From 9847a892cb287e74884859a68633023ab465132f Mon Sep 17 00:00:00 2001 From: Alexander Puck Neuwirth Date: Wed, 9 Oct 2024 15:14:26 +0200 Subject: [PATCH] corrct Y1 order --- .github/workflows/test-yoda.yml | 22 +- .github/workflows/test.yml | 0 debug/histo1d.ipynb | 174 - debug/histo2d.ipynb | 9815 +++++++++++++++--------------- src/babyyoda/Histo2D_v2.py | 4 +- src/babyyoda/grogu/histo2d_v2.py | 2 +- 6 files changed, 4947 insertions(+), 5070 deletions(-) delete mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test-yoda.yml b/.github/workflows/test-yoda.yml index e3b694d..b006a96 100644 --- a/.github/workflows/test-yoda.yml +++ b/.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 }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index e69de29..0000000 diff --git a/debug/histo1d.ipynb b/debug/histo1d.ipynb index 0dcb897..0672685 100644 --- a/debug/histo1d.ipynb +++ b/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([('/', )])" - ] - }, - "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": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g.plot()" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/debug/histo2d.ipynb b/debug/histo2d.ipynb index c975e83..c217b0c 100644 --- a/debug/histo2d.ipynb +++ b/debug/histo2d.ipynb @@ -21,12 +21,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Warning: is not allowed to get flow bins, flow bin option set to None\n" + "Warning: is not allowed to get flow bins, flow bin option set to None\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -51,12 +51,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Warning: is not allowed to get flow bins, flow bin option set to None\n" + "Warning: is not allowed to get flow bins, flow bin option set to None\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -67,7 +67,7 @@ ], "source": [ "hists[\"/ALICE_2020_I1797621/GGPdfXQ\"].plot()\n", - "plot.yscale(\"log\")\n", + "# plot.yscale(\"log\")\n", "plot.xscale(\"log\")" ] }, @@ -83,5037 +83,5088 @@ "text": [ "2500\n", "1e-06 1.318257e-06 0.0 0.6\n", - "1.318257e-06 1.737801e-06 0.0 0.6\n", - "1.737801e-06 2.290868e-06 0.0 0.6\n", - "2.290868e-06 3.019952e-06 0.0 0.6\n", - "3.019952e-06 3.981072e-06 0.0 0.6\n", - "3.981072e-06 5.248075e-06 0.0 0.6\n", - "5.248075e-06 6.91831e-06 0.0 0.6\n", - "6.91831e-06 9.120108e-06 0.0 0.6\n", - "9.120108e-06 1.202264e-05 0.0 0.6\n", - "1.202264e-05 1.584893e-05 0.0 0.6\n", - "1.584893e-05 2.089296e-05 0.0 0.6\n", - "2.089296e-05 2.754229e-05 0.0 0.6\n", - "2.754229e-05 3.630781e-05 0.0 0.6\n", - "3.630781e-05 4.786301e-05 0.0 0.6\n", - "4.786301e-05 6.309573e-05 0.0 0.6\n", - "6.309573e-05 8.317638e-05 0.0 0.6\n", - "8.317638e-05 0.0001096478 0.0 0.6\n", - "0.0001096478 0.000144544 0.0 0.6\n", - "0.000144544 0.0001905461 0.0 0.6\n", - "0.0001905461 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16.8\n", + "2.290868e-06 3.019952e-06 16.8 17.4\n", + "2.290868e-06 3.019952e-06 17.4 18.0\n", + "2.290868e-06 3.019952e-06 18.0 18.6\n", + "2.290868e-06 3.019952e-06 18.6 19.2\n", + "2.290868e-06 3.019952e-06 19.2 19.8\n", + "2.290868e-06 3.019952e-06 19.8 