Implement logging

pbjam/__init__.py

@@ -4,12 +4,27 @@
 import os
 PACKAGEDIR = os.path.abspath(os.path.dirname(__file__))
 
+# Setup global pbjam logger
+import logging
+logger = logging.getLogger(__name__)
+logger.setLevel('DEBUG')  # <--- minimum level for global pbjam package logger
+
+# Setup console handler
+from .jar import _stream_handler
+console_handler = _stream_handler(level='INFO')
+logger.addHandler(console_handler)
+logger.debug(f'Initializing {__name__}')
+
 from .version import __version__
+logger.debug(f'version == {__version__}')
+
 from .priors import kde
 from .session import session
 from .asy_peakbag import asymp_spec_model, asymptotic_fit
 from .peakbag import peakbag
 from .ellone import ellone
 from .star import star
 from .mcmc import mcmc
-from .mcmc import nested
+from .mcmc import nested
+
+logger.debug(f'Initialized {__name__}')

pbjam/asy_peakbag.py

@@ -12,9 +12,13 @@
 import pandas as pd
 import scipy.stats as scist
 from .plotting import plotting
-from .jar import normal
+from .jar import normal, debug
 from collections import OrderedDict
-import warnings
+import warnings, logging
+
+logger = logging.getLogger(__name__)
+debugger = debug(logger)
+
 
 class asymp_spec_model():
     """Class for spectrum model using asymptotic relation.
@@ -35,7 +39,7 @@ class asymp_spec_model():
         Number of radial order to fit.
 
     """
-
+    # @debugger
     def __init__(self, f, norders):
         self.f = np.array([f]).flatten()
         self.norders = int(norders)
@@ -326,7 +330,7 @@ class asymptotic_fit(plotting, asymp_spec_model):
         science results!
 
     """
-
+    # @debugger
     def __init__(self, st, norders=None):
 
         self.pg = st.pg
@@ -354,7 +358,11 @@ def __init__(self, st, norders=None):
         self.path = st.path
 
         st.asy_fit = self
-
+
+    def __repr__(self):
+        return f'<pbjam.asymptotic_fit norders={self.norders}>'
+
+    @debugger 
     def __call__(self, method, developer_mode):
         """ Setup, run and parse the asymptotic relation fit.
 
@@ -400,7 +408,6 @@ def __call__(self, method, developer_mode):
 
         return {'modeID': self.modeID, 'summary': self.summary}
 
-
     def prior(self, p):
         """ Calculates the log prior 
 
@@ -514,7 +521,7 @@ def _get_summary_stats(self, fit):
 
         return summary
 
-
+    @debugger
     def get_modeIDs(self, fit, norders):
         """ Set mode ID in a dataframe
 

pbjam/data/pbjam_references.bib

@@ -1,5 +1,5 @@
 
-@article{nested, 
+@article{cpnest, 
     title={johnveitch/cpnest: Minor optimisation}, 
     DOI={10.5281/zenodo.835874}, 
     publisher={Zenodo}, 

pbjam/ellone.py

@@ -31,10 +31,15 @@
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.utils import shuffle as skshuffle
 import hdbscan as Hdbscan
-import warnings
+import warnings, logging
 from .plotting import plotting
 import astropy.units as units
 import lightkurve as lk
+from .jar import debug
+
+logger = logging.getLogger(__name__)
+debugger = debug(logger)
+
 
 class ellone(plotting):
     """ Basic l=1 detection 
@@ -71,7 +76,7 @@ class ellone(plotting):
     instead, in which case the l=2,0 modes may be picked up instead of the l=1.
 
     """
-    
+
     def __init__(self, pbinst=None, f=None, s=None):
 
         if pbinst:
@@ -101,7 +106,8 @@ def __init__(self, pbinst=None, f=None, s=None):
         self.hdblabels = None
         self.hdbX = None
         self.hdb_clusterN = None
-
+
+    @debugger
     def residual(self,):
         """ Compute the residual after dividing out l=2,0
 
@@ -129,7 +135,7 @@ def residual(self,):
             idx = (flad[0] <= self.f) & (self.f <= flad[-1])
             res[idx] /= mod[i,:]
         return res
-     
+
     def binning(self, nbin):
         """ Simply mean-binning
 
@@ -177,7 +183,7 @@ def H0test(self, fbin, sbin, nbin, dnu, reject=0.1):
         idx = k < reject
         return idx, k
 
-
+    @debugger
     def H0_inconsistent(self, dnu, Nmax, rejection_level):
         """ Find bins inconsistent with noise
 
@@ -220,6 +226,7 @@ def H0_inconsistent(self, dnu, Nmax, rejection_level):
 
         return nu, N, pH0s
 
+    @debugger
     def clustering_preprocess(self, nu, N, limits = (0, 100000)):
         """ Preprocess the samples before clustering
 
@@ -268,6 +275,7 @@ def span(self, x):
 
         return max(x)-min(x)
 
+    @debugger
     def clustering(self, nu, N, Nmax, outlier_limit=0.5, cluster_prob=0.9):
         """ Perform HDBscan clustering
 
@@ -326,8 +334,7 @@ def clustering(self, nu, N, Nmax, outlier_limit=0.5, cluster_prob=0.9):
 
         return nus[1:], nstds[1:]
 
-
-
+    @debugger
     def get_ell1(self, dnu):
         """ Estimate frequency of l=1 modes (p-modes)
 
@@ -380,6 +387,7 @@ def get_ell1(self, dnu):
 
         return nul1s, nul1s_std
 
+    @debugger
     def __call__(self, dnu, Nmax = 30, rejection_level = 0.1):
         """ Perform all the steps to estimate l=1 frequencies