added a test for eko convolution and LHAPDF info files

NNPDF · Jun 6, 2023 · 5ad09fe · 5ad09fe
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diff --git a/n3fit/src/n3fit/tests/regressions/quickcard_1.json b/n3fit/src/n3fit/tests/regressions/quickcard_1.json
@@ -51,45 +51,45 @@
   ],
   "stop_epoch": 1000,
   "best_epoch": 999,
-  "erf_tr": 31.9080753326416,
-  "erf_vl": 29.347797393798828,
-  "chi2": 20.596416473388672,
+  "erf_tr": 31.90971565246582,
+  "erf_vl": 29.347829818725586,
+  "chi2": 20.596195220947266,
   "pos_state": "POS_VETO",
   "arc_lengths": [
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-    1.199236066426708,
-    1.088847738155109,
-    1.3196733515313401,
-    1.0838076324596133
+    1.0344419604975799,
+    1.199249640484144,
+    1.0880527116547254,
+    1.3247058914025585,
+    1.08332657756542
   ],
   "integrability": [
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-    0.00263668211118695,
-    0.0002383394112248638,
-    3.2648048400878915,
-    0.003965188574513334
+    0.002648560388475274,
+    0.0026485603884783826,
+    0.00023979029810894392,
+    3.264821648597718,
+    0.003986286144939122
   ],
   "timing": {
     "walltime": {
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+      "Total": 37.674946308135986,
       "start": 0.0,
-      "replica_set": 0.23225688934326172,
-      "replica_fitted": 20.825780630111694,
-      "replica_set_to_replica_fitted": 20.593523740768433
+      "replica_set": 0.2974090576171875,
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     "cputime": {
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+      "Total": 95.89040509899999,
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-      "replica_fitted": 25.856265199,
-      "replica_set_to_replica_fitted": 21.003564205
+      "replica_set": 4.001058462,
+      "replica_fitted": 95.890103173,
+      "replica_set_to_replica_fitted": 91.889044711
     }
   },
   "version": {
-    "keras": "2.8.0",
-    "tensorflow": "2.8.2, mkl=False",
+    "keras": "2.11.0",
+    "tensorflow": "2.11.0, mkl=True",
     "numpy": "1.24.3",
-    "nnpdf": "4.0.6.742+g00e5c8e08-dev",
-    "validphys": "4.0.6.742+g00e5c8e08-dev"
+    "nnpdf": "4.0.6.743+g88e22f616-dev",
+    "validphys": "4.0.6.743+g88e22f616-dev"
   }
 }
diff --git a/n3fit/src/n3fit/tests/regressions/quickcard_2.json b/n3fit/src/n3fit/tests/regressions/quickcard_2.json
@@ -3,7 +3,7 @@
     {
       "fl": "sng",
       "smallx": 1.1098793745040894,
-      "largex": 2.694117546081543,
+      "largex": 2.6941299438476562,
       "trainable": true
     },
     {
@@ -15,25 +15,25 @@
     {
       "fl": "v",
       "smallx": 0.7499998211860657,
-      "largex": 1.719839096069336,
+      "largex": 1.7198402881622314,
       "trainable": true
     },
     {
       "fl": "v3",
-      "smallx": 0.21590425074100494,
-      "largex": 1.3583499193191528,
+      "smallx": 0.21582084894180298,
+      "largex": 1.3583420515060425,
       "trainable": true
     },
     {
       "fl": "v8",
-      "smallx": 0.7548623085021973,
-      "largex": 2.390861749649048,
+      "smallx": 0.754185676574707,
+      "largex": 2.3908095359802246,
       "trainable": true
     },
     {
       "fl": "t3",
       "smallx": 1.422916054725647,
-      "largex": 2.2746126651763916,
+      "largex": 2.2746129035949707,
       "trainable": true
     },
     {
@@ -44,52 +44,52 @@
     },
     {
       "fl": "cp",
-      "smallx": 0.23281367123126984,
+      "smallx": 0.23247937858104706,
       "largex": 2.796694040298462,
       "trainable": true
     }
   ],
   "stop_epoch": 1000,
   "best_epoch": 999,
-  "erf_tr": 5.0698370933532715,
-  "erf_vl": 5.813074588775635,
-  "chi2": 3.6448733806610107,
+  "erf_tr": 5.0740556716918945,
