Eliminated the usage of astropy logging

stingray/base.py

@@ -13,6 +13,7 @@
 from astropy.table import Table
 from astropy.time import Time, TimeDelta
 from astropy.units import Quantity
+from stingray.loggingconfig import setup_logger
 
 from .io import _can_save_longdouble, _can_serialize_meta
 from .utils import (
@@ -58,6 +59,8 @@
     "StingrayTimeseries",
 ]
 
+logger = setup_logger()
+
 
 def convert_table_attrs_to_lowercase(table: Table) -> Table:
     """Convert the column names of an Astropy Table to lowercase."""
@@ -1971,7 +1974,7 @@ def _get_all_array_attrs(objs):
                 all_meta_attrs.remove(attr)
 
         for attr in ignore_meta:
-            logging.info(f"The {attr} attribute will be removed from the output ")
+            logger.info(f"The {attr} attribute will be removed from the output ")
             if attr in all_meta_attrs:
                 all_meta_attrs.remove(attr)
 
@@ -2242,7 +2245,7 @@ def fill_bad_time_intervals(
 
         btis = get_btis(self.gti, self.time[0], self.time[-1])
         if len(btis) == 0:
-            logging.info("No bad time intervals to fill")
+            logger.info("No bad time intervals to fill")
             return copy.deepcopy(self)
         filtered_times = self.time[self.mask]
 
@@ -2255,7 +2258,7 @@ def fill_bad_time_intervals(
             even_sampling = False
             if self.dt > 0 and np.isclose(mean_data_separation, self.dt, rtol=0.01):
                 even_sampling = True
-            logging.info(f"Data are {'not' if not even_sampling else ''} evenly sampled")
+            logger.info(f"Data are {'not' if not even_sampling else ''} evenly sampled")
 
         if even_sampling:
             est_samples_in_gap = int(max_length / self.dt)
@@ -2272,7 +2275,7 @@ def fill_bad_time_intervals(
             length = bti[1] - bti[0]
             if length > max_length:
                 continue
-            logging.info(f"Filling bad time interval {bti} ({length:.4f} s)")
+            logger.info(f"Filling bad time interval {bti} ({length:.4f} s)")
             epsilon = 1e-5 * length
             added_gtis.append([bti[0] - epsilon, bti[1] + epsilon])
             filt_low_t, filt_low_idx = find_nearest(filtered_times, bti[0])
@@ -2307,7 +2310,7 @@ def fill_bad_time_intervals(
                     new_attrs[attr].append(np.zeros(nevents) + np.nan)
             total_filled_time += length
 
-        logging.info(f"A total of {total_filled_time} s of data were simulated")
+        logger.info(f"A total of {total_filled_time} s of data were simulated")
 
         new_gtis = join_gtis(self.gti, added_gtis)
         new_times = np.concatenate(new_times)
@@ -2497,7 +2500,7 @@ def estimate_segment_size(self, min_counts=None, min_samples=None, even_sampling
                 and np.isclose(mean_data_separation, self.dt, rtol=0.01)
             ):
                 even_sampling = True
-            logging.info(f"Data are {'not' if not even_sampling else ''} evenly sampled")
+            logger.info(f"Data are {'not' if not even_sampling else ''} evenly sampled")
 
         if min_counts is None:
             if even_sampling and hasattr(self, "counts"):

stingray/events.py

@@ -10,6 +10,7 @@
 import numpy as np
 
 from stingray.utils import _int_sum_non_zero
+from stingray.loggingconfig import setup_logger
 
 from .base import StingrayTimeseries
 from .filters import get_deadtime_mask
@@ -21,6 +22,8 @@
 
 __all__ = ["EventList"]
 
+logger = setup_logger()
+
 
 @njit
 def _from_lc_numba(times, counts, empty_times):
@@ -309,7 +312,7 @@ def to_binned_timeseries(self, dt, array_attrs=None):
 
         for attr in array_attrs:
             if getattr(self, attr, None) is not None:
-                logging.info(f"Creating the {attr} array")
+                logger.info(f"Creating the {attr} array")
 
                 attr_dict[attr] = histogram(
                     self.time, bins=nbins, weights=getattr(self, attr), range=ranges

stingray/gti.py

@@ -10,6 +10,7 @@
 from .utils import assign_value_if_none, apply_function_if_none
 from .utils import check_iterables_close, is_sorted
 from stingray.exceptions import StingrayError
+from stingray.loggingconfig import setup_logger
 
 
 __all__ = [
@@ -38,6 +39,8 @@
     "generate_indices_of_segment_boundaries_binned",
 ]
 
+logger = setup_logger()
+
 
 def gti_len(gti):
     """Deprecated, will be removed in version 2.0. Use get_total_gti_length."""
@@ -116,7 +119,7 @@ def load_gtis(fits_file, gtistring=None):
     """
 
     gtistring = assign_value_if_none(gtistring, "GTI")
-    logging.info("Loading GTIS from file %s" % fits_file)
+    logger.info("Loading GTIS from file %s" % fits_file)
     lchdulist = fits.open(fits_file, checksum=True, ignore_missing_end=True)
     lchdulist.verify("warn")
 
@@ -624,7 +627,7 @@ def create_gti_from_condition(time, condition, safe_interval=0, dt=None):
 
     gtis = []
     for idx in idxs:
-        logging.debug(idx)
+        logger.debug(idx)
         startidx = idx[0]
         stopidx = idx[1] - 1
 

stingray/io.py

@@ -14,6 +14,7 @@
 import matplotlib.pyplot as plt
 
 import stingray.utils as utils
+from stingray.loggingconfig import setup_logger
 
 from .utils import assign_value_if_none, is_string, order_list_of_arrays, is_sorted
 from .gti import get_gti_from_all_extensions, load_gtis
@@ -35,6 +36,8 @@
 except AttributeError:  # pragma: no cover
     HAS_128 = False
 
