Pip package

.github/workflows/ci.yml

@@ -12,25 +12,24 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12']
+        python-version: ['3.10', '3.11', '3.12', '3.13']
 
     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
       run: |
         python -m pip install --upgrade pip
-        python -m pip install pytest coverage
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        pip install -e '.[dev]'
     - name: Test with pytest
       run: |
         coverage run -m pytest -v && coverage xml
     - name: Upload coverage to Codecov
-      if: ${{ matrix.python-version == '3.12' }}
-      uses: codecov/codecov-action@v3
+      if: ${{ matrix.python-version == '3.13' }}
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         fail_ci_if_error: true

.gitignore

@@ -16,3 +16,6 @@ venv*/
 # Test coverage related files and directories
 .coverage
 coverage_html_report
+
+# Package build files and folders
+*.egg-info/

README.md

@@ -5,30 +5,14 @@
 
 ## Introduction
 
-The nuclear mass tables produced by [NUBASE](http://amdc.in2p3.fr/web/nubase_en.html) and [AME](https://www-nds.iaea.org/amdc/) are parsed into pandas dataframes.
-These dataframes are then read with the [dash](https://plotly.com/dash/) module to create an interactive webpage to allow the user to interogate the data they are interested in.
+The nuclear mass tables produced by [NUBASE](http://amdc.in2p3.fr/web/nubase_en.html) and [AME](https://www-nds.iaea.org/amdc/) have unique formats.
+This package does the hard work for you and parses the files into a single pandas dataframe for simpler access.
 
 No guarantee is supplied with regards to the accuracy of the data presented.
 Estimated values are included, please always refer to the original sources.
 All data should, however, be accurate.
 
-Additional functionality and polish will be added as I learn more about [dash](https://plotly.com/dash/).
-In the meantime, suggestions are welcome via [issues](https://github.com/php1ic/pynch/issues) or a pull request.
-
-## Setup
-
-As is the standard, you can confirm you have the necessary modules using the [requirements.txt](./requirements.txt) file
-```bash
-pip install --user -r requirements.txt
-```
-
-## Running
-
-With all of the necessary requirements installed, the below will start the app
-```bash
-python3 app.py
-```
-The console will tell you where to point your browser - likely http://127.0.0.1:8050/.
+Suggestions are welcome via [issues](https://github.com/php1ic/pynch/issues) or a pull request.
 
 ## Mass tables
 
@@ -41,16 +25,23 @@ The data files released by the papers linked below are used to create the mass t
 The NUBASE files are read for all of the data values, with the AME files being used to populate an additional mass excess data field.
 No comparison or validation is done on common values.
 
-## Additional uses
+## Setup
+
+Until the package is added to Python Package Index, the local clone of the repo must be installed from within the top level directory.
+```bash
+pip install -e .
+```
+
+## Usage
 
-If you want to do your own thing with the data, you could import this module, access `MassTable().full_data`, then sort, slice and filter the resultant dataframe to your heart's content.
+Import the module to access `MassTable().full_data`, then sort, slice and filter the resultant dataframe to your heart's content.
 
 For example, track how the accuracy of the mass excess of 18B changes once it is experimentally measured
 ```python
 >>> import pynch.mass_table as mt
 >>> df = mt.MassTable().full_data
->>> df[(df['A'] == 18) & (df['Z'] == 5)][['Experimental', 'NubaseMassExcess', 'NubaseMassExcessError', 'NubaseRelativeError', 'DiscoveryYear']]
-           Experimental  NubaseMassExcess  NubaseMassExcessError  NubaseRelativeError  DiscoveryYear
+>>> df[(df['A'] == 18) & (df['Z'] == 5)][['Experimental', 'NUBASEMassExcess', 'NUBASEMassExcessError', 'NUBASERelativeError', 'DiscoveryYear']]
+           Experimental  NUBASEMassExcess  NUBASEMassExcessError  NUBASERelativeError  DiscoveryYear
 TableYear
 2003              False           52320.0                  800.0             0.015291           1900
 2012               True           51850.0                  170.0             0.003279           2010
@@ -61,7 +52,7 @@ Or for all of the A=100 isotopes from the 2012 table that have a mass-excess err
 ```python
 >>> import pynch.mass_table as mt
 >>> df = mt.MassTable().full_data
->>> df.query('TableYear == 2012 and A == 100 and NubaseMassExcessError < 10.0')[['A', 'Z', 'Symbol', 'DiscoveryYear']]
+>>> df.query('TableYear == 2012 and A == 100 and NUBASEMassExcessError < 10.0')[['A', 'Z', 'Symbol', 'DiscoveryYear']]
              A   Z Symbol  DiscoveryYear
 TableYear
 2012       100  40     Zr           1970
@@ -77,12 +68,12 @@ Or how does the NUBASE mass-excess compare with the AME value for experimentally
 >>> import pynch.mass_table as mt
 >>> df = mt.MassTable().full_data
 >>> # Create a new column comparing the measured values
->>> df['NUBASE-AME'] = df['NubaseMassExcess'] - df['AMEMassExcess']
+>>> df['NUBASE-AME'] = df['NUBASEMassExcess'] - df['AMEMassExcess']
 >>> # Extract the data for measured isotopes and from the latest table
 >>> df_comparison = df.query('TableYear == 2020 and Experimental == True')
 >>> # Sort the difference in measured data by absolute value and print the columns we are interested in
->>> df_comparison.sort_values(by=['NUBASE-AME'], key=abs, ascending=False)[['A', 'Z', 'Symbol', 'NubaseMassExcess', 'AMEMassExcess', 'NUBASE-AME']].head(n=10)
-             A   Z Symbol  NubaseMassExcess  AMEMassExcess  NUBASE-AME
+>>> df_comparison.sort_values(by=['NUBASE-AME'], key=abs, ascending=False)[['A', 'Z', 'Symbol', 'NUBASEMassExcess', 'AMEMassExcess', 'NUBASE-AME']].head(n=10)
+             A   Z Symbol  NUBASEMassExcess  AMEMassExcess  NUBASE-AME
 TableYear
 2020       221  91     Pa           20370.0      20374.937      -4.937
 2020        57  23      V          -44440.0     -44435.063      -4.937

pynch/mass_table.py

@@ -1,4 +1,5 @@
 """Functionality to parse all data file into a single object."""
+import importlib.resources
 import logging
 import pathlib
 import typing
@@ -8,7 +9,7 @@
 from pynch.ame_mass_parse import AMEMassParser
 from pynch.ame_reaction_1_parse import AMEReactionParserOne
 from pynch.ame_reaction_2_parse import AMEReactionParserTwo
-from pynch.nubase_parse import NubaseParser
+from pynch.nubase_parse import NUBASEParser
 
 
 class MassTable:
@@ -19,8 +20,8 @@ class MassTable:
 
     def __init__(self):
         """Do all of the work at construction."""
-        # Assume this file is some/path/pynch/pynch/mass_table.py
-        self.data_path = pathlib.Path(__file__) / ".." / ".." / "data"
+        self.data_path = importlib.resources.files("pynch.data")
+        print(self.data_path)
         self.existing_years = [2003, 2012, 2016, 2020]
         self.nubase = pd.concat([self._parse_nubase_data(y) for y in self.existing_years], ignore_index=True)
         self.ame = pd.concat([self._parse_ame_data(y) for y in self.existing_years], ignore_index=True)
@@ -78,7 +79,7 @@ def _validate_year(self, year: int) -> int:
     def _parse_nubase_data(self, year: int) -> pd.DataFrame:
         """Get the nubase for the given year as a pandas.DataFrame."""
         year = self._validate_year(year)
-        return NubaseParser(self._get_nubase_datafile(year), year).read_file()
+        return NUBASEParser(self._get_nubase_datafile(year), year).read_file()
 
     def _parse_ame_data(self, year: int) -> pd.DataFrame:
         """Combine all the AME files from the given year into a pandas.DataFrame."""
@@ -97,14 +98,14 @@ def _combine_all_data(self) -> pd.DataFrame:
         common_columns = ['A', 'Z', 'N', 'TableYear', 'Symbol']
         df = self.nubase.merge(self.ame, on=common_columns)
 
-        df["NubaseRelativeError"] = abs(
-            df["NubaseMassExcessError"] / df["NubaseMassExcess"]
+        df["NUBASERelativeError"] = abs(
+            df["NUBASEMassExcessError"] / df["NUBASEMassExcess"]
         )
         df["AMERelativeError"] = abs(df["AMEMassExcessError"] / df["AMEMassExcess"])
 
         # 12C has a 0.0 +/ 0.0 mass excess by definition so calculating relative error -> NaN
         # Set the value to 0.0 as that's what it is
-        df.loc[(df.Symbol == "C") & (df.A == 12), "NubaseRelativeError"] = 0.0
+        df.loc[(df.Symbol == "C") & (df.A == 12), "NUBASERelativeError"] = 0.0
         df.loc[(df.Symbol == "C") & (df.A == 12), "AMERelativeError"] = 0.0
 
         # 198Au has a typo in it's decay mode in the 2012 table. It is recorded as '-'

pynch/nubase_file.py

@@ -2,7 +2,7 @@
 from pynch.parse import Parse
 
 
-class NubaseFile(Parse):
+class NUBASEFile(Parse):
     """Easy access to where variables are in the NUBASE file.
 
     The NUBASE data file is formatted by location in the line, values exist