Repository: StingraySoftware/stingray

Issue:
Bug in `Powerspectrum` when GTI is shorter than light curve?
I have a data set where the GTIs are slightly shorter than the data in the FITS file. When I create a light curve and then try to use `Powerspectrum`, I get an AssertionError that no GTIs are longer than `segment_size`. I think this is a bug where `Powerspectrum` internally sets `segment_size` to whatever the length of the data is, and when that's passed on to the code that checks the GTIs, it finds no GTI that is the length of the data. I think this should internally set the segment_size to the length of the GTI, rather than fail. 

Minimum example:
```
import numpy as np
from stingray import Lightcurve, Powerspectrum

tstart = 1.0
tend = 10.0
gti = [[1.0, 9.0]]

time = np.linspace(tstart, tend, 10000)
counts = np.random.poisson(10, size=time.shape[0])

lc = Lightcurve(time, counts, gti=gti)
ps = Powerspectrum(lc, norm="leahy")
```

Last part of output:
```
File /opt/miniconda3/envs/qpogp/lib/python3.9/site-packages/stingray-1.0-py3.9.egg/stingray/gti.py:1257, in bin_intervals_from_gtis(gtis, segment_size, time, dt, fraction_step, epsilon)
   1252     newbins = calculate_segment_bin_start(
   1253         startbin, stopbin, nbin, fraction_step=fraction_step)
   1254     spectrum_start_bins = \
   1255         np.append(spectrum_start_bins,
   1256                   newbins)
-> 1257 assert len(spectrum_start_bins) > 0, \
   1258     ("No GTIs are equal to or longer than segment_size.")
   1259 return spectrum_start_bins, spectrum_start_bins + nbin

AssertionError: No GTIs are equal to or longer than segment_size.
```

Discussion:
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> Comparison is base [(`5285c23`)](https://app.codecov.io/gh/StingraySoftware/stingray/commit/5285c23221ed81c946007e8ae116c84fdc9947d4?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=StingraySoftware) 96.35% compared to head [(`79fd5ff`)](https://app.codecov.io/gh/StingraySoftware/stingray/pull/797?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=StingraySoftware) 78.95%.


<details><summary>Additional details and impacted files</summary>


```diff
@@             Coverage Diff             @@
##             main     #797       +/-   ##
===========================================
- Coverage   96.35%   78.95%   -17.41%     
===========================================
  Files          43       43               
  Lines        8784     8786        +2     
===========================================
- Hits         8464     6937     -1527     
- Misses        320     1849     +1529     
```



</details>

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Patch:
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 7c1a92c1..ecf83e1e 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -32,7 +32,7 @@ Bug Fixes
 - Various bug fixes in DynamicalPowerspectrum, on event loading and time rebinning (`#779 <https://github.com/StingraySoftware/stingray/pull/779>`__)
 - Fix issue with the Poisson noise calculation in lag spectra, that produced NaN errors under some conditions (`#789 <https://github.com/StingraySoftware/stingray/pull/789>`__)
 - Fix rms computation and error bars (`#792 <https://github.com/StingraySoftware/stingray/pull/792>`__)
-
+- Fix issue with ``Powerspectrum`` of a single light curve (`#663 <https://github.com/StingraySoftware/stingray/pull/663>`__)
 
 Breaking Changes
 ^^^^^^^^^^^^^^^^
diff --git a/docs/notebooks b/docs/notebooks
index 44828775..ccc49119 160000
--- a/docs/notebooks
+++ b/docs/notebooks
@@ -1,1 +1,1 @@
-Subproject commit 448287755a373496cd5f81ae6ee904d3de0f4561
+Subproject commit ccc491197b11b018304ca4397af7968a6111a2bb
diff --git a/setup.cfg b/setup.cfg
index e351c44c..82852c01 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -115,6 +115,7 @@ filterwarnings =
     ignore:.*is_categorical_dtype is deprecated.*:DeprecationWarning
     ignore:.*datetime.datetime.utcfromtimestamp.*:DeprecationWarning
     ignore:.*__array_wrap__ must accept context and return_scalar arguments:DeprecationWarning
+    ignore:.*Pyarrow:
 
 ;addopts = --disable-warnings
 
diff --git a/stingray/crossspectrum.py b/stingray/crossspectrum.py
index 75d40950..5a6d8486 100644
--- a/stingray/crossspectrum.py
+++ b/stingray/crossspectrum.py
@@ -16,7 +16,7 @@ from .events import EventList
 from .gti import cross_two_gtis, time_intervals_from_gtis
 from .lightcurve import Lightcurve
 from .fourier import avg_cs_from_iterables, error_on_averaged_cross_spectrum
-from .fourier import avg_cs_from_events, poisson_level
+from .fourier import avg_cs_from_timeseries, poisson_level
 from .fourier import normalize_periodograms, raw_coherence
 from .fourier import get_flux_iterable_from_segments, power_color
 from .fourier import get_rms_from_unnorm_periodogram
@@ -1566,6 +1566,27 @@ class Crossspectrum(StingrayObject):
         ``crossspectrum_from_XXXX`` function, and initialize ``self`` with
         the correct attributes.
         """
+        if segment_size is None and isinstance(data1, StingrayObject):
+            common_gti = cross_two_gtis(data1.gti, data2.gti)
+            data1.gti = common_gti
+            data2.gti = common_gti
+            data1 = data1.apply_gtis(inplace=False)
+            data2 = data2.apply_gtis(inplace=False)
+
+            data1_is_binned = (
+                "counts" in data1.array_attrs() or "_counts" in data1.internal_array_attrs()
+            )
+            data2_is_binned = (
+                "counts" in data2.array_attrs() or "_counts" in data2.internal_array_attrs()
+            )
+
+            if data1_is_binned and data2_is_binned:
+                assert data2.time.size == data1.time.size and np.allclose(
+                    data2.time - data1.time, 0
+                ), "Time arrays are not the same"
+            elif data1_is_binned or data2_is_binned:
+                raise ValueError("Please use input data of the same kind")
+
         if isinstance(data1, EventList):
             spec = crossspectrum_from_events(
                 data1,
@@ -2444,7 +2465,7 @@ def crossspectrum_from_time_array(
     force_averaged = segment_size is not None
     # Suppress progress bar for single periodogram
     silent = silent or (segment_size is None)
-    results = avg_cs_from_events(
+    results = avg_cs_from_timeseries(
         times1,
         times2,
         gti,
@@ -2684,7 +2705,7 @@ def crossspectrum_from_timeseries(
         err1 = getattr(ts1, error_flux_attr)
         err2 = getattr(ts2, error_flux_attr)
 
-    results = avg_cs_from_events(
+    results = avg_cs_from_timeseries(
         ts1.time,
         ts2.time,
         gti,
diff --git a/stingray/fourier.py b/stingray/fourier.py
index 52d3e046..ce0b2943 100644
--- a/stingray/fourier.py
+++ b/stingray/fourier.py
@@ -12,7 +12,14 @@ from .gti import (
     generate_indices_of_segment_boundaries_binned,
     generate_indices_of_segment_boundaries_unbinned,
 )
-from .utils import fft, fftfreq, histogram, show_progress, sum_if_not_none_or_initialize
+from .utils import (
+    fft,
+    fftfreq,
+    histogram,
+    show_progress,
+    sum_if_not_none_or_initialize,
+    fix_segment_size_to_integer_samples,
+)
 
 
 def integrate_power_in_frequency_range(
@@ -1497,7 +1504,7 @@ def get_flux_iterable_from_segments(
     gti : [[gti00, gti01], [gti10, gti11], ...]
         good time intervals
     segment_size : float
-        length of segments
+        length of segments. If ``None``, the full light curve is used.
 
