corrct Y1 order

.github/workflows/test-yoda.yml

@@ -42,14 +42,14 @@ jobs:
       run: |
         pytest -r sx tests/
 
-    - name: Report core project coverage with Codecov
-      if: >-
-        github.event_name != 'schedule' &&
-        matrix.os == 'ubuntu-latest'
-      uses: codecov/codecov-action@v4
-      with:
-        fail_ci_if_error: true
-        files: ./coverage.xml
-        flags: unittests-${{ matrix.python-version }}
-        name: pylhe
-        token: ${{ secrets.CODECOV_TOKEN }}
+#    - name: Report core project coverage with Codecov
+#      if: >-
+#        github.event_name != 'schedule' &&
+#        matrix.os == 'ubuntu-latest'
+#      uses: codecov/codecov-action@v4
+#      with:
+#        fail_ci_if_error: true
+#        files: ./coverage.xml
+#        flags: unittests-${{ matrix.python-version }}
+#        name: pylhe
+#        token: ${{ secrets.CODECOV_TOKEN }}

debug/histo1d.ipynb

@@ -83,180 +83,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "079e91f6-0542-4962-a6e2-9070db4c69b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import yoda as yd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f758b849-2388-4dfd-95c5-d888962cc605",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "class GH1D:\n",
-    "    def __init__(self, target):\n",
-    "        # Store the target object where calls and attributes will be forwarded\n",
-    "        super().__setattr__(\"target\", target)\n",
-    "\n",
-    "    @property\n",
-    "    def axes(self):\n",
-    "        return [list(zip(self.xMins(), self.xMaxs()))]\n",
-    "\n",
-    "    @property\n",
-    "    def kind(self):\n",
-    "        return \"COUNT\"\n",
-    "\n",
-    "    def values(self):\n",
-    "        return self.sumWs()\n",
-    "\n",
-    "    def variances(self):\n",
-    "        return np.array([b.sumW2() for b in self.bins()])\n",
-    "\n",
-    "    def counts(self):\n",
-    "        return np.array([b.effNumEntries() for b in self.bins()])\n",
-    "\n",
-    "    def xMins(self):\n",
-    "        return np.array([b.xMin() for b in self.bins()])\n",
-    "\n",
-    "    def xMaxs(self):\n",
-    "        return np.array([b.xMax() for b in self.bins()])\n",
-    "\n",
-    "    def sumWs(self):\n",
-    "        return np.array([b.sumW() for b in self.bins()])\n",
-    "\n",
-    "    def plot(self, *args, w2method=\"sqrt\", **kwargs):\n",
-    "        import mplhep as hep\n",
-    "\n",
-    "        hep.histplot(self, w2=self.variances(), *args, w2method=w2method, **kwargs)\n",
-    "\n",
-    "    def __getattr__(self, name):\n",
-    "        # First, check if the Forwarder object itself has the attribute\n",
-    "        if name in self.__dict__ or hasattr(type(self), name):\n",
-    "            return object.__getattribute__(self, name)\n",
-    "        # If not, forward attribute access to the target\n",
-    "        elif hasattr(self.target, name):\n",
-    "            return getattr(self.target, name)\n",
-    "        raise AttributeError(\n",
-    "            f\"'{type(self).__name__}' object and target have no attribute '{name}'\"\n",
-    "        )\n",
-    "\n",
-    "    def __setattr__(self, name, value):\n",
-    "        # First, check if the attribute belongs to the Forwarder itself\n",
-    "        if name in self.__dict__ or hasattr(type(self), name):\n",
-    "            object.__setattr__(self, name, value)\n",
-    "        # If not, forward attribute setting to the target\n",
-    "        elif hasattr(self.target, name):\n",
-    "            setattr(self.target, name, value)\n",
-    "        else:\n",
-    "            raise AttributeError(\n",
-    "                f\"Cannot set attribute '{name}'; it does not exist in target or Forwarder.\"\n",
-    "            )\n",
-    "\n",
-    "    def __call__(self, *args, **kwargs):\n",
-    "        # If the target is callable, forward the call, otherwise raise an error\n",
-    "        if callable(self.target):\n",
-    "            return self.target(*args, **kwargs)\n",
-    "        raise TypeError(f\"'{type(self.target).__name__}' object is not callable\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c830b5c7-9d5f-45a3-b986-12fb928b46c6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "71587df4-b96f-48ca-8580-ed37263b7797",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hists = yd.read(\"../tests/test_histo1d_v2.yoda\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "182fd8b1-bd18-4ffe-ad39-f6d9ae377fc3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('/', <BinnedHisto1D[d] '/' 10 bins>)])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2d162074-fe43-411a-8e27-7876d01ee1b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = hists[\"/\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd95041c-5e80-4fde-86da-14f3fedab072",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5ceb1109-e2d8-4972-b0fe-80002ee91db8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = GH1D(hists[\"/\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "72ffd353-125b-4a11-8b7e-146c93413b1c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g.plot()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,