+  "erf_vl": 5.817448139190674,
+  "chi2": 3.645888328552246,
   "pos_state": "POS_VETO",
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+    1.0539481882370751,
+    4.728937647007949,
+    1.063644108706556
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   "integrability": [
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   "timing": {
     "walltime": {
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+      "Total": 36.70934009552002,
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+      "replica_fitted": 36.70907497406006,
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     "cputime": {
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+      "Total": 96.177467606,
       "start": 0.0,
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-      "replica_fitted": 26.207412609,
-      "replica_set_to_replica_fitted": 21.280699479
+      "replica_set": 4.545300222999998,
+      "replica_fitted": 96.17717635,
+      "replica_set_to_replica_fitted": 91.631876127
     }
   },
   "version": {
-    "keras": "2.8.0",
-    "tensorflow": "2.8.2, mkl=False",
+    "keras": "2.11.0",
+    "tensorflow": "2.11.0, mkl=True",
     "numpy": "1.24.3",
-    "nnpdf": "4.0.6.742+g00e5c8e08-dev",
-    "validphys": "4.0.6.742+g00e5c8e08-dev"
+    "nnpdf": "4.0.6.743+g88e22f616-dev",
+    "validphys": "4.0.6.743+g88e22f616-dev"
   }
 }
diff --git a/n3fit/src/n3fit/tests/test_evolven3fit.py b/n3fit/src/n3fit/tests/test_evolven3fit.py
@@ -1,7 +1,12 @@
-import pathlib
 import logging
+import pathlib
+import pytest
+import shutil
+import subprocess as sp
 from numpy.testing import assert_allclose
 import numpy as np
+from reportengine.compat import yaml
+from validphys.api import API
 from validphys.pdfbases import PIDS_DICT
 from evolven3fit_new import utils, eko_utils
 from eko import EKO, runner
@@ -15,6 +20,23 @@ def check_consecutive_members(grid, value):
     return np.allclose(grid[list(grid).index(value) + 1], value)
 
 
+def check_lhapdf_info(info_path):
+    """Check the LHAPDF info file is correct"""
+    info = yaml.load(info_path.open("r", encoding="utf-8"))
+
+    alphas_qs = info["AlphaS_Qs"]
+    alphas = info["AlphaS_Vals"]
+
+    np.testing.assert_equal(info["QMin"], alphas_qs[0])
+    np.testing.assert_equal(len(alphas), len(alphas_qs))
+    assert isinstance(info["Particle"], int)
+    np.testing.assert_allclose(info["AlphaS_OrderQCD"], 2)
+    assert info["ErrorType"] == "replicas"
+
+    for flavor in info["Flavors"]:
+        assert isinstance(flavor, int)
+
+
 def test_utils():
     # Testing the default grid
     grid = utils.generate_q2grid(1.65, None, None, {})
@@ -80,3 +102,25 @@ def test_eko_utils(tmp_path):
     eko_op = EKO.read(save_path)
     assert_allclose(eko_op.operator_card.raw["xgrid"], x_grid)
     assert_allclose(list(eko_op.operator_card.raw["mugrid"]), op_card_dict["mugrid"])
+
+
+@pytest.mark.parametrize("fitname", ["Basic_runcard_3replicas_lowprec_399"])
+def test_perform_evolution(tmp_path, fitname):
+    """Test that evolven3fit_new is able to utilize the current eko in the respective theory.
+    In addition checks that the generated .info files are correct
+    """
+    fit = API.fit(fit=fitname)
+    # Move the fit to a temporary folder
+    tmp_fit = tmp_path / fitname
+    shutil.copytree(fit.path, tmp_fit)
+    # Clear the .log and .dat files
+    (tmp_fit / "evolven3fit_new.log").unlink()
+    tmp_nnfit = tmp_fit / "nnfit"
+    tmp_info = tmp_nnfit/f"{fitname}.info"
+    tmp_info.unlink()
+    for datpath in tmp_nnfit.glob("replica_*/*.dat"):
+        datpath.unlink()
+    # And re-evolve the fit
+    sp.run(["evolven3fit_new", "evolve", fitname], cwd=tmp_path, check=True)
+    # check that everything worked!
+    check_lhapdf_info(tmp_info)