     Other parameters
     ----------------
@@ -1527,12 +1534,20 @@ def get_flux_iterable_from_segments(
     cast_kind = float
     if dt is None and binned:
         dt = np.median(np.diff(times[:100]))
+
     if binned:
         fluxes = np.asarray(fluxes)
         if np.iscomplexobj(fluxes):
             cast_kind = complex
 
-    fun = _which_segment_idx_fun(binned, dt)
+    if segment_size is None:
+        segment_size = gti[-1, 1] - gti[0, 0]
+
+        def fun(times, gti, segment_size):
+            return [[gti[0, 0], gti[-1, 1], 0, times.size]]
+
+    else:
+        fun = _which_segment_idx_fun(binned, dt)
 
     for s, e, idx0, idx1 in fun(times, gti, segment_size):
         if idx1 - idx0 < 2:
@@ -2228,7 +2243,15 @@ def avg_cs_from_iterables(
     return results
 
 
-def avg_pds_from_events(
+def avg_pds_from_events(*args, **kwargs):
+    warnings.warn(
+        "avg_pds_from_events is deprecated, use avg_cs_from_timeseries instead", DeprecationWarning
+    )
+
+    return avg_pds_from_timeseries(*args, **kwargs)
+
+
+def avg_pds_from_timeseries(
     times,
     gti,
     segment_size,
@@ -2256,7 +2279,7 @@ def avg_pds_from_events(
     gti : [[gti00, gti01], [gti10, gti11], ...]
         Good time intervals.
     segment_size : float
-        Length of segments.
+        Length of segments. If ``None``, the full light curve is used.
     dt : float
         Time resolution of the light curves used to produce periodograms.
 
@@ -2294,13 +2317,14 @@ def avg_pds_from_events(
     mean : float
         the mean flux
     """
-    if segment_size is None:
-        segment_size = gti.max() - gti.min()
-    n_bin = int(segment_size / dt)
-    if fluxes is None:
-        dt = segment_size / n_bin
+    binned = fluxes is not None
+    if segment_size is not None:
+        segment_size, n_bin = fix_segment_size_to_integer_samples(segment_size, dt)
+    elif binned and segment_size is None:
+        n_bin = fluxes.size
     else:
-        segment_size = n_bin * dt
+        _, n_bin = fix_segment_size_to_integer_samples(gti.max() - gti.min(), dt)
+
     flux_iterable = get_flux_iterable_from_segments(
         times, gti, segment_size, n_bin, dt=dt, fluxes=fluxes, errors=errors
     )
@@ -2317,7 +2341,14 @@ def avg_pds_from_events(
     return cross
 
 
-def avg_cs_from_events(
+def avg_cs_from_events(*args, **kwargs):
+    warnings.warn(
+        "avg_cs_from_events is deprecated, use avg_cs_from_timeseries instead", DeprecationWarning
+    )
+    return avg_cs_from_timeseries(*args, **kwargs)
+
+
+def avg_cs_from_timeseries(
     times1,
     times2,
     gti,
@@ -2353,7 +2384,7 @@ def avg_cs_from_events(
     gti : [[gti00, gti01], [gti10, gti11], ...]
         common good time intervals
     segment_size : float
-        length of segments
+        length of segments. If ``None``, the full light curve is used.
     dt : float
         Time resolution of the light curves used to produce periodograms
 
@@ -2398,20 +2429,21 @@ def avg_cs_from_events(
     n_ave : int
         the number of averaged periodograms
     """
-    if segment_size is None:
-        segment_size = gti.max() - gti.min()
-    n_bin = int(segment_size / dt)
-    # adjust dt
-    # dt = segment_size / n_bin
-    if fluxes1 is None and fluxes2 is None:
-        dt = segment_size / n_bin
+
+    binned = fluxes1 is not None and fluxes2 is not None
+
+    if segment_size is not None:
+        segment_size, n_bin = fix_segment_size_to_integer_samples(segment_size, dt)
+    elif binned and segment_size is None:
+        n_bin = fluxes1.size
     else:
-        segment_size = n_bin * dt
+        _, n_bin = fix_segment_size_to_integer_samples(gti.max() - gti.min(), dt)
+
     flux_iterable1 = get_flux_iterable_from_segments(
-        times1, gti, segment_size, n_bin, dt=dt, fluxes=fluxes1, errors=errors1
+        times1, gti, segment_size, n_bin=n_bin, dt=dt, fluxes=fluxes1, errors=errors1
     )
     flux_iterable2 = get_flux_iterable_from_segments(
-        times2, gti, segment_size, n_bin, dt=dt, fluxes=fluxes2, errors=errors2
+        times2, gti, segment_size, n_bin=n_bin, dt=dt, fluxes=fluxes2, errors=errors2
     )
 
