remove use_theorycovmat also from non-python files

validphys2/examples/API_extension_Pineappl.ipynb

@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "62ef3d68-89ed-4014-8719-4de3fbc1ecc6",
    "metadata": {},
    "outputs": [],
@@ -83,7 +83,7 @@
     "            pdf = pdf.load()\n",
     "        ret = []\n",
     "        bin_norm = self._grid.bin_normalizations().reshape(-1, 1)\n",
-    "        \n",
+    "\n",
     "        for i, member in enumerate(pdf.members):\n",
     "            tmp = self._grid.convolve_with_one(2212, member.xfxQ2, member.alphasQ2, xi=all_scales).reshape(-1, len(all_scales))\n",
     "\n",
@@ -92,7 +92,7 @@
     "\n",
     "            # Apply shifts (if any) usually 0:\n",
     "            tmp = np.concatenate([np.zeros((self._shift, len(all_scales))), tmp])\n",
-    "            \n",
+    "\n",
     "            ret.append(tmp)\n",
     "\n",
     "        return np.array(ret)*self._factor\n",
@@ -111,7 +111,7 @@
     "        self._pine_objects = pine_objects\n",
     "\n",
     "    @functools.lru_cache\n",
-    "    def predictions(self, pdf):        \n",
+    "    def predictions(self, pdf):\n",
     "        operators = []\n",
     "        for pine_operator in self._pine_objects:\n",
     "            tmp = []\n",
@@ -124,7 +124,7 @@
     "\n",
     "        # Loop over scales to get the result for all members for every scale\n",
     "        return self._operation(*operators) # (nmembers, ndata, nscales)\n",
-    "        \n",
+    "\n",
     "    def __str__(self):\n",
     "        return f\"PineContainer({self._name})\"\n",
     "\n",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "09b0bc28-e540-414f-a789-ed8fb5bac5a6",
    "metadata": {},
    "outputs": [],
@@ -161,14 +161,14 @@
     "\n",
     "            if theory_meta.shifts is not None:\n",
     "                shift = theory_meta.shifts.get(i, 0)\n",
-    "            \n",
+    "\n",
     "            pine_path = grid_path / f\"{i}.{EXT}\"\n",
     "            tmp.append(PineObject(pine_path, factor, shift=shift))\n",
     "        pinegrids.append(tmp)\n",
     "\n",
     "    operation = OP[theory_meta.operation]\n",
     "    return PineContainer(pinegrids, dsname=dataset.name,  operation=operation)\n",
-    "    \n",
+    "\n",
     "def _pine_predictions(dataset, pdf, central_only=False):\n",
     "    \"\"\"Given a dataset and a PDF, produces predictions with pineappl\n",
     "    The output shape is a list of DataFrames with the right shape for ThPredictions\"\"\"\n",
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "0d88977d-5763-4929-8766-3919c8f70817",
    "metadata": {
     "scrolled": true
@@ -255,7 +255,7 @@
     "pdf_name = \"NNPDF40_nnlo_as_01180\"\n",
     "dname = \"LHCB_WPWM_8TEV_MUON_Y\"\n",
     "kwargs = {\"dataset_input\": {\"dataset\": dname}, \"theoryid\" : tid, \"use_cuts\":\"internal\", \"pdf\": pdf_name}\n",
-    "theory_opt = {\"point_prescription\": \"3 point\", \"theoryids\": {\"from_\": \"scale_variation_theories\"}, \"use_theorycovmat\": True}\n",
+    "theory_opt = {\"point_prescription\": \"3 point\", \"theoryids\": {\"from_\": \"scale_variation_theories\"}}\n",
     "\n",
     "base_chi2 = API.abs_chi2_data(**kwargs)\n",
     "print(f\"Exp chi2: {base_chi2.central_result / base_chi2.ndata:.4}\")\n",
@@ -320,11 +320,11 @@
    "source": [
     "def create_data_pickle(dname, pdf, theoryid, force=False, output_folder=Path(\".\")):\n",
     "    data_path = Path(output_folder) / f\"results_{dname}_{theoryid}_{pdf}.pkl\"\n",
-    "     \n",
+    "\n",
     "    if data_path.exists() and not force:\n",
     "        print(f\"Skipping {dname} ({pdf}) since {data_path} already exists. If you want to overwrite it use `force=True`\")\n",
     "        return\n",
-    "        \n",
+    "\n",
     "    print(f\"Computing predictions for {dname} with {pdf} (tid={theoryid})\")\n",
     "    generic = {\n",
     "        \"dataset_input\": {\"dataset\": dname},\n",
@@ -342,7 +342,6 @@
     "        **generic,\n",
     "        point_prescription=\"9 point\",\n",
     "        theoryids={\"from_\": \"scale_variation_theories\"},\n",
-    "        use_theorycovmat=True,\n",
     "    )\n",
     "    ret = {\n",
     "        \"exp_data\": res_data.central_value,\n",
@@ -352,7 +351,7 @@
     "        \"theory_uncert\": np.sqrt(np.diag(theory_covmat)),\n",
     "    }\n",
     "    ds = API.dataset(**generic)\n",
-    "    # Read the cuts \n",
+    "    # Read the cuts\n",
     "    cuts = ds.cuts.load()+1 # kinematics start at 1\n",
     "    kin_df = ds.commondata.metadata.load_kinematics().loc[cuts]\n",
     "    results_df = pd.DataFrame(ret, index=kin_df.index)\n",
@@ -379,7 +378,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "nnpdf",
    "language": "python",
    "name": "python3"
   },
@@ -393,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

validphys2/examples/data_theory_comparison_w_sv.yaml

@@ -7,7 +7,6 @@ use_cuts: "internal"
 
 theoryid: 717 # define the central theory
 point_prescription: "3 point"
-use_theorycovmat: true
 
 use_pdferr: true
 

validphys2/src/validphys/results.py

@@ -578,7 +578,7 @@ def results_with_theory_covmat(dataset, results, theory_covmat_dataset):
     data theory covariance in which the experimental uncertainties are not stained by the thcovmat
     """
     # TODO: in principle this function could be removed, and `results` could automagically include
-    # the theory covmat when a key, let's call it `use_theorycovmat: true`, by changing the nodes in
+    # the theory covmat when a certain key (to be introduced) is set to True by changing the nodes in
     # `config.py` however at the moment config.py _loads_ theory covmats and we need to compute it
     # on the fly
     from .covmats import sqrt_covmat