Fix err dist none

docs/changes/909.bugfix.rst

@@ -0,0 +1 @@
+Fix ligthcurve breaking when err_dist=None and changed default err_dist to None to match docs

docs/simulator.rst

@@ -25,6 +25,7 @@ The examples here assume that the following libraries and modules have been impo
 	>>> import numpy as np
 	>>> from stingray import Lightcurve, sampledata
 	>>> from stingray.simulator import simulator, models
+   >>> import pytest
 
 Creating a Simulator Object
 ---------------------------
@@ -48,7 +49,8 @@ Simulate Method
 Stingray provides multiple ways to simulate a light curve. However, all these methods follow a common recipe::
 
   >>> sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125, rms=1.0)
-  >>> lc = sim.simulate(2)
+  >>> with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+  ...    lc = sim.simulate(2)
 
 Using Power-Law Spectrum
 ------------------------
@@ -154,7 +156,8 @@ Channel Simulation
 The `simulator` class provides the functionality to simulate light curves independently for each channel. This is useful, for example, when dealing with energy dependent impulse responses where we can create a di↵erent simulation channel for each energy range. The module provides options to count, retrieve and delete channels.::
 
   >>> sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125, rms=1.0)
-  >>> sim.simulate_channel('3.5 - 4.5', 2)
+  >>> with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+  ...    sim.simulate_channel('3.5 - 4.5', 2)
   >>> sim.count_channels()
   1
   >>> lc = sim.get_channel('3.5 - 4.5')
@@ -164,9 +167,10 @@ Alternatively, assume that we have light curves in the simulated energy channels
 
   >>> sim.count_channels()
   0
-  >>> sim.simulate_channel('3.5 - 4.5', 2)
-  >>> sim.simulate_channel('4.5 - 5.5', 2)
-  >>> sim.simulate_channel('5.5 - 6.5', 2)
+  >>> with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+  ...    sim.simulate_channel('3.5 - 4.5', 2)
+  ...    sim.simulate_channel('4.5 - 5.5', 2)
+  ...    sim.simulate_channel('5.5 - 6.5', 2)
   >>> chans = sim.get_channels(['3.5 - 4.5','4.5 - 5.5','5.5 - 6.5'])
   >>> sim.delete_channels(['3.5 - 4.5','4.5 - 5.5','5.5 - 6.5'])
 

stingray/lightcurve.py

@@ -39,7 +39,7 @@
 
 __all__ = ["Lightcurve"]
 
-valid_statistics = ["poisson", "gauss", None]
+valid_statistics = ["poisson", "gauss", "none"]
 
 logger = setup_logger()
 
@@ -215,7 +215,7 @@ def __init__(
         err=None,
         input_counts=True,
         gti=None,
-        err_dist="poisson",
+        err_dist=None,
         bg_counts=None,
         bg_ratio=None,
         frac_exp=None,
@@ -275,6 +275,11 @@ def __init__(
         if not skip_checks:
             time, counts, err = self.initial_optional_checks(time, counts, err, gti=gti)
 
+        if err_dist is None and input_counts:
+            err_dist = "poisson"
+        elif err_dist is None:
+            err_dist = "none"
+
         if err_dist.lower() not in valid_statistics:
             # err_dist set can be increased with other statistics
             raise StingrayError(
@@ -283,10 +288,8 @@ def __init__(
             )
         elif not err_dist.lower() == "poisson":
             simon(
-                "Stingray only uses poisson err_dist at the moment. "
-                "All analysis in the light curve will assume Poisson "
-                "errors. "
-                "Sorry for the inconvenience."
+                "Beware! Stingray only supports poisson err_dist at the moment in many methods"
+                ", and 'gauss' in a few more. "
             )
 
         self._time = time

stingray/sampledata.py

@@ -24,4 +24,4 @@ def sample_data():
     counts = data[0 : len(data), 1]
 
