covmat-by-reference-bug

.github/actions/install_conda_pip/action.yml

@@ -39,7 +39,7 @@ runs:
     - name: Install host requirements
       shell: bash -l {0}
       run: |
-        conda install lhapdf pandoc
+        conda install lhapdf pandoc pip
     - name: Install package with test extras
       shell: bash -l {0}
       run: |

.github/actions/prepare_environment/action.yml

@@ -28,7 +28,7 @@ inputs:
 runs:
   using: "composite"
   steps:
-    - uses: conda-incubator/setup-miniconda@v3
+    - uses: conda-incubator/setup-miniconda@v4
       with:
         python-version: ${{ inputs.python-version }}
         use-mamba: true

.github/workflows/all_tests_nnpdf.yml

@@ -78,7 +78,7 @@ jobs:
       - name: Build recipe
         shell: bash -l {0}
         run: |
-          conda install conda-build --yes
+          conda install -n base conda-build --yes
           conda build -q conda-recipe --package-format=1
       - name: Keep conda package as artifact
         if: ${{ !cancelled() }}

nnpdf_data/nnpdf_data/filter_utils/utils.py

@@ -438,6 +438,7 @@ def check_xq2_degenearcy(Q2, x):
             unique kinematics are: {unique_pairs.shape[1]}, original size: {size}"""
         ) from exc
 
+
 def uncert_skip_variant(source_file, skip_file, uncert_file, uncert_name, remove_source=True):
     r"""
     Create two new uncertainty files, one where the specified uncertainty
@@ -469,7 +470,9 @@ def uncert_skip_variant(source_file, skip_file, uncert_file, uncert_name, remove
     content_uncert = {}
 
     if 'definitions' in content and uncert_name in content['definitions']:
-        content_uncert['definitions'] = {uncert_name: copy.deepcopy(content['definitions'][uncert_name])}
+        content_uncert['definitions'] = {
+            uncert_name: copy.deepcopy(content['definitions'][uncert_name])
+        }
         content_uncert['bins'] = {}
         bins = []
         for i in range(len(content['bins'])):

validphys2/src/validphys/covmats.py

@@ -413,9 +413,7 @@ def dataset_inputs_t0_total_covmat_separate(
     In this case the t0 prescription is used for the experimental covmat and the multiplicative
     errors are separated. Moreover, the theory covmat is added to experimental covmat.
     """
-    covmat = dataset_inputs_t0_exp_covmat_separate
-    covmat += loaded_theory_covmat
-    return covmat
+    return dataset_inputs_t0_exp_covmat_separate + loaded_theory_covmat
 
 
 def dataset_inputs_t0_exp_covmat_separate(
@@ -448,9 +446,7 @@ def dataset_inputs_total_covmat_separate(dataset_inputs_exp_covmat_separate, loa
     In this case the t0 prescription is not used for the experimental covmat and the multiplicative
     errors are separated. Moreover, the theory covmat is added to experimental covmat.
     """
-    covmat = dataset_inputs_exp_covmat_separate
-    covmat += loaded_theory_covmat
-    return covmat
+    return dataset_inputs_exp_covmat_separate + loaded_theory_covmat
 
 
 def dataset_inputs_exp_covmat_separate(
@@ -482,9 +478,7 @@ def dataset_inputs_t0_total_covmat(dataset_inputs_t0_exp_covmat, loaded_theory_c
     by fitting_data_dict. In this case the t0 prescription is used for the experimental covmat
     and the multiplicative errors are included in it. Moreover, the theory covmat is added to experimental covmat.
     """
-    covmat = dataset_inputs_t0_exp_covmat
-    covmat += loaded_theory_covmat
-    return covmat
+    return dataset_inputs_t0_exp_covmat + loaded_theory_covmat
 
 
 def dataset_inputs_t0_exp_covmat(
@@ -517,9 +511,7 @@ def dataset_inputs_total_covmat(dataset_inputs_exp_covmat, loaded_theory_covmat)
     by fitting_data_dict. In this case the t0 prescription is not used for the experimental covmat
     and the multiplicative errors are included in it. Moreover, the theory covmat is added to experimental covmat.
     """
-    covmat = dataset_inputs_exp_covmat
-    covmat += loaded_theory_covmat
-    return covmat
+    return dataset_inputs_exp_covmat + loaded_theory_covmat
 
 
 def dataset_inputs_exp_covmat(

validphys2/src/validphys/tests/test_covmats.py

@@ -9,7 +9,15 @@
 
 from nnpdf_data.commondataparser import load_commondata
 from validphys.api import API
-from validphys.covmats import dataset_t0_predictions, reorder_thcovmat_as_expcovmat, sqrt_covmat
+from validphys.covmats import (
+    dataset_inputs_t0_total_covmat,
+    dataset_inputs_t0_total_covmat_separate,
+    dataset_inputs_total_covmat,
+    dataset_inputs_total_covmat_separate,
+    dataset_t0_predictions,
+    reorder_thcovmat_as_expcovmat,
+    sqrt_covmat,
+)
 from validphys.tests.conftest import DATA, HESSIAN_PDF, PDF, THEORYID
 
 # Experiments which have non trivial correlations between their datasets
@@ -153,3 +161,24 @@ def test_single_datapoint(single_data_single_point_internal_cuts_config):
     # Ensure the dataset is only a single datapoint
     assert ld.ndata == 1
     ld.systematic_errors(t0_predictions)
+
+
+@pytest.mark.parametrize(
+    "combine_fn",
+    [
+        dataset_inputs_t0_total_covmat_separate,
+        dataset_inputs_total_covmat_separate,
+        dataset_inputs_t0_total_covmat,
+        dataset_inputs_total_covmat,
+    ],
+)
+def test_covmat_summing_helpers_do_not_mutate_inputs(combine_fn):
+    exp = np.array([[1.0, 0.2], [0.2, 2.0]])
+    th = np.array([[0.5, 0.1], [0.1, 0.3]])
+    exp_before = exp.copy()
+
+    result = combine_fn(exp, th)
+
+    np.testing.assert_allclose(exp, exp_before)
+    np.testing.assert_allclose(result, exp_before + th)
+    assert result is not exp