20.4\n", + "2.290868e-06 3.019952e-06 20.4 21.0\n", + "2.290868e-06 3.019952e-06 21.0 21.6\n", + "2.290868e-06 3.019952e-06 21.6 22.2\n", + "2.290868e-06 3.019952e-06 22.2 22.8\n", + "2.290868e-06 3.019952e-06 22.8 23.4\n", + "2.290868e-06 3.019952e-06 23.4 24.0\n", + "2.290868e-06 3.019952e-06 24.0 24.6\n", + "2.290868e-06 3.019952e-06 24.6 25.2\n", + "2.290868e-06 3.019952e-06 25.2 25.8\n", + "2.290868e-06 3.019952e-06 25.8 26.4\n", + "2.290868e-06 3.019952e-06 26.4 27.0\n", + "2.290868e-06 3.019952e-06 27.0 27.6\n", + "2.290868e-06 3.019952e-06 27.6 28.2\n", + "2.290868e-06 3.019952e-06 28.2 28.8\n", + "2.290868e-06 3.019952e-06 28.8 29.4\n", "2.290868e-06 3.019952e-06 29.4 30.0\n", - "3.019952e-06 3.981072e-06 29.4 30.0\n", - 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30.0\n", - "0.003019952 0.003981072 29.4 30.0\n", - "0.003981072 0.005248075 29.4 30.0\n", - "0.005248075 0.00691831 29.4 30.0\n", - "0.00691831 0.009120108 29.4 30.0\n", - "0.009120108 0.01202264 29.4 30.0\n", - "0.01202264 0.01584893 29.4 30.0\n", - "0.01584893 0.02089296 29.4 30.0\n", - "0.02089296 0.02754229 29.4 30.0\n", - "0.02754229 0.03630781 29.4 30.0\n", - "0.03630781 0.04786301 29.4 30.0\n", - "0.04786301 0.06309573 29.4 30.0\n", - "0.06309573 0.08317638 29.4 30.0\n", - "0.08317638 0.1096478 29.4 30.0\n", - "0.1096478 0.144544 29.4 30.0\n", - "0.144544 0.1905461 29.4 30.0\n", - "0.1905461 0.2511886 29.4 30.0\n", - "0.2511886 0.3311311 29.4 30.0\n", - "0.3311311 0.4365158 29.4 30.0\n", - "0.4365158 0.5754399 29.4 30.0\n", - "0.5754399 0.7585776 29.4 30.0\n", - "0.7585776 1.0 29.4 30.0\n" - ] - } - ], - "source": [ - "hists = grogu.read_grogu(\"../tests/test_histo2d_v2.yoda\")\n", - "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", - "h.bins()[0]\n", - "print(len(h.bins()))\n", - "for b in h.bins():\n", - " print(b.xMin(), b.xMax(), b.yMin(), b.yMax())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f185a905-d513-4fdb-b604-81839d6ea884", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: is not allowed to get flow bins, flow bin option set to None\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", - "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", - "h" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a0bf61da-d35c-4e98-9c10-55ab4ffd72c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: is not allowed to get flow bins, flow bin option set to None\n" - ] - }, - { - "data": { - "image/png": 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", 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2.4 3.0\n", + "0.5754399 0.7585776 3.0 3.6\n", + "0.5754399 0.7585776 3.6 4.2\n", + "0.5754399 0.7585776 4.2 4.8\n", + "0.5754399 0.7585776 4.8 5.4\n", + "0.5754399 0.7585776 5.4 6.0\n", + "0.5754399 0.7585776 6.0 6.6\n", + "0.5754399 0.7585776 6.6 7.2\n", + "0.5754399 0.7585776 7.2 7.8\n", + "0.5754399 0.7585776 7.8 8.4\n", + "0.5754399 0.7585776 8.4 9.0\n", + "0.5754399 0.7585776 9.0 9.6\n", + "0.5754399 0.7585776 9.6 10.2\n", + "0.5754399 0.7585776 10.2 10.8\n", + "0.5754399 0.7585776 10.8 11.4\n", + "0.5754399 0.7585776 11.4 12.0\n", + "0.5754399 