     # Return class:`Lightcurve` object
-    return lightcurve.Lightcurve(dates, counts, dt=dt, skip_checks=True)
+    return lightcurve.Lightcurve(dates, counts, dt=dt, skip_checks=True, err_dist="poisson")

stingray/tests/test_crosscorrelation.py

@@ -23,9 +23,9 @@ def setup_class(cls):
         freq = 1 / 50
         flux1 = 0.5 + 0.5 * np.sin(2 * np.pi * freq * times)
         flux2 = 0.5 + 0.5 * np.sin(2 * np.pi * freq * (times - 20))
-
-        cls.lc1 = Lightcurve(times, flux1, dt=dt, err_dist="gauss", gti=gti, skip_checks=True)
-        cls.lc2 = Lightcurve(times, flux2, dt=dt, err_dist="gauss", gti=gti, skip_checks=True)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            cls.lc1 = Lightcurve(times, flux1, dt=dt, err_dist="gauss", gti=gti, skip_checks=True)
+            cls.lc2 = Lightcurve(times, flux2, dt=dt, err_dist="gauss", gti=gti, skip_checks=True)
 
     def test_crosscorr(self):
         cr = CrossCorrelation(self.lc1, self.lc2)

stingray/tests/test_crossspectrum.py

@@ -505,14 +505,15 @@ def setup_class(self):
         counts1 = np.random.poisson(10000, size=time.shape[0])
         counts1_norm = counts1 / 13.4
         counts1_norm_err = np.std(counts1) / 13.4
-        self.lc1_norm = Lightcurve(
-            time,
-            counts1_norm,
-            gti=[[tstart, self.tseg]],
-            dt=dt,
-            err_dist="gauss",
-            err=np.zeros_like(counts1_norm) + counts1_norm_err,
-        )
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            self.lc1_norm = Lightcurve(
+                time,
+                counts1_norm,
+                gti=[[tstart, self.tseg]],
+                dt=dt,
+                err_dist="gauss",
+                err=np.zeros_like(counts1_norm) + counts1_norm_err,
+            )
         self.lc1 = Lightcurve(time, counts1, gti=[[tstart, self.tseg]], dt=dt)
         self.rate1 = np.mean(counts1) / dt  # mean count rate (counts/sec) of light curve 1
 
@@ -1217,7 +1218,8 @@ def test_rebin_log_returns_complex_values_and_errors(self):
     def test_timelag(self):
         dt = 0.1
         simulator = Simulator(dt, 10000, rms=0.2, mean=1000)
-        test_lc1 = simulator.simulate(2)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            test_lc1 = simulator.simulate(2)
         test_lc1.counts -= np.min(test_lc1.counts)
 
         with pytest.warns(UserWarning):
@@ -1417,7 +1419,7 @@ def setup_class(cls):
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", category=UserWarning)
 
-            lc = Lightcurve(timestamps, signal, err_dist="poisson", dt=dt, gti=[[0, 100]])
+            lc = Lightcurve(timestamps, signal, dt=dt, gti=[[0, 100]])
 
         cls.lc = lc
 

stingray/tests/test_lightcurve.py

@@ -391,15 +391,15 @@ def test_irregular_time_warning(self):
         )
 
         with pytest.warns(UserWarning, match=warn_str):
-            _ = Lightcurve(times, counts, err_dist="poisson")
+            _ = Lightcurve(times, counts)
 
     def test_unrecognize_err_dist_warning(self):
         """
         Check if a non-poisson error_dist throws the correct warning.
         """
         times = [1, 2, 3, 4, 5]
         counts = [2, 2, 2, 2, 2]
-        warn_str = "SIMON says: Stingray only uses poisson err_dist at " "the moment"
+        warn_str = "SIMON says: Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."
 