     is_events = np.all([val is None for val in (fluxes1, fluxes2, errors1, errors2)])
diff --git a/stingray/powerspectrum.py b/stingray/powerspectrum.py
index fe2fb2b2..4fd26243 100755
--- a/stingray/powerspectrum.py
+++ b/stingray/powerspectrum.py
@@ -14,7 +14,7 @@ from .gti import cross_two_gtis, time_intervals_from_gtis
 
 from .lightcurve import Lightcurve
 from .fourier import avg_pds_from_iterable, unnormalize_periodograms
-from .fourier import avg_pds_from_events
+from .fourier import avg_pds_from_timeseries
 from .fourier import get_flux_iterable_from_segments
 from .fourier import poisson_level
 from .fourier import get_rms_from_unnorm_periodogram
@@ -1134,7 +1134,7 @@ def powerspectrum_from_time_array(
     force_averaged = segment_size is not None
     # Suppress progress bar for single periodogram
     silent = silent or (segment_size is None)
-    table = avg_pds_from_events(
+    table = avg_pds_from_timeseries(
         times,
         gti,
         segment_size,
@@ -1267,7 +1267,7 @@ def powerspectrum_from_lightcurve(
     if gti is None:
         gti = lc.gti
 
-    table = avg_pds_from_events(
+    table = avg_pds_from_timeseries(
         lc.time,
         gti,
         segment_size,
@@ -1347,7 +1347,7 @@ def powerspectrum_from_timeseries(
     if error_flux_attr is not None:
         err = getattr(ts, error_flux_attr)
 
-    results = avg_pds_from_events(
+    results = avg_pds_from_timeseries(
         ts.time,
         gti,
         segment_size,
diff --git a/stingray/utils.py b/stingray/utils.py
index 7f0b1631..d065d661 100644
--- a/stingray/utils.py
+++ b/stingray/utils.py
@@ -970,6 +970,51 @@ def _als(y, lam, p, niter=10):
     return z
 
 
+def fix_segment_size_to_integer_samples(segment_size, dt, tolerance=0.01):
+    """Fix segment size to an integer number of bins.
+
+    In the most common case, it will be reduced to an integer number of bins,
+    approximating to the lower integer. However, when it is close to the next
+    integer, it will be approximated to the higher integer.
+
+    Parameters
+    ----------
+    segment_size : float
+        The segment size in seconds
+    dt : float
+        The sample time in seconds
+
+    Other Parameters
+    ----------------
+    tolerance : float
+        The tolerance to consider when approximating to the higher integer
+
+    Returns
+    -------
+    segment_size : float
+        The segment size in seconds, fixed to an integer number of bins
+    n_bin : int
+        The number of bins in the segment
+
+    Examples
+    --------
+    >>> seg, n = fix_segment_size_to_integer_samples(1.0, 0.1)
+    >>> assert seg == 1.0, n == 10
+    >>> seg, n = fix_segment_size_to_integer_samples(0.999, 0.1)
+    >>> assert seg == 1.0, n == 10
+    """
+    n_bin_float = segment_size / dt
+    n_bin_down = np.floor(segment_size / dt)
+    n_bin_up = np.ceil(segment_size / dt)
+    n_bin = n_bin_down
+
+    if n_bin_up - n_bin_float < tolerance:
+        n_bin = n_bin_up
+
+    segment_size = n_bin * dt
+    return segment_size, int(n_bin)
+
+
 def baseline_als(x, y, lam=None, p=None, niter=10, return_baseline=False, offset_correction=False):
     """Baseline Correction with Asymmetric Least Squares Smoothing.
 
