Phase lag fixes, code cleanup and optimization

stingray/fourier.py

@@ -2058,17 +2058,17 @@ def lsft_slow(
     ft_res : numpy.ndarray
         An array of Fourier transformed data.
     """
-    y_ = copy.deepcopy(y) - np.mean(y)
-    freqs = freqs[freqs >= 0]
+    y_ = y - np.mean(y)
+    freqs = np.asarray(freqs[np.asarray(freqs) >= 0])
 
     ft_real = np.zeros_like(freqs)
     ft_imag = np.zeros_like(freqs)
     ft_res = np.zeros_like(freqs, dtype=np.complex128)
 
-    num_y = len(y_)
-    num_freqs = len(freqs)
+    num_y = y_.shape[0]
+    num_freqs = freqs.shape[0]
     sum_y = np.sum(y_)
-    const1 = np.sqrt(0.5) * np.sqrt(num_y)
+    const1 = np.sqrt(0.5 * num_y)
     const2 = const1 * np.sign(sign)
     ft_real = ft_imag = np.zeros(num_freqs)
     ft_res = np.zeros(num_freqs, dtype=np.complex128)

stingray/lombscargle.py

@@ -476,17 +476,9 @@ def _initialize_empty(self):
         self.variance2 = None
         return
 
-    def phase_lag(self):
-        """Not applicable for unevenly sampled data"""
-        raise AttributeError(
-            "Object has no attribute named 'phase_lag' ! Not applicable for unevenly sampled data"
-        )
-
     def time_lag(self):
-        """Not applicable for unevenly sampled data"""
-        raise AttributeError(
-            "Object has no attribute named 'time_lag' ! Not applicable for unevenly sampled data"
-        )
+        super().__doc__
+        return self.phase_lag() / (2 * np.pi * self.freq)
 
     def classical_significances(self):
         """Not applicable for unevenly sampled data"""

stingray/tests/test_fourier.py

@@ -545,9 +545,11 @@ def test_unnormalize_poisson_noise(self, norm, power_type):
 
 @pytest.mark.parametrize("phlag", [0.05, 0.1, 0.2, 0.4])
 def test_lag(phlag):
-    freq=1.1123232252
+    freq = 1.1123232252
+
     def func(time, phase=0):
         return 2 + np.sin(2 * np.pi * (time * freq - phase))
+
     time = np.sort(np.random.uniform(0, 100, 3000))
     ft0 = lsft_slow(func(time, 0), time, np.array([freq]))
     ft1 = lsft_slow(func(time, phlag), time, np.array([freq]))
@@ -556,9 +558,9 @@ def func(time, phase=0):
         measured_lag -= 0.5
     while measured_lag <= -0.5:
         measured_lag += 0.5
-        
+
     print(measured_lag)
-    assert np.isclose((np.angle(ft1) - np.angle(ft0)) / 2 / np.pi, phlag, atol=0.01)
+    assert np.isclose((np.angle(ft1) - np.angle(ft0)) / 2 / np.pi, phlag, atol=0.02, rtol=0.02)
 
 
 def test_lsft_slow_fast():

stingray/tests/test_lombscargle.py

@@ -158,8 +158,6 @@ def test_valid_method(self, method):
     @pytest.mark.parametrize(
         "func_name",
         [
-            "phase_lag",
-            "time_lag",
             "classical_significances",
             "from_time_array",
             "from_events",