Merge pull request #315 from SNEWS2/JostMigenda/Cleanup

Various bits of cleanup

.github/workflows/integration.yml

@@ -23,9 +23,9 @@ jobs:
         python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies

.github/workflows/publish.yml

@@ -13,9 +13,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.11'
     - name: Install dependencies

.github/workflows/tests.yml

@@ -24,11 +24,11 @@ jobs:
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       # Set up the Python environment and dependencies
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies

python/snewpy/__init__.py

@@ -9,6 +9,7 @@
 
 from ._version import __version__
 from pathlib import Path
+from sys import exit
 import os
 
 try:

python/snewpy/flux.py

@@ -60,8 +60,6 @@
 from scipy.integrate import cumulative_trapezoid
 from scipy.interpolate import interp1d
 from enum import IntEnum
-
-from copy import copy
 from functools import wraps
 
 #list of units which will be used as units for decomposition inside the Container
@@ -285,16 +283,15 @@ def integrate(self, axis:Union[Axes,str], limits:np.ndarray=None)->'Container':
         yc = cumulative_trapezoid(self.array, x=ax, axis=axis, initial=0)
         #get first and last value to use as the fill values
         yc_limits = (yc.take(0,axis=axis), yc.take(-1,axis=axis)) 
-        #this will make the _intergal constant if it gets out of bounds, 
+        #this will make the _integral constant if it gets out of bounds,
         # i.e. effectively the flux outside of bounds is zero
         _integral = interp1d(x=ax, y=yc, fill_value=yc_limits, axis=axis, bounds_error=False)
         array = np.diff(_integral(limits),axis=axis) << (self.array.unit*ax.unit)
         axes = list(self.axes)
         axes[axis] = limits
         #choose the proper class
         return Container(array, *axes, integrable_axes=self._integrable_axes.difference({axis}))
-        return result
-
+
     def integrate_or_sum(self, axis:Union[Axes,str])->'Container':
         if self.can_integrate(axis):
             return self.integrate(axis)

python/snewpy/models/presn_loaders.py

@@ -41,7 +41,7 @@ class Odrzywolek_2010(SupernovaModel):
     def __init__(self, filename:str):
         df = pd.read_csv(
             self.request_file(filename),
-            delim_whitespace=True,
+            sep='\s+',
             skiprows=1,
             usecols=[1,6,7,8],
             names=["time","a","alpha","b"],
@@ -83,7 +83,7 @@ def __init__(self, filename:str):
         df = pd.read_csv(
             self.request_file(filename),
             comment="#",
-            delim_whitespace=True,
+            sep='\s+',
             names=["time","Enu",Flavor.NU_E,Flavor.NU_E_BAR,Flavor.NU_X,Flavor.NU_X_BAR],
             usecols=range(6),
         )

python/snewpy/rate_calculator.py

@@ -13,7 +13,6 @@
 from astropy import units as u
 from warnings import warn
 from typing import Dict
-from dataclasses import dataclass
 from typing import Callable
 import scipy.stats as st
 

python/snewpy/snowglobes_interface.py

@@ -83,7 +83,7 @@ def __init__(self, base_dir:Path='', detectors:str="all", detector_effects=True)
             self._load_smearing_matrices(self.base_dir/'smear')
 
     def _load_detectors(self, path:Path, detectors:str):
-        df = pd.read_table(path,names=['name','mass','factor'], delim_whitespace=True, comment='#')
+        df = pd.read_table(path, names=['name', 'mass', 'factor'], sep='\s+', comment='#')
         df['tgt_mass']=df.mass*df.factor
 
         if detectors == 'all':
@@ -108,17 +108,17 @@ def _load_channels(self, chan_dir):
             if l.startswith('%'):
                 # Format 1: explicit binning, same for all channels
                 tokens = l.split()[1:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
             elif l.startswith('SN_nu'):
                 # Format 2: explicit binning, differs per channel
                 tokens = l.split()[6:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'], usecols=range(1,6))
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'], usecols=range(1,6))
                 # drop coherent scattering channels, which have different binning
                 df = df[df["name"].str.startswith('coh_') == False]
             else:
                 # Format 3: no binning specified, use SNOwGLoBES default values
                 tokens = '% 200 0.0005 0.100 200 0.0005 0.100'.split()[1:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
 
             self.channels[material] = df
 
@@ -136,7 +136,7 @@ def _load_efficiency_vectors(self, path):
         for detector in self.detectors:
             res_det = {}
             for file in (path/str(detector)).glob(f'effic_*_{detector}.dat'):
-                channel =  file.stem[len('effic_'):-len(detector)-1]
+                channel = file.stem[len('effic_'):-len(detector)-1]
                 logger.debug(f'Reading file ({detector},{channel}): {file}')
                 with open(file) as f:
                     effs = np.fromiter(f.readlines()[0].split("{")[-1].split("}")[0].split(","), float)