diff --git a/stingray/varenergyspectrum.py b/stingray/varenergyspectrum.py
index b1a40157..9b1d7a35 100644
--- a/stingray/varenergyspectrum.py
+++ b/stingray/varenergyspectrum.py
@@ -6,7 +6,12 @@ from stingray.gti import check_separate, cross_two_gtis
 from stingray.lightcurve import Lightcurve
 from stingray.utils import assign_value_if_none, simon, excess_variance, show_progress
 
-from stingray.fourier import avg_cs_from_events, avg_pds_from_events, fftfreq, get_average_ctrate
+from stingray.fourier import (
+    avg_cs_from_timeseries,
+    avg_pds_from_timeseries,
+    fftfreq,
+    get_average_ctrate,
+)
 from stingray.fourier import poisson_level, error_on_averaged_cross_spectrum, cross_to_covariance
 from abc import ABCMeta, abstractmethod
 
@@ -530,7 +535,7 @@ class RmsSpectrum(VarEnergySpectrum):
                 sub2_power_noise = poisson_level(norm="abs", meanrate=countrate_sub2)
 
                 # Calculate the cross spectrum
-                results = avg_cs_from_events(
+                results = avg_cs_from_timeseries(
                     sub_events,
                     sub_events2,
                     self.gti,
@@ -550,7 +555,7 @@ class RmsSpectrum(VarEnergySpectrum):
                     delta_nu_after_mean * np.sqrt(sub_power_noise * sub2_power_noise)
                 )
             else:
-                results = avg_pds_from_events(
+                results = avg_pds_from_timeseries(
                     sub_events, self.gti, self.segment_size, self.bin_time, silent=True, norm="abs"
                 )
                 if results is None:
@@ -804,7 +809,7 @@ class LagSpectrum(VarEnergySpectrum):
         ref_events = self._get_times_from_energy_range(self.events2, self.ref_band[0])
 
         # Calculate the PDS in the reference band. Needed to calculate errors.
-        results = avg_pds_from_events(
+        results = avg_pds_from_timeseries(
             ref_events, self.gti, self.segment_size, self.bin_time, silent=True, norm="none"
         )
 
@@ -827,7 +832,7 @@ class LagSpectrum(VarEnergySpectrum):
             # Extract the photon arrival times from the subject band
             sub_events = self._get_times_from_energy_range(self.events1, eint)
 
-            results_cross = avg_cs_from_events(
+            results_cross = avg_cs_from_timeseries(
                 sub_events,
                 ref_events,
                 self.gti,
@@ -837,7 +842,7 @@ class LagSpectrum(VarEnergySpectrum):
                 norm="none",
             )
 
-            results_ps = avg_pds_from_events(
+            results_ps = avg_pds_from_timeseries(
                 sub_events, self.gti, self.segment_size, self.bin_time, silent=True, norm="none"
             )
 
@@ -982,7 +987,7 @@ class ComplexCovarianceSpectrum(VarEnergySpectrum):
         countrate_ref = get_average_ctrate(ref_events, self.gti, self.segment_size)
         ref_power_noise = poisson_level(norm="abs", meanrate=countrate_ref)
 
-        results = avg_pds_from_events(
+        results = avg_pds_from_timeseries(
             ref_events, self.gti, self.segment_size, self.bin_time, silent=True, norm="abs"
         )
         freq = results["freq"]
@@ -1004,7 +1009,7 @@ class ComplexCovarianceSpectrum(VarEnergySpectrum):
             countrate_sub = get_average_ctrate(sub_events, self.gti, self.segment_size)
             sub_power_noise = poisson_level(norm="abs", meanrate=countrate_sub)
 