         with warnings.catch_warnings(record=True) as w:
             warnings.filterwarnings("always")
@@ -554,7 +554,7 @@ def test_input_countrate(self):
         mean_counts = 2.0
         times = np.arange(0 + dt / 2, 5 - dt / 2, dt)
         countrate = np.zeros_like(times) + mean_counts
-        lc = Lightcurve(times, countrate, input_counts=False)
+        lc = Lightcurve(times, countrate, input_counts=False, err_dist="poisson")
         assert np.allclose(lc.counts, np.zeros_like(countrate) + mean_counts * dt)
 
     def test_meanrate(self):
@@ -726,7 +726,7 @@ def test_slicing(self):
             dt=self.dt,
             gti=self.gti,
             err=self.counts / 10,
-            err_dist="gauss",
+            err_dist="poisson",
         )
         assert np.allclose(lc[1:3].counts, np.array([4, 6]))
         assert np.allclose(lc[:2].counts, np.array([2, 4]))
@@ -1326,14 +1326,14 @@ def test_split_lc_by_gtis_minpoints(self):
     def test_shift(self):
         times = [1, 2, 3, 4, 5, 6, 7, 8]
         counts = [1, 1, 1, 1, 2, 3, 3, 2]
-        lc = Lightcurve(times, counts, input_counts=True)
+        lc = Lightcurve(times, counts, input_counts=True, err_dist="poisson")
         lc2 = lc.shift(1)
         assert np.allclose(lc2.time - 1, times)
         lc2 = lc.shift(-1)
         assert np.allclose(lc2.time + 1, times)
         assert np.allclose(lc2.counts, lc.counts)
         assert np.allclose(lc2.countrate, lc.countrate)
-        lc = Lightcurve(times, counts, input_counts=False)
+        lc = Lightcurve(times, counts, input_counts=False, err_dist="poisson")
         lc2 = lc.shift(1)
         assert np.allclose(lc2.counts, lc.counts)
         assert np.allclose(lc2.countrate, lc.countrate)
@@ -1350,6 +1350,7 @@ def test_table_roundtrip(self):
             mission="BUBU",
             instr="BABA",
             mjdref=53467.0,
+            err_dist="poisson"
         )
 
         ts = lc.to_astropy_table()
@@ -1378,6 +1379,7 @@ def test_table_roundtrip_ctrate(self):
             instr="BABA",
             mjdref=53467.0,
             input_counts=False,
+            err_dist="poisson"
         )
 
         ts = lc.to_astropy_table()
@@ -1393,7 +1395,7 @@ def test_timeseries_roundtrip(self):
         wrong format is provided.
         """
         N = len(self.times)
-        lc = Lightcurve(self.times, self.counts, mission="BUBU", instr="BABA", mjdref=53467.0)
+        lc = Lightcurve(self.times, self.counts, mission="BUBU", instr="BABA", mjdref=53467.0, err_dist="poisson")
 
         ts = lc.to_astropy_timeseries()
         new_lc = lc.from_astropy_timeseries(ts)
@@ -1413,7 +1415,7 @@ def test_timeseries_roundtrip_ctrate(self):
         countrate = np.zeros_like(times) + mean_counts
 
         lc = Lightcurve(
-            times, countrate, mission="BUBU", instr="BABA", mjdref=53467.0, input_counts=False
+            times, countrate, mission="BUBU", instr="BABA", mjdref=53467.0, input_counts=False, err_dist="poisson"
         )
 
         ts = lc.to_astropy_timeseries()
@@ -1558,7 +1560,7 @@ def test_apply_gtis_lc_rate(self, inplace):
         time = np.arange(1, 10, dt)
         countrate = np.zeros_like(time) + 5
         # create the lightcurve from countrare
-        lc_rate = Lightcurve(time, counts=countrate, input_counts=False, gti=[[-0.5, 10.5]])
+        lc_rate = Lightcurve(time, counts=countrate, input_counts=False, gti=[[-0.5, 10.5]], err_dist="poisson")
         lc_rate.gti = [[-0.5, 2.5]]
         lc_rate_new = lc_rate.apply_gtis(inplace=inplace)
         if inplace:

stingray/tests/test_lombscargle.py

@@ -35,8 +35,9 @@ def test_autofrequency():
 class TestLombScargleCrossspectrum:
     def setup_class(self):
         sim = Simulator(0.0001, 50, 100, 1, random_state=42, tstart=0)
-        lc1 = sim.simulate(0)
-        lc2 = sim.simulate(0)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            lc1 = sim.simulate(0)
+            lc2 = sim.simulate(0)
         self.rate1 = lc1.countrate
         self.rate2 = lc2.countrate
         low, high = lc1.time.min(), lc1.time.max()
@@ -147,7 +148,9 @@ def test_init_with_negative_max_freq(self):
             lscs = LombScargleCrossspectrum(self.lc1, self.lc2, max_freq=-1)
 
     def test_make_crossspectrum_diff_lc_counts_shape(self):
-        lc_ = Simulator(0.0001, 103, 100, 1, random_state=42, tstart=0).simulate(0)
+        sim = Simulator(0.0001, 103, 100, 1, random_state=42, tstart=0)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            lc_ = sim.simulate(0)
         with pytest.warns(UserWarning) as record:
             lscs = LombScargleCrossspectrum(self.lc1, lc_)
         assert np.any(["different statistics" in r.message.args[0] for r in record])
@@ -246,7 +249,8 @@ def func(time, phase=0):
 class TestLombScarglePowerspectrum:
     def setup_class(self):
         sim = Simulator(0.0001, 100, 100, 1, random_state=42, tstart=0)
-        lc = sim.simulate(0)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            lc = sim.simulate(0)
         self.rate = lc.countrate
         low, high = lc.time.min(), lc.time.max()
         s1 = lc.counts

stingray/tests/test_varenergyspectrum.py

@@ -48,7 +48,8 @@ def setup_class(cls):
         from ..simulator import Simulator
 
         simulator = Simulator(0.1, 10000, rms=0.2, mean=200)
-        test_lc = simulator.simulate(1)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            test_lc = simulator.simulate(1)
         cls.test_ev1, cls.test_ev2 = EventList(), EventList()
         cls.test_ev1.simulate_times(test_lc)
         cls.test_ev1.energy = np.random.uniform(0.3, 12, len(cls.test_ev1.time))
@@ -190,9 +191,10 @@ def setup_class(cls):
         flux = data / 40
         times = np.arange(data.size) * cls.bin_time
         gti = np.asanyarray([[0, data.size * cls.bin_time]])
-        test_lc = Lightcurve(
-            times, flux, err_dist="gauss", gti=gti, dt=cls.bin_time, skip_checks=True
-        )
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            test_lc = Lightcurve(
+                times, flux, err_dist="gauss", gti=gti, dt=cls.bin_time, skip_checks=True
+            )
 
         cls.test_ev1, cls.test_ev2 = EventList(), EventList()
         cls.test_ev1.simulate_times(test_lc)
@@ -551,8 +553,9 @@ def setup_class(cls):
         times, flux, rolled_flux = times[good], flux[good], rolled_flux[good]
 
         length = times[-1] - times[0]
-        test_ref = Lightcurve(times, flux, err_dist="gauss", dt=dt, skip_checks=True)
-        test_sub = Lightcurve(test_ref.time, rolled_flux, err_dist=test_ref.err_dist, dt=dt)
+        with pytest.warns(UserWarning, match="Beware! Stingray only supports poisson err_dist at the moment in many methods, and 'gauss' in a few more."):
+            test_ref = Lightcurve(times, flux, err_dist="gauss", dt=dt, skip_checks=True)
+            test_sub = Lightcurve(test_ref.time, rolled_flux, err_dist=test_ref.err_dist, dt=dt)
         test_ref_ev, test_sub_ev = EventList(), EventList()
         test_ref_ev.simulate_times(test_ref)
         test_sub_ev.simulate_times(test_sub)