Merge pull request #181 from NNPDF/allow_different_ekos

Allow evolution and convolution with different ekos for a single grid

benchmarks/bench_autosv.py

@@ -40,7 +40,7 @@ def benchmark_compute_ren_sv_grid(test_files, tmp_path, test_pdf, lhapdf_path):
     plusrensv_grid = pineappl.grid.Grid.read(plusrensv_grid_path)
     sv_list = [(0.5, 1.0), (2.0, 1.0)]  # Only ren sv have to be tested
     bin_number = to_test_grid.bins()
-    to_test_res = to_test_grid.convolute_with_one(
+    to_test_res = to_test_grid.convolve_with_one(
         2212,
         pdf.xfxQ2,
         pdf.alphasQ2,
@@ -49,7 +49,7 @@ def benchmark_compute_ren_sv_grid(test_files, tmp_path, test_pdf, lhapdf_path):
         np.array([], dtype=bool),
         sv_list,
     ).reshape(bin_number, len(sv_list))
-    plusrensv_res = plusrensv_grid.convolute_with_one(
+    plusrensv_res = plusrensv_grid.convolve_with_one(
         2212,
         pdf.xfxQ2,
         pdf.alphasQ2,

benchmarks/bench_cli.py

@@ -81,12 +81,12 @@ def benchmark_compare_cli(lhapdf_path, test_files, test_pdf):
     with lhapdf_path(test_pdf):
         result = runner.invoke(
             command,
-            ["compare", str(grid_path), str(fk_path), "2", "0", "NNPDF40_nlo_as_01180"],
+            ["compare", str(fk_path), str(grid_path), "2", "0", "NNPDF40_nlo_as_01180"],
         )
     assert "yll left" in result.output
 
 
-def benchmark_convolute_cli(test_files, tmp_path):
+def benchmark_convolve_cli(test_files, tmp_path):
     grid_path = pathlib.Path(
         test_files / "data/grids/400/HERA_NC_225GEV_EP_SIGMARED.pineappl.lz4"
     )
@@ -95,7 +95,7 @@ def benchmark_convolute_cli(test_files, tmp_path):
     runner = CliRunner()
     result = runner.invoke(
         command,
-        ["convolute", str(grid_path), str(eko_path), str(fk_path), "2", "0"],
+        ["convolve", str(fk_path), str(grid_path), "2", "0", str(eko_path)],
     )
     assert "Optimizing for Nf6Ind" in result.output
 

benchmarks/bench_evolve.py

@@ -120,7 +120,7 @@ def benchmark_evolve_grid(tmp_path, lhapdf_path, test_files, test_pdf):
                 1.0,
                 1.0,
                 assumptions=assumptions,
-                comparison_pdf="NNPDF40_nnlo_as_01180",
+                comparison_pdf1="NNPDF40_nnlo_as_01180",
             )
             # check metadata is there - fixes https://github.com/NNPDF/pineko/issues/70
             fk = pineappl.fk_table.FkTable.read(target_path)

benchmarks/bench_kfactor.py

@@ -28,7 +28,7 @@ def benchmark_kfactor_inclusion(test_files, tmp_path, test_pdf, lhapdf_path):
     order_mask_nnloQCD = pineappl.grid.Order.create_mask(
         pluskfactor_grid.orders(), 3, 0, True
     )
-    to_test_res_nlo = pluskfactor_grid.convolute_with_one(
+    to_test_res_nlo = pluskfactor_grid.convolve_with_one(
         2212,
         pdf.xfxQ2,
         pdf.alphasQ2,
@@ -37,7 +37,7 @@ def benchmark_kfactor_inclusion(test_files, tmp_path, test_pdf, lhapdf_path):
         np.array([], dtype=bool),
         sv_list,
     ).reshape(bin_number, len(sv_list))
-    to_test_res_nnlo = pluskfactor_grid.convolute_with_one(
+    to_test_res_nnlo = pluskfactor_grid.convolve_with_one(
         2212,
         pdf.xfxQ2,
         pdf.alphasQ2,