-            results_cross = avg_cs_from_events(
+            results_cross = avg_cs_from_timeseries(
                 sub_events,
                 ref_events,
                 self.gti,
@@ -1014,7 +1019,7 @@ class ComplexCovarianceSpectrum(VarEnergySpectrum):
                 norm="abs",
             )
 
-            results_ps = avg_pds_from_events(
+            results_ps = avg_pds_from_timeseries(
                 sub_events, self.gti, self.segment_size, self.bin_time, silent=True, norm="abs"
             )
 


Test patch:
diff --git a/stingray/tests/test_crossspectrum.py b/stingray/tests/test_crossspectrum.py
index ea5022fa..90f6741e 100644
--- a/stingray/tests/test_crossspectrum.py
+++ b/stingray/tests/test_crossspectrum.py
@@ -178,6 +178,19 @@ class TestAveragedCrossspectrumEvents(object):
         )
         self.lc1, self.lc2 = self.events1, self.events2
 
+    def test_single_cs_of_lc_with_tight_gtis_does_not_crash(self):
+        tstart = 1.0
+        tend = 10.0
+        gti = [[1.0, 9.0]]
+
+        time = np.linspace(tstart, tend, 10001)
+
+        counts1 = np.random.poisson(10, size=time.shape[0])
+        counts2 = np.random.poisson(10, size=time.shape[0])
+        lc1 = Lightcurve(time, counts1, gti=gti)
+        lc2 = Lightcurve(time, counts2, gti=gti)
+        Crossspectrum(lc1, lc2, norm="leahy")
+
     @pytest.mark.parametrize("norm", ["leahy", "frac", "abs", "none"])
     def test_common_mean_gives_comparable_scatter(self, norm):
         acs = AveragedCrossspectrum(
@@ -704,14 +717,22 @@ class TestCrossspectrum(object):
 
     def test_make_crossspectrum_diff_lc_counts_shape(self):
         counts = np.array([1] * 10001)
-        time = np.linspace(0.0, 1.0001, 10001)
-        lc_ = Lightcurve(time, counts)
+        dt = 0.0001
+        time = np.arange(0.0, 1.0001, dt)
+        lc_ = Lightcurve(time, counts, gti=[[time[0] - dt / 2, time[-1] + dt / 2]])
         with pytest.warns(UserWarning, match="Lightcurves do not have same tseg"):
-            with pytest.raises(
-                AssertionError, match="No GTIs are equal to or longer than segment_size"
-            ):
+            with pytest.raises(AssertionError, match="Time arrays are not the same"):
                 Crossspectrum(self.lc1, lc_)
 
+    def test_make_crossspectrum_lc_and_evts(self):
+        counts = np.array([1] * 10001)
+        dt = 0.0001
+        time = np.arange(0.0, 1.0001, dt)
+        lc_ = Lightcurve(time, counts, gti=[[time[0] - dt / 2, time[-1] + dt / 2]])
+        ev_ = EventList(time)
+        with pytest.raises(ValueError, match="Please use input data of the same kind"):
+            Crossspectrum(ev_, lc_, skip_checks=True)
+
     def test_make_crossspectrum_diff_lc_stat(self):
         lc_ = copy.deepcopy(self.lc1)
         lc_.err_dist = "gauss"
diff --git a/stingray/tests/test_fourier.py b/stingray/tests/test_fourier.py
index 51b9b560..860a4152 100644
--- a/stingray/tests/test_fourier.py
+++ b/stingray/tests/test_fourier.py
@@ -8,6 +8,8 @@ from astropy.table import Table
 from stingray.fourier import fft, fftfreq, normalize_abs, normalize_frac, poisson_level
 from stingray.fourier import (
     get_flux_iterable_from_segments,
+    avg_pds_from_timeseries,
+    avg_cs_from_timeseries,
     avg_pds_from_events,
     avg_cs_from_events,
 )
@@ -91,8 +93,20 @@ def test_avg_pds_imperfect_lc_size():
     gti = np.asarray([[-0.5, 99.5]])
     segment_size = 5.99
     dt = 1
-    res = avg_pds_from_events(times, gti, segment_size, dt, fluxes=fluxes)
-    assert res.meta["segment_size"] == 5
+    res = avg_pds_from_timeseries(times, gti, segment_size, dt, fluxes=fluxes)
+    assert res.meta["segment_size"] == 6
+    assert res.meta["dt"] == 1
+
+
+def test_avg_pds_from_events_warns():
+    times = np.arange(100)
+    fluxes = np.ones(100).astype(float)
+    gti = np.asarray([[-0.5, 99.5]])
+    segment_size = 5.99
+    dt = 1
+    with pytest.warns(DeprecationWarning, match="avg_pds_from_events is deprecated"):
+        res = avg_pds_from_events(times, gti, segment_size, dt, fluxes=fluxes)
+    assert res.meta["segment_size"] == 6
     assert res.meta["dt"] == 1
 
