add a workflow that tests some pdfs

.github/workflows/test_many_pdfs.yml

@@ -0,0 +1,24 @@
+name: Check interpolation for many PDFs
+on: [push]
+
+jobs:
+  pdftest:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Conda 
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+            python-version: "3.11"
+            auto-update-conda: true
+      - name: Install dependencies, package and LHAPDF
+        shell: bash --login {0}
+        run: |
+          conda install lhapdf -c conda-forge
+          pip install .
+          lhapdf-management update --init
+      - name: Test a random assortment of 100 PDFs
+        shell: bash -l {0}
+        run: |
+          export PDFFLOW_LOG_LEVEL=0
+          python benchmarks_and_tests/test_many_pdfs.py --yes --verbose -n 50

benchmarks_and_tests/test_many_pdfs.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+"""
+    This little script uses the lhapdf python interface to:
+    1) Get the latest list of PDF sets
+    2) Download a subset of them (or all)
+    3) Test with a random set of points that the LHPADF and pdfflow produce the same result
+
+    Uses lhapdf-management to install the PDFs programatically :)
+"""
+
+import sys
+import tempfile
+from argparse import ArgumentParser
+from pathlib import Path
+
+import lhapdf
+import numpy as np
+import pdfflow
+from lhapdf_management import environment, pdf_list, pdf_update
+
+lhapdf.setVerbosity(0)
+
+
+def _compare_w_lhapdf(pdf, npoints=1000, tolerance=1e-6):
+    """Compare a LHAPDF and pdfflow  pdfs
+    for an array of npoints"""
+    # Now get a random member
+    m = np.random.randint(len(loaded_pdf), dtype=int)
+
+    pdfflow_pdf = pdfflow.mkPDF(f"{pdf}/{m}")
+    lhapdf_pdf = lhapdf.mkPDF(f"{pdf}/{m}")
+
+    # Get n points for x between 0 and 1
+    xx = np.random.rand(npoints)
+    # And n points for q between 1 and 1e5 GeV
+    qq = (1.0 + np.random.rand(npoints) * np.sqrt(pdfflow_pdf.q2max)) ** 2
+
+    # Make sure the order is the same as in pdfflow
+    flavors = pdf.info["Flavors"]
+    lhapdf_results = lhapdf_pdf.xfxQ2(flavors, xx, qq)
+
+    lres = np.array(lhapdf_results)
+    pres = pdfflow_pdf.py_xfxQ2_allpid(xx, qq).numpy()
+
+    np.testing.assert_allclose(pres, lres, rtol=tolerance)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description=__doc__)
+    parser.add_argument("-d", "--dir", help="Directory where to download the sets", type=Path)
+    parser.add_argument("-y", "--yes", help="Respond yes to every question", action="store_true")
+    parser.add_argument("-v", "--verbose", help="Be verbose", action="store_true")
+    parser.add_argument(
+        "-a",
+        "--all",
+        help="Try really ALL sets, otherwise, do a random selection of N of them",
+        action="store_true",
+    )
+    parser.add_argument(
+        "-n",
+        "--npdfs",
+        help="If all is not given, hoy many PDFs to actually test (default 50)",
+        type=int,
+        default=50,
+    )
+    parser.add_argument(
+        "-p",
+        "--points",
+        help="How many points in x/q to test per PDF set (default 1000)",
+        type=int,
+        default=1000,
+    )
+    parser.add_argument(
+        "-t", "--tolerance", help="Tolerance for the test (default 1e-6)", type=float, default=1e-6
+    )
+
+    args = parser.parse_args()
+
+    if args.dir is None:
+        target_dir = Path(tempfile.mkdtemp())
+    else:
+        target_dir = args.dir
+
+    if not args.yes:
+        print(
+            f"""You are about to download a potentially large number of PDF sets to {target_dir.absolute()}
+This is likely to be heavy in both your storage and your bandwith."""
+        )
+        yn = input(" > Do you want to continue? [Y/N] ")
+        if not yn.lower() in ("y", "yes", "ye", "si"):
+            sys.exit(0)
+
+    target_dir.mkdir(exist_ok=True)
+
+    # Set the datapath
+    environment.datapath = target_dir
+    lhapdf.setPaths([target_dir.as_posix()])
+
+    # Get the latest PDF list
+    pdf_update()
+
+    # And now list them all
+    list_of_pdfs = pdf_list()
+
+    # if not --all, take a mask of N PDFs
+    if not args.all:
+        if args.npdfs > len(list_of_pdfs):
+            raise ValueError(
+                f"The value of N ({args.npdfs}) cannot be greater than the number of PDFs available ({len(list_of_pdfs)}), use --all if you just want to test all of them"
+            )
+        list_of_pdfs = np.random.choice(list_of_pdfs, size=args.npdfs, replace=False)
+
+    # And time to install!
+    failed_pdfs = []
+    for pdf in list_of_pdfs:
+        if args.verbose:
+            print(f"Testing {pdf}... ", end="")
+        try:
+            pdf.install()
+            # Try loading the PDF
+            loaded_pdf = pdf.load()
+            _compare_w_lhapdf(loaded_pdf, npoints=args.points, tolerance=args.tolerance)
+        except Exception as e:
+            # We are not going to care that much _how_ the failure happened
+            if args.verbose:
+                print(f"{pdf} failed!")
+            failed_pdfs.append((pdf, e))
+
+    if failed_pdfs:
+        print("\nThe failed pdfs are: ")
+        for pdf, error in failed_pdfs:
+            print(f"{pdf} with {error}")
+        raise Exception("Some PDFs failed!")
+    else:
+        print("\nNo PDF failed the test!")