a light curve object from an array of time stamps and an
array of counts.
Parameters
----------
time: iterable
A list or array of time stamps for a light curve
counts: iterable, optional, default None
A list or array of the counts in each bin corresponding to the
bins defined in `time` (note: use `input_counts=False` to
input the count range, i.e. counts/second, otherwise use
counts/bin).
err: iterable, optional, default None:
A list or array of the uncertainties in each bin corresponding to
the bins defined in `time` (note: use `input_counts=False` to
input the count rage, i.e. counts/second, otherwise use
counts/bin). If None, we assume the data is poisson distributed
and calculate the error from the average of the lower and upper
1-sigma confidence intervals for the Poissonian distribution with
mean equal to `counts`.
input_counts: bool, optional, default True
If True, the code assumes that the input data in 'counts'
is in units of counts/bin. If False, it assumes the data
in 'counts' is in counts/second.
gti: 2-d float array, default None
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They are *not* applied to the data by default.
They will be used by other methods to have an indication of the
"safe" time intervals to use during analysis.
err_dist: str, optional, default=None
Statistic of the Lightcurve, it is used to calculate the
uncertainties and other statistical values apropriately.
Default makes no assumptions and keep errors equal to zero.
mjdref: float
MJD reference (useful in most high-energy mission data)
Attributes
----------
time: numpy.ndarray
The array of midpoints of time bins.
bin_lo:
The array of lower time stamp of time bins.
bin_hi:
The array of higher time stamp of time bins.
counts: numpy.ndarray
The counts per bin corresponding to the bins in `time`.
counts_err: numpy.ndarray
The uncertainties corresponding to `counts`
countrate: numpy.ndarray
The counts per second in each of the bins defined in `time`.
countrate_err: numpy.ndarray
The uncertainties corresponding to `countrate`
meanrate: float
The mean count rate of the light curve.
meancounts: float
The mean counts of the light curve.
n: int
The number of data points in the light curve.
dt: float
The time resolution of the light curve.
mjdref: float
MJD reference date (tstart / 86400 gives the date in MJD at the
start of the observation)
tseg: float
The total duration of the light curve.
tstart: float
The start time of the light curve.
gti: 2-d float array
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They indicate the "safe" time intervals
to be used during the analysis of the light curve.
err_dist: string
Statistic of the Lightcurve, it is used to calculate the
uncertainties and other statistical values apropriately.
It propagates to Spectrum classes.
"""
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err_low, err_high = poisson_conf_interval(np.asarray(counts),
interval='frequentist-confidence', sigma=1)
# calculate approximately symmetric uncertainties
err_low -= np.asarray(counts)
err_high -= np.asarray(counts)
err = (np.absolute(err_low) + np.absolute(err_high))/2.0
# other estimators can be implemented for other statistics
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
if dt is None:
self.dt = np.median(self.time[1:] - self.time[:-1])
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
self.tstart = self.time[0] - 0.5*self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = np.diff(self.time)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err = poisson_symmetrical_errors(counts)
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
dt_array = np.diff(np.sort(self.time))
dt_array_unsorted = np.diff(self.time)
unsorted = np.any(dt_array_unsorted < 0)
if dt is None:
if unsorted:
logging.warning("The light curve is unordered! This may cause "
"unexpected behaviour in some methods! Use "
"sort() to order the light curve in time and "
"check that the time resolution `dt` is "
"calculated correctly!")
self.dt = np.median(dt_array)
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
if unsorted:
self.tstart = np.min(self.time) - 0.5 * self.dt
self.tseg = np.max(self.time) - np.min(self.time) + self.dt
else:
self.tstart = self.time[0] - 0.5 * self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = []
for g in self.gti:
mask = create_gti_mask(self.time, [g], dt=self.dt)
t = self.time[mask]
dt_array.extend(np.diff(t))
dt_array = np.asarray(dt_array)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err = poisson_symmetrical_errors(counts)
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
dt_array = np.diff(np.sort(self.time))
dt_array_unsorted = np.diff(self.time)
unsorted = np.any(dt_array_unsorted < 0)
if dt is None:
if unsorted:
logging.warning("The light curve is unordered! This may cause "
"unexpected behaviour in some methods! Use "
"sort() to order the light curve in time and "
"check that the time resolution `dt` is "
"calculated correctly!")
self.dt = np.median(dt_array)
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
if unsorted:
self.tstart = np.min(self.time) - 0.5 * self.dt
self.tseg = np.max(self.time) - np.min(self.time) + self.dt
else:
self.tstart = self.time[0] - 0.5 * self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = []
for g in self.gti:
mask = create_gti_mask(self.time, [g], dt=self.dt)
t = self.time[mask]
dt_array.extend(np.diff(t))
dt_array = np.asarray(dt_array)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def test_gti_mask_fails_empty_time(self):
arr = np.array([])
gti = np.array([[0, 2.1], [3.9, 5]])
with pytest.raises(ValueError) as excinfo:
create_gti_mask(arr, gti, return_new_gtis=True)
assert 'empty time array' in str(excinfo.value)
def apply_gtis(self):
good = create_gti_mask(self.time, lc.gti)