Move resources to top level directory

.github/workflows/pytest.yml → .github/workflows/build_and_test.yml

@@ -19,6 +19,10 @@ on:
     # * is a special character in YAML so you have to quote this string
     - cron:  '25 9 * * 2'
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   build:
 
@@ -44,8 +48,12 @@ jobs:
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
     - name: Run pytest
+      run: pytest
+    - name: Test mu.py
       run: |
-        pytest
+        pip install -e .[resources]
+        ./resources/mu/mu.py -o fake/path/mymudet.pkl --plight pl_step_1000 resources/mu/mmc/ice_allm97.pklz
+        pytest -k test_pdet
 
   results:
     if: ${{ always() }}

README.md

@@ -57,14 +57,16 @@ See `examples/plots.py` for more detailed examples.
 
 ![Pdet](/paper/figs_for_readme/prpl_step1000.png?raw=true)
 
-To calculate the passing fraction requires knowing the muon detection pdf as a function of the overburden and energy of the muon at the surface. This is constructed from a convolution of the muon reaching probability and the detector response. The muon reaching probability is constructed from MMC simulations and is provided for propagation in ice in `resources/mu/mmc/ice.pklz`. The detector response probability must be defined in `resources/mu/pl.py` as a function of the muon energy (at detector). Then, construct the overall muon detection pdf and place it into the correct location.
+To calculate the passing fraction requires knowing the muon detection probability as a function of the overburden and energy of the muon at the surface. This is constructed from a convolution of the muon reaching probability and the detector response. The scripts for generating the necessary files are not packaged but provided in the `resources/` directory, which can be obtained with a clone of this repository. They also require some extra dependencies, which can be installed with `pip install nuVeto[resources]`.
+
+The muon reaching probability is constructed from MMC simulations and is provided for propagation in ice in `resources/mu/mmc/ice_(allm97|bb).pklz` for two different cross section parameterizations. The detector response probability must be defined in `resources/mu/pl.py` as a function of the muon energy (at detector). Then, construct the overall muon detection probability.
 
 ```bash
-cd nuVeto/resources/mu
-./mu.py -o ../../prpl/mymudet.pkl --plight pl_step_1000 mmc/ice_allm97.pklz
+cd resources/mu
+./mu.py -o mymudet.pkl --plight pl_step_1000 mmc/ice_allm97.pklz
 ```
 
-To use the newly generated file, pass it as a string to the `prpl` argument.
+To use the newly generated file, pass the stem without file extension as a string to the `prpl` argument.
 ```bash
 passing(enu, cos_theta, prpl='mymudet')`.
 ```

nuVeto/__init__.py

@@ -1,2 +1,2 @@
 __all__ = ['mu', 'utils', 'nuveto',
-           'barr_uncertainties', 'external', 'examples']
+           'uncertainties', 'external', 'examples']

pyproject.toml

@@ -29,13 +29,13 @@ classifiers = [
 
 [project.optional-dependencies]
 plotting = ["matplotlib"]
-resources = ["pythia8", "matplotlib", "pandas"]
+resources = ["pythia8mc", "matplotlib", "pandas"]
 testing = ["pytest"]
 
 [project.urls]
 Repository = "https://github.com/tianluyuan/nuVeto.git"
 
 [tool.setuptools.packages.find]
-exclude = ["paper*"]
+exclude = ["paper*", "resources*"]
 
 [tool.setuptools_scm]

nuVeto/resources/mu/mu.py → resources/mu/mu.py

@@ -1,15 +1,16 @@
 #!/usr/bin/env python3
-
-import os
+from pathlib import Path
 from collections import namedtuple
 import pickle
 import gzip
 import argparse
 import numpy as np
 import pandas as pd
+import logging
 from scipy import interpolate
-from nuVeto.resources.mu import pl
+from importlib import resources
 from nuVeto.utils import calc_bins, centers
+import pl
 
 
 def hist_preach(infile):
@@ -35,7 +36,7 @@ def hist_preach(infile):
 def int_ef(preach, plight):
     """ integate p_reach*p_light over e_f to reduce dimensionality for interpolator
     """
-    if isinstance(preach, str) and os.path.isfile(preach):
+    if isinstance(preach, str) and Path(preach).is_file():
         try:
             preach = pickle.load(gzip.open(preach, 'rb'), encoding='latin1')
         except IOError:
@@ -55,7 +56,9 @@ def interp(preach, plight):
     return interpolate.RegularGridInterpolator((df.index, df.columns), df.values, bounds_error=False, fill_value=None)
 
 
-if __name__ == '__main__':
+def main():
+    logger = logging.getLogger('mu.py')
+    logging.basicConfig(encoding='utf-8', level=logging.INFO)
     parser = argparse.ArgumentParser(
         description='Generate muon detection probability')
     parser.add_argument('mmc', metavar='MMC',
@@ -65,14 +68,22 @@ def interp(preach, plight):
                         help='choice of a plight function to apply as defined in pl.py')
     parser.add_argument('--noconvolution', default=False, action='store_true',
                         help='Generate pdfs of preach from raw MMC output and save to pklz')
-    parser.add_argument('-o', dest='output', default='mu.pkl',
-                        help='output file. To be read in this needs to be in "nuVeto/data/prpl/"')
+    parser.add_argument('-o', dest='output', default='mu.pkl', type=Path,
+                        help='output file. If --noconvolution is False this will be saved into the necessary package directory.')
 
     args = parser.parse_args()
     if args.noconvolution:
+        output = args.output
         hpr = hist_preach(args.mmc)
-        pickle.dump(hpr, gzip.open(args.output, 'wb'), protocol=-1)
+        pickle.dump(hpr, gzip.open(output, 'wb'), protocol=-1)
     else:
+        output = resources.files('nuVeto') / 'data' / 'prpl' / args.output.name
+        if args.output.name != str(args.output):
+            logger.warning(f'Overriding {args.output} to {output}. To suppress this warning pass the filename only.')
         intp = interp(args.mmc, getattr(pl, args.plight))
-        pickle.dump(intp, open(args.output, 'wb'), protocol=-1)
-    print(f'Output pickled into {args.output}')
+        pickle.dump(intp, open(output, 'wb'), protocol=-1)
+    logger.info(f'Output pickled into {output}')
+
+
+if __name__ == '__main__':
+    main()