+logger = setup_logger()
+
 
 def rough_calibration(pis, mission):
     """Make a rough conversion between PI channel and energy.
@@ -799,7 +802,7 @@ def ref_mjd(fits_file, hdu=1):
 
     if isinstance(fits_file, Iterable) and not is_string(fits_file):  # pragma: no cover
         fits_file = fits_file[0]
-        logging.info("opening %s" % fits_file)
+        logger.info("opening %s" % fits_file)
 
     hdulist = fits.open(fits_file, ignore_missing_end=True)
 
@@ -843,7 +846,7 @@ def common_name(str1, str2, default="common"):
     common_str = common_str.lstrip("_").lstrip("-")
     if common_str == "":
         common_str = default
-    logging.debug("common_name: %s %s -> %s" % (str1, str2, common_str))
+    logger.debug("common_name: %s %s -> %s" % (str1, str2, common_str))
     return common_str
 
 

stingray/lightcurve.py

@@ -34,11 +34,14 @@
 from stingray.io import lcurve_from_fits
 from stingray import bexvar
 from stingray.base import interpret_times
+from stingray.loggingconfig import setup_logger
 
 __all__ = ["Lightcurve"]
 
 valid_statistics = ["poisson", "gauss", None]
 
+logger = setup_logger()
+
 
 class Lightcurve(StingrayTimeseries):
     """
@@ -288,7 +291,7 @@ def __init__(
         self._time = time
 
         if dt is None and time.size > 1:
-            logging.info(
+            logger.info(
                 "Computing the bin time ``dt``. This can take "
                 "time. If you know the bin time, please specify it"
                 " at light curve creation"
@@ -480,7 +483,7 @@ def bin_hi(self):
         return self._bin_hi
 
     def initial_optional_checks(self, time, counts, err, gti=None):
-        logging.info(
+        logger.info(
             "Checking if light curve is well behaved. This "
             "can take time, so if you are sure it is already "
             "sorted, specify skip_checks=True at light curve "
@@ -515,11 +518,11 @@ def initial_optional_checks(self, time, counts, err, gti=None):
         if nonfinite_flag:
             warnings.warn("There are non-finite points in the data, but they are outside GTIs. ")
 
-        logging.info("Checking if light curve is sorted.")
+        logger.info("Checking if light curve is sorted.")
         unsorted = not is_sorted(time)
 
         if unsorted:
-            logging.warning("The light curve is unsorted.")
+            logger.warning("The light curve is unsorted.")
         return time, counts, err
 
     def check_lightcurve(self):
@@ -875,14 +878,14 @@ def make_lightcurve(toa, dt, tseg=None, tstart=None, gti=None, mjdref=0, use_his
             if gti is not None:
                 tseg = np.max(gti) - tstart
 
-        logging.info("make_lightcurve: tseg: " + str(tseg))
+        logger.info("make_lightcurve: tseg: " + str(tseg))
 
         timebin = int(tseg / dt)
         # If we are missing the next bin by just 1%, let's round up:
         if tseg / dt - timebin >= 0.99:
             timebin += 1
 
-        logging.info("make_lightcurve: timebin:  " + str(timebin))
+        logger.info("make_lightcurve: timebin:  " + str(timebin))
 
         tend = tstart + timebin * dt
         good = (tstart <= toa) & (toa < tend)

stingray/loggingconfig.py

@@ -1,5 +1,6 @@
 import logging
 
+logger = None
 
 class CustomFormatter(logging.Formatter):
 
@@ -25,10 +26,15 @@ def format(self, record):
 
 
 def setup_logger():
-    logger = logging.getLogger(__name__)
-    handler = logging.StreamHandler()
-    formatter = CustomFormatter()
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-    logger.setLevel(logging.DEBUG)
-    return logger
+    global logger
+
+    if not logger:
+        logger = logging.getLogger(__name__)
+        handler = logging.StreamHandler()
+        formatter = CustomFormatter()
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+        logger.setLevel(logging.DEBUG)
+
+
+    return logger

stingray/modeling/parameterestimation.py

@@ -46,6 +46,7 @@
     logmin,
     fitter_to_model_params,
 )
+from stingray.loggingconfig import CustomFormatter
 
 
 class OptimizationResults(object):
@@ -140,6 +141,8 @@ def __init__(self, lpost, res, neg=True, log=None):
             self.log.setLevel(logging.DEBUG)
             if not self.log.handlers:
                 ch = logging.StreamHandler()
+                formatter = CustomFormatter()
+                ch.setFormatter(formatter)
                 ch.setLevel(logging.DEBUG)
                 self.log.addHandler(ch)
 

stingray/modeling/tests/test_parameterestimation.py

@@ -11,6 +11,7 @@
 from stingray.modeling import ParameterEstimation, PSDParEst, OptimizationResults, SamplingResults
 from stingray.modeling import PSDPosterior, set_logprior, PSDLogLikelihood, LogLikelihood
 from stingray.modeling.posterior import fitter_to_model_params
+from stingray.loggingconfig import CustomFormatter
 
 try:
     from statsmodels.tools.numdiff import approx_hess
@@ -46,6 +47,8 @@ def __init__(self, lpost, res, neg, log=None):
             self.log.setLevel(logging.DEBUG)
             if not self.log.handlers:
                 ch = logging.StreamHandler()
+                formatter = CustomFormatter()
+                ch.setFormatter(formatter)
                 ch.setLevel(logging.DEBUG)
                 self.log.addHandler(ch)