benchmarks/bench_regression.py

@@ -125,12 +125,12 @@ def benchmark_regression(tmp_path, dataset):
         fkt = FkTable.read(
             tmp_path / "data" / "fktables" / str(THEORYID) / f"{grid_name}.pineappl.lz4"
         )
-        result.append(fkt.convolute_with_one(2212, pdf.xfxQ2))
+        result.append(fkt.convolve_with_one(2212, pdf.xfxQ2))
     result = np.concatenate(result)
 
     if not regression_path.exists():
         np.save(regression_path, result)
         raise FileNotFoundError("Regression did not exist and has been regenerated")
 
     regression_data = np.load(regression_path)
-    np.testing.assert_allclose(regression_data, result)
+    np.testing.assert_allclose(regression_data, result, rtol=4e-6)

benchmarks/bench_theory.py

@@ -58,8 +58,13 @@ def benchmark_inherit_grids(test_files):
 
 
 def benchmark_inherit_eko(tmp_path):
-    from_eko = theory_obj.ekos_path()
-    theory_obj.inherit_eko("TestEko", from_eko, tmp_path)
+    for grid in theory_obj.grids_path().iterdir():
+        name = grid.stem.split(".")[0]
+        # here NUTEV eko is not present
+        if (theory_obj.ekos_path() / f"{name}.tar").exists():
+            theory_obj.inherit_eko(name, grid, tmp_path)
+            assert tmp_path.is_dir()
+            assert (tmp_path / f"{name}.tar").exists()
 
 
 def benchmark_inherit_ekos(test_files):
@@ -166,9 +171,9 @@ def benchmark_fk(test_files, test_configs):
     )
     theory_obj_hera.opcard(grid_name, pathlib.Path(test_files / grid_path), tcard)
 
-    theory_obj_hera.fk(grid_name, grid_path, tcard, pdf=None)
+    theory_obj_hera.fk(grid_name, grid_path, tcard, pdf1=None, pdf2=None)
     # test overwrite function
-    theory_obj_hera.fk(grid_name, grid_path, tcard, pdf=None)
+    theory_obj_hera.fk(grid_name, grid_path, tcard, pdf1=None, pdf2=None)
     log_path = pathlib.Path(test_files / "logs/fk/400-HERA_NC_225GEV_EP_SIGMARED.log")
     if os.path.exists(log_path):
         os.remove(log_path)

docs/source/overview/running.rst

@@ -44,13 +44,13 @@ Generating the FK Table
 -----------------------
 
 You need to have the |EKO| computed in the previous step.
-Then you can convolute the |EKO| with the grids by running::
+Then you can convolve the |EKO| with the grids by running::
 
   pineko theory fks THEORY_ID DATASET1 DATASET2 ...
 
-Note that you can also convolute a single grid with a single eko (obtaining a single FK table) by running::
+Note that you can also convolve a single grid with a single eko (obtaining a single FK table) by running::
 
-  pineko convolute GRID OPCARD FKTABLE MAX_AS MAX_AL
+  pineko convolve FKTABLE GRID MAX_AS MAX_AL OP_PATH_1 OP_PATH_2
 
 If necessary it is possible to specify the values of the *renormalization* and *factorization* scale variations with
 the options ``--xir`` and ``--xif``.
@@ -89,9 +89,9 @@ Comparing grids and FK tables
 """""""""""""""""""""""""""""
 
 With the command ``pineko compare`` it is possible to compare the predictions as provided by the grid
-(convoluted with a |PDF|) with the predictions as provided by the |FK| table. This is done like
+(convolved with a |PDF|) with the predictions as provided by the |FK| table. This is done like
 
-  pineko compare GRID FKTABLE MAX_AS MAX_AL PDF
+  pineko compare FKTABLE GRID MAX_AS MAX_AL PDF_1 PDF_2
 
 again eventually specifying the values of *renormalization* and *factorization* scales with the
 appropriate options.