Rename functions _from_events -> _from_timeseries

stingray/crossspectrum.py

@@ -16,7 +16,7 @@
 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
@@ -2456,7 +2456,7 @@ 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,
@@ -2696,7 +2696,7 @@ 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,

stingray/fourier.py

@@ -2243,7 +2243,15 @@ def local_show_progress(a):
     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,
@@ -2333,7 +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,

stingray/powerspectrum.py

@@ -14,7 +14,7 @@
 
 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,

stingray/tests/test_fourier.py

@@ -8,6 +8,8 @@
 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,
 )
@@ -91,7 +93,19 @@ 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)
+    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
 
@@ -103,13 +117,28 @@ 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"] == 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
+
+
 class TestCoherence(object):
     @classmethod
     def setup_class(cls):
@@ -244,15 +273,15 @@ def test_fts_from_segments_cts_and_events_are_equal(self):
 
     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])
     @pytest.mark.parametrize("return_auxil", [True, False])
     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 @@ def test_avg_cs_bad_input(self, return_auxil, return_subcs):
 
     @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 @@ def test_avg_pds_use_common_mean_similar_stats(self, norm):
             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 @@ def test_avg_pds_use_common_mean_similar_stats(self, norm):
 
     @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 @@ def test_avg_cs_use_common_mean_similar_stats(self, norm):
             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 @@ def test_avg_cs_use_common_mean_similar_stats(self, norm):
     @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 @@ def test_avg_pds_cts_and_events_are_equal(self, norm, use_common_mean):
             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 @@ def test_avg_pds_cts_and_events_are_equal(self, norm, use_common_mean):
     @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 @@ def test_avg_pds_cts_and_err_and_events_are_equal(self, norm, use_common_mean):
             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 @@ def test_avg_pds_cts_and_err_and_events_are_equal(self, norm, use_common_mean):
     @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 @@ def test_avg_cs_cts_and_events_are_equal(self, norm, use_common_mean):
             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 @@ def test_avg_cs_cts_and_events_are_equal(self, norm, use_common_mean):
     @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 @@ def test_avg_cs_cts_and_err_and_events_are_equal(self, norm, use_common_mean):
             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,

stingray/varenergyspectrum.py

@@ -6,7 +6,12 @@
 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 @@ def _spectrum_function(self):
                 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 @@ def _spectrum_function(self):
                     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 @@ def _spectrum_function(self):
         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 @@ def _spectrum_function(self):
             # 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 @@ def _spectrum_function(self):
                 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 @@ def _spectrum_function(self):
         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 @@ def _spectrum_function(self):
             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 @@ def _spectrum_function(self):
                 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"
             )