Test python installation with dedicated workflow

.github/workflows/python_installation.yml

@@ -0,0 +1,44 @@
+name: Test the python installation
+
+on: [push]
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.10", "3.11"]
+        include:
+          - os: ubuntu-latest
+            CONDA_OS: linux-64
+      fail-fast: false
+    runs-on: ${{ matrix.os }}
+    env:
+      NETRC_FILE: ${{ secrets.NETRC_FILE }}
+      NNPDF_SSH_KEY: ${{ secrets.NNPDF_SSH_KEY }}
+    steps:
+    - uses: actions/checkout@v4
+    - uses: conda-incubator/setup-miniconda@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Setup conda and install LHAPDF and pandoc
+      shell: bash -l {0}
+      run: |
+        echo "$NETRC_FILE" | base64 --decode > ~/.netrc
+        conda install -n base conda-libmamba-solver
+        conda config --set solver libmamba
+        conda config --append channels conda-forge
+        conda config --prepend channels https://packages.nnpdf.science/public
+        conda config --set show_channel_urls true
+        conda install lhapdf pandoc
+        conda activate test
+    - name: Install nnpdf with testing and qed extras
+      shell: bash -l {0}
+      run: |
+        conda activate test
+        pip install .[qed,tests]
+    - name: Test n3fit and validphys
+      shell: bash -l {0}
+      run: |
+        conda activate test
+        pytest --pyargs --mpl validphys n3fit

n3fit/src/n3fit/tests/test_preprocessing.py

@@ -64,4 +64,4 @@ def test_preprocessing():
         ]
     ]
     prefactors = prepro(test_x)
-    np.testing.assert_allclose(test_prefactors, prefactors)
+    np.testing.assert_allclose(test_prefactors, prefactors, rtol=1e-6)

validphys2/src/validphys/tests/test_plots.py

@@ -1,58 +1,67 @@
 import matplotlib
-#This is to fix a weird bug in LHAPDF
+
+# This is to fix a weird bug in LHAPDF
 matplotlib.use('agg')
 
 import pytest
 
 from validphys.api import API
 from validphys.tests.conftest import PDF, THEORYID, DATA
 
+TOLERANCE_VALUE = 12
+
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plotpdfs():
     pdfs = [PDF]
     Q = 10
     flavours = ['g']
-    #plot_pdfs returns a generator with (figure, name_hint)
+    # plot_pdfs returns a generator with (figure, name_hint)
     return next(API.plot_pdfs(pdfs=pdfs, Q=Q, flavours=flavours))[0]
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_dataspecschi2():
     dsinpts = [
         {'dataset': 'NMC'},
-        {'dataset': 'ATLASTTBARTOT', 'cfac':['QCD']},
-        {'dataset': 'CMSZDIFF12', 'cfac':('QCD', 'NRM'), 'sys':10}
+        {'dataset': 'ATLASTTBARTOT', 'cfac': ['QCD']},
+        {'dataset': 'CMSZDIFF12', 'cfac': ('QCD', 'NRM'), 'sys': 10},
     ]
     dataspecs = [
         {'pdf': PDF, 'theoryid': THEORYID, 'speclabel': 'no t0'},
-        {'pdf': PDF, 'theoryid': THEORYID, 'use_t0': False, 'speclabel': 'with t0'}
+        {'pdf': PDF, 'theoryid': THEORYID, 'use_t0': False, 'speclabel': 'with t0'},
     ]
     return API.plot_dataspecs_datasets_chi2(
         dataset_inputs=dsinpts,
         dataspecs=dataspecs,
         use_cuts='internal',
-        metadata_group='experiment'
+        metadata_group='experiment',
     )
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plot_smpdf(single_data_internal_cuts_config):
     return next(API.plot_smpdf(**single_data_internal_cuts_config))
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plot_smpdf_categorical(single_data_categorical_internal_cuts_config):
     return next(API.plot_smpdf(**single_data_categorical_internal_cuts_config))
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plot_obscorrs(single_data_internal_cuts_config):
     corrpair = [{"corrpair": (i["dataset"],)} for i in DATA[:2]]
     return API.plot_obscorrs(**single_data_internal_cuts_config, corrpair=corrpair)
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plot_xq2():
     theoryid = THEORYID
     use_cuts = "nocuts"
@@ -74,8 +83,9 @@ def test_plot_xq2():
         metadata_group=metadata_group,
     )
 
+
 @pytest.mark.linux
-@pytest.mark.mpl_image_compare
+@pytest.mark.mpl_image_compare(tolerance=TOLERANCE_VALUE)
 def test_plot_xq2_custom():
     theoryid = THEORYID
     use_cuts = "nocuts"