 
@@ -103,10 +117,25 @@ def test_avg_cs_imperfect_lc_size():
     gti = np.asarray([[-0.5, 99.5]])
     segment_size = 5.99
     dt = 1
-    res = avg_cs_from_events(
+    res = avg_cs_from_timeseries(
         times1, times2, gti, segment_size, dt, fluxes1=fluxes1, fluxes2=fluxes2
     )
-    assert res.meta["segment_size"] == 5
+    assert res.meta["segment_size"] == 6
+    assert res.meta["dt"] == 1
+
+
+def test_avg_cs_from_events_warns():
+    times1 = times2 = np.arange(100)
+    fluxes1 = np.ones(100).astype(float)
+    fluxes2 = np.ones(100).astype(float)
+    gti = np.asarray([[-0.5, 99.5]])
+    segment_size = 5.99
+    dt = 1
+    with pytest.warns(DeprecationWarning, match="avg_cs_from_events is deprecated"):
+        res = avg_cs_from_events(
+            times1, times2, gti, segment_size, dt, fluxes1=fluxes1, fluxes2=fluxes2
+        )
+    assert res.meta["segment_size"] == 6
     assert res.meta["dt"] == 1
 
 
@@ -244,7 +273,7 @@ class TestFourier(object):
 
     def test_avg_pds_bad_input(self):
         times = np.sort(rng.uniform(0, 1000, 1))
-        out_ev = avg_pds_from_events(times, self.gti, self.segment_size, self.dt)
+        out_ev = avg_pds_from_timeseries(times, self.gti, self.segment_size, self.dt)
         assert out_ev is None
 
     @pytest.mark.parametrize("return_subcs", [True, False])
@@ -252,7 +281,7 @@ class TestFourier(object):
     def test_avg_cs_bad_input(self, return_auxil, return_subcs):
         times1 = np.sort(rng.uniform(0, 1000, 1))
         times2 = np.sort(rng.uniform(0, 1000, 1))
-        out_ev = avg_cs_from_events(
+        out_ev = avg_cs_from_timeseries(
             times1,
             times2,
             self.gti,
@@ -265,7 +294,7 @@ class TestFourier(object):
 
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_pds_use_common_mean_similar_stats(self, norm):
-        out_comm = avg_pds_from_events(
+        out_comm = avg_pds_from_timeseries(
             self.times,
             self.gti,
             self.segment_size,
@@ -275,7 +304,7 @@ class TestFourier(object):
             silent=True,
             fluxes=None,
         )
-        out = avg_pds_from_events(
+        out = avg_pds_from_timeseries(
             self.times,
             self.gti,
             self.segment_size,
@@ -289,7 +318,7 @@ class TestFourier(object):
 
