Try to make write more robust to float128 errors in FITS

stingray/base.py

@@ -191,21 +191,14 @@ def to_astropy_table(self, no_longdouble=False) -> Table:
 
         for attr in array_attrs:
             vals = np.asarray(getattr(self, attr))
-            if no_longdouble and isinstance(vals.flat[0], np.longdouble):
-                vals = vals.astype(float)
+            if no_longdouble:
+                vals = reduce_precision_if_extended(vals)
             data[attr] = vals
 
         ts = Table(data)
         meta_dict = self.get_meta_dict()
         if not no_longdouble:
-            for attr in meta_dict.keys():
-                vals = getattr(self, attr)
-                probe = vals
-                if isinstance(vals, Iterable) and len(vals) > 0 and not isinstance(vals, str):
-                    probe = np.asarray(vals).flat[0]
-                if isinstance(probe, np.longdouble):
-                    vals = vals.astype(float)
-                meta_dict[attr] = vals
+            meta_dict[attr] = reduce_precision_if_extended(vals)
 
         ts.meta.update(meta_dict)
 
@@ -509,7 +502,6 @@ def write(self, filename: str, fmt: str = None) -> None:
             Table({"a": [np.longdouble(3)]}).write(probe_file, format=fmt, overwrite=True)
             CAN_SAVE_LONGD = True
             os.unlink(probe_file)
-            raise ValueError("float128")
         except ValueError as e:
             if "float128" not in str(e):  # pragma: no cover
                 raise
@@ -1414,3 +1406,47 @@ def interpret_times(time: TTime, mjdref: float = 0) -> tuple[npt.ArrayLike, floa
             pass
 
     raise ValueError(f"Unknown time format: {type(time)}")
+
+
+def reduce_precision_if_extended(
+    x, probe_types=["float128", "float96", "float80"], destination=float
+):
+    """Reduce a number to a standard float if extended precision.
+
+    Ignore all non-float types.
+
+    Parameters
+    ----------
+    x : float
+        The number to be reduced
+
+    Returns
+    -------
+    x_red : same type of input
+        The input, only reduce to ``float`` precision if ``np.float128``
+
+    Examples
+    --------
+    >>> x = 1.0
+    >>> val = reduce_precision_if_extended(x, probe_types=["float64"])
+    >>> val is x
+    True
+    >>> x = np.asanyarray(1.0).astype(int)
+    >>> val = reduce_precision_if_extended(x, probe_types=["float64"])
+    >>> val is x
+    True
+    >>> x = np.asanyarray([1.0]).astype(int)
+    >>> val = reduce_precision_if_extended(x, probe_types=["float64"])
+    >>> val is x
+    True
+    >>> x = np.asanyarray(1.0).astype(np.float64)
+    >>> reduce_precision_if_extended(x, probe_types=["float64"], destination=np.float32) is x
+    False
+    >>> x = np.asanyarray([1.0]).astype(np.float64)
+    >>> reduce_precision_if_extended(x, probe_types=["float64"], destination=np.float32) is x
+    False
+    """
+    if any([t in str(np.obj2sctype(x)) for t in probe_types]):
+        x_ret = x.astype(destination)
+        return x_ret
+    return x

stingray/lightcurve.py

@@ -17,7 +17,7 @@
 from astropy.time import TimeDelta, Time
 from astropy import units as u
 
-from stingray.base import StingrayTimeseries
+from stingray.base import StingrayTimeseries, reduce_precision_if_extended
 import stingray.utils as utils
 from stingray.exceptions import StingrayError
 from stingray.gti import (
@@ -1596,10 +1596,10 @@ def from_lightkurve(lk, skip_checks=True):
     def to_astropy_timeseries(self):
         return self._to_astropy_object(kind="timeseries")
 
-    def to_astropy_table(self):
-        return self._to_astropy_object(kind="table")
+    def to_astropy_table(self, **kwargs):
+        return self._to_astropy_object(kind="table", **kwargs)
 
-    def _to_astropy_object(self, kind="table"):
+    def _to_astropy_object(self, kind="table", no_longdouble=False):
         data = {}
 
         for attr in [
@@ -1611,15 +1611,22 @@ def _to_astropy_object(self, kind="table"):
             "_bin_hi",
         ]:
             if hasattr(self, attr) and getattr(self, attr) is not None:
-                data[attr.lstrip("_")] = np.asarray(getattr(self, attr))
+                vals = np.asarray(getattr(self, attr))
+                if no_longdouble:
+                    vals = reduce_precision_if_extended(vals)
+                data[attr.lstrip("_")] = vals
+
+        time_array = self.time
+        if no_longdouble:
+            time_array = reduce_precision_if_extended(time_array)
 
         if kind.lower() == "table":
-            data["time"] = self.time
+            data["time"] = time_array
             ts = Table(data)
         elif kind.lower() == "timeseries":
             from astropy.timeseries import TimeSeries
 
-            ts = TimeSeries(data=data, time=TimeDelta(self.time * u.s))
+            ts = TimeSeries(data=data, time=TimeDelta(time_array * u.s))
         else:  # pragma: no cover
             raise ValueError("Invalid kind (accepted: table or timeseries)")
 
@@ -1634,6 +1641,9 @@ def _to_astropy_object(self, kind="table"):
             "err_dist",
         ]:
             if hasattr(self, attr) and getattr(self, attr) is not None:
+                vals = getattr(self, attr)
+                if no_longdouble:
+                    vals = reduce_precision_if_extended(vals)
                 ts.meta[attr.lstrip("_")] = getattr(self, attr)
 
         return ts

stingray/utils.py

@@ -39,6 +39,12 @@
     HAS_PYFFTW = False
 
 
+try:
+    np.float128
+    HAS_128 = True
+except AttributeError:
+    HAS_128 = False
+
 # If numba is installed, import jit. Otherwise, define an empty decorator with
 # the same name.
 try: