Input of lsft_slow,fast changed to be non stingray specific, added ch…

…angelog,

docs/changes/737.feature.rst

@@ -0,0 +1,2 @@
+- Implemented the Lomb Scargle Fourier Transform
+- Using which wrote the corresponding :class:`LombScargleCrossspectrum` and :class:`LombScarglePowerspectrum`

stingray/fourier.py

@@ -4,6 +4,7 @@
 from typing import Optional
 
 import numpy as np
+import numpy.typing as npt
 from astropy.table import Table
 from astropy.timeseries.periodograms.lombscargle.implementations.utils import trig_sum
 
@@ -1928,11 +1929,8 @@ def avg_cs_from_events(
     return results
 
 
-def lsft_fast(lc: Lightcurve, w0, df, Nf):
+def lsft_fast(y, t, dy, w0, df, Nf):
     # Work in Progress
-    y = lc.counts
-    t = lc.time
-    dy = lc.counts_err
     weights = dy**-2.0
     weights /= weights.sum()
 
@@ -1941,8 +1939,9 @@ def lsft_fast(lc: Lightcurve, w0, df, Nf):
 
 
 def lsft_slow(
-    lc: Lightcurve,
-    ww: np.ndarray,
+    y: npt.ArrayLike,
+    t: npt.ArrayLike,
+    ww: npt.ArrayLike,
     sign: Optional[int] = 1,
     fullspec: Optional[bool] = False,
 ):
@@ -1951,8 +1950,11 @@ def lsft_slow(
 
     Parameters
     ----------
-    lc : :class:`stingray.lightcurve.Lightcurve`
-        A light curve object.
+    y : a `:class:numpy.array` of floats
+        Observations to be transformed.
+
+    y : `:class:numpy.array` of floats
+        Times of the observations
 
     freqs : numpy.ndarray
         An array of frequencies at which the transform is sampled.
@@ -1974,11 +1976,9 @@ def lsft_slow(
     const1 = 1 / np.sqrt(2)
     const2 = const1 * sign
 
-    xx = lc.counts
-    sum_xx = np.sum(xx)
-    t = lc.time
+    sum_xx = np.sum(y)
 
-    num_xt = len(xx)
+    num_xt = len(y)
     num_ww = len(ww)
 
     ft_real = ft_imag = np.zeros((num_ww))
@@ -1997,8 +1997,8 @@ def lsft_slow(
             watan = np.arctan2(ssum, csum)
             wtau = 0.5 * watan
 
-            sumr = np.sum(np.multiply(xx, np.cos(wrun * t - wtau)))
-            sumi = np.sum(np.multiply(xx, np.sin(wrun * t - wtau)))
+            sumr = np.sum(np.multiply(y, np.cos(wrun * t - wtau)))
+            sumi = np.sum(np.multiply(y, np.sin(wrun * t - wtau)))
 
             scos2 = np.sum((np.power(np.cos(wrun * t - wtau), 2)))
             ssin2 = np.sum((np.power(np.sin(wrun * t - wtau), 2)))

stingray/lombscargle.py

@@ -2,17 +2,15 @@
 from typing import Optional, Union
 
 import numpy as np
-
+import numpy.typing as npt
 from astropy.timeseries.periodograms import LombScargle
 
-from stingray.crossspectrum import Crossspectrum
-from stingray.exceptions import StingrayError
-from stingray.utils import simon
-
-from .events import EventList  # Convert to relative nomenclature
-from .lightcurve import Lightcurve  # Convert to relative nomenclature
-
-from .fourier import lsft_slow, lsft_fast
+from .crossspectrum import Crossspectrum
+from .events import EventList
+from .exceptions import StingrayError
+from .fourier import lsft_fast, lsft_slow
+from .lightcurve import Lightcurve
+from .utils import simon
 
 
 # method default will change to fast after implementation of the fast algorithm
@@ -263,8 +261,8 @@ def _make_crossspectrum(self, lc1, lc2, fullspec=False, method="fast"):
         if lc1.n != lc2.n:
             raise StingrayError("Lightcurves do not have the same number of bins per segment.")
 
-        # if not np.isclose(lc1.dt, lc2.dt, rtol=0.1 * lc1.dt / lc1.tseg):
-        #     raise StingrayError("Lightcurves do not have the same time binning dt.")
+        if not np.isclose(lc1.dt, lc2.dt, rtol=0.1 * lc1.dt / lc1.tseg):
+            raise StingrayError("Lightcurves do not have the same time binning dt.")
 
         lc1.dt = lc2.dt
         self.dt = lc1.dt
@@ -290,14 +288,12 @@ def _make_crossspectrum(self, lc1, lc2, fullspec=False, method="fast"):
             )
 
         if self.__class__.__name__ == "LombScarglePowerspectrum":
-            print("ps")
             self.power_err = self.unnorm_power_err = self.power / np.sqrt(self.m)
         elif self.__class__.__name__ == "LombScargleCrossspectrum":
             simon(
                 "Errorbars on cross spectra are not thoroughly tested."
                 "Please report any inconsistencies."
             )
-            print("ls")
             self.unnorm_power_err = np.sqrt(2) / np.sqrt(self.m)
             self.unnorm_power_err /= np.divide(2, np.sqrt(np.abs(self.nphots1 * self.nphots2)))
             self.unnorm_power_err += np.zeros_like(self.unnorm_power)
@@ -389,8 +385,8 @@ def _ls_cross(self, lc1, lc2, ww=None, fullspec=False, method="slow"):
                 ww = ww2
 
         if method == "slow":
-            lsft1 = lsft_slow(lc1, ww, sign=1, fullspec=fullspec)
-            lsft2 = lsft_slow(lc2, ww, sign=-1, fullspec=fullspec)
+            lsft1 = lsft_slow(lc1.counts, lc1.time, ww, sign=1, fullspec=fullspec)
+            lsft2 = lsft_slow(lc2.counts, lc2.time, ww, sign=-1, fullspec=fullspec)
         # elif method == "fast":
         #     lsft1 = lsft_fast(lc1, ww, fullspec=fullspec)
         cross = np.multiply(lsft1, lsft2)