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_cs_use_common_mean_similar_stats(self, norm):
-        out_comm = avg_cs_from_events(
+        out_comm = avg_cs_from_timeseries(
             self.times,
             self.times2,
             self.gti,
@@ -300,7 +329,7 @@ class TestFourier(object):
             silent=True,
             return_subcs=True,
         )
-        out = avg_cs_from_events(
+        out = avg_cs_from_timeseries(
             self.times,
             self.times2,
             self.gti,
@@ -328,7 +357,7 @@ class TestFourier(object):
     @pytest.mark.parametrize("use_common_mean", [True, False])
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_pds_cts_and_events_are_equal(self, norm, use_common_mean):
-        out_ev = avg_pds_from_events(
+        out_ev = avg_pds_from_timeseries(
             self.times,
             self.gti,
             self.segment_size,
@@ -339,7 +368,7 @@ class TestFourier(object):
             fluxes=None,
             return_subcs=True,
         )
-        out_ct = avg_pds_from_events(
+        out_ct = avg_pds_from_timeseries(
             self.bin_times,
             self.gti,
             self.segment_size,
@@ -355,7 +384,7 @@ class TestFourier(object):
     @pytest.mark.parametrize("use_common_mean", [True, False])
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_pds_cts_and_err_and_events_are_equal(self, norm, use_common_mean):
-        out_ev = avg_pds_from_events(
+        out_ev = avg_pds_from_timeseries(
             self.times,
             self.gti,
             self.segment_size,
@@ -366,7 +395,7 @@ class TestFourier(object):
             fluxes=None,
             return_subcs=True,
         )
-        out_ct = avg_pds_from_events(
+        out_ct = avg_pds_from_timeseries(
             self.bin_times,
             self.gti,
             self.segment_size,
@@ -389,7 +418,7 @@ class TestFourier(object):
     @pytest.mark.parametrize("use_common_mean", [True, False])
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_cs_cts_and_events_are_equal(self, norm, use_common_mean):
-        out_ev = avg_cs_from_events(
+        out_ev = avg_cs_from_timeseries(
             self.times,
             self.times2,
             self.gti,
@@ -399,7 +428,7 @@ class TestFourier(object):
             use_common_mean=use_common_mean,
             silent=False,
         )
-        out_ct = avg_cs_from_events(
+        out_ct = avg_cs_from_timeseries(
             self.bin_times,
             self.bin_times,
             self.gti,
@@ -419,7 +448,7 @@ class TestFourier(object):
     @pytest.mark.parametrize("use_common_mean", [True, False])
     @pytest.mark.parametrize("norm", ["frac", "abs", "none", "leahy"])
     def test_avg_cs_cts_and_err_and_events_are_equal(self, norm, use_common_mean):
-        out_ev = avg_cs_from_events(
+        out_ev = avg_cs_from_timeseries(
             self.times,
             self.times2,
             self.gti,
@@ -429,7 +458,7 @@ class TestFourier(object):
             use_common_mean=use_common_mean,
             silent=False,
         )
-        out_ct = avg_cs_from_events(
+        out_ct = avg_cs_from_timeseries(
             self.bin_times,
             self.bin_times,
             self.gti,
diff --git a/stingray/tests/test_powerspectrum.py b/stingray/tests/test_powerspectrum.py
index cd1f6c04..13ddca43 100644
--- a/stingray/tests/test_powerspectrum.py
+++ b/stingray/tests/test_powerspectrum.py
@@ -357,6 +357,17 @@ class TestPowerspectrum(object):
 
         cls.lc = Lightcurve(time, counts=poisson_counts, dt=dt, gti=[[tstart, tend]])
 
+    def test_single_ps_of_lc_with_tight_gtis_does_not_crash(self):
+        tstart = 1.0
+        tend = 10.0
+        gti = [[1.0, 9.0]]
+
+        time = np.linspace(tstart, tend, 10000)
+        counts = np.random.poisson(10, size=time.shape[0])
+
+        lc = Lightcurve(time, counts, gti=gti)
+        Powerspectrum(lc, norm="leahy")
+
     @pytest.mark.parametrize("skip_checks", [True, False])
     def test_initialize_empty(self, skip_checks):
         cs = Powerspectrum(skip_checks=skip_checks)