0.7585776 12.0 12.6\n", + "0.5754399 0.7585776 12.6 13.2\n", + "0.5754399 0.7585776 13.2 13.8\n", + "0.5754399 0.7585776 13.8 14.4\n", + "0.5754399 0.7585776 14.4 15.0\n", + "0.5754399 0.7585776 15.0 15.6\n", + "0.5754399 0.7585776 15.6 16.2\n", + "0.5754399 0.7585776 16.2 16.8\n", + "0.5754399 0.7585776 16.8 17.4\n", + "0.5754399 0.7585776 17.4 18.0\n", + "0.5754399 0.7585776 18.0 18.6\n", + "0.5754399 0.7585776 18.6 19.2\n", + "0.5754399 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"0.7585776 1.0 7.2 7.8\n", + "0.7585776 1.0 7.8 8.4\n", + "0.7585776 1.0 8.4 9.0\n", + "0.7585776 1.0 9.0 9.6\n", + "0.7585776 1.0 9.6 10.2\n", + "0.7585776 1.0 10.2 10.8\n", + "0.7585776 1.0 10.8 11.4\n", + "0.7585776 1.0 11.4 12.0\n", + "0.7585776 1.0 12.0 12.6\n", + "0.7585776 1.0 12.6 13.2\n", + "0.7585776 1.0 13.2 13.8\n", + "0.7585776 1.0 13.8 14.4\n", + "0.7585776 1.0 14.4 15.0\n", + "0.7585776 1.0 15.0 15.6\n", + "0.7585776 1.0 15.6 16.2\n", + "0.7585776 1.0 16.2 16.8\n", + "0.7585776 1.0 16.8 17.4\n", + "0.7585776 1.0 17.4 18.0\n", + "0.7585776 1.0 18.0 18.6\n", + "0.7585776 1.0 18.6 19.2\n", + "0.7585776 1.0 19.2 19.8\n", + "0.7585776 1.0 19.8 20.4\n", + "0.7585776 1.0 20.4 21.0\n", + "0.7585776 1.0 21.0 21.6\n", + "0.7585776 1.0 21.6 22.2\n", + "0.7585776 1.0 22.2 22.8\n", + "0.7585776 1.0 22.8 23.4\n", + "0.7585776 1.0 23.4 24.0\n", + "0.7585776 1.0 24.0 24.6\n", + "0.7585776 1.0 24.6 25.2\n", + "0.7585776 1.0 25.2 25.8\n", + "0.7585776 1.0 25.8 26.4\n", + "0.7585776 1.0 26.4 27.0\n", "0.7585776 1.0 27.0 27.6\n", + "0.7585776 1.0 27.6 28.2\n", + "0.7585776 1.0 28.2 28.8\n", + "0.7585776 1.0 28.8 29.4\n", + "0.7585776 1.0 29.4 30.0\n" + ] + } + ], + "source": [ + "hists = grogu.read_grogu(\"../tests/test_histo2d_v2.yoda\")\n", + "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", + "h.bins()[0]\n", + "print(len(h.bins()))\n", + "for b in h.bins():\n", + " print(b.xMin(), b.xMax(), b.yMin(), b.yMax())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f185a905-d513-4fdb-b604-81839d6ea884", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: is not allowed to get flow bins, flow bin option set to None\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", + "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a0bf61da-d35c-4e98-9c10-55ab4ffd72c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: is not allowed to get flow bins, flow bin option set to None\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", + "hists[\"/ALICE_2020_I1797621/GGPdfXQ\"].plot()\n", + "# plot.yscale(\"log\")\n", + "plot.xscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "49cb50c7-8fe1-4840-8b2c-97e0e0f188b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2500\n", + "1e-06 1.318257e-06 0.0 0.6\n", + "1e-06 1.318257e-06 0.6 1.2\n", + "1e-06 1.318257e-06 1.2 1.8\n", + "1e-06 1.318257e-06 1.8 2.4\n", + "1e-06 1.318257e-06 2.4 3.0\n", + "1e-06 1.318257e-06 3.0 3.6\n", + "1e-06 1.318257e-06 3.6 4.2\n", + "1e-06 1.318257e-06 4.2 4.8\n", + "1e-06 1.318257e-06 4.8 5.4\n", + "1e-06 1.318257e-06 5.4 6.0\n", + "1e-06 1.318257e-06 6.0 6.6\n", + "1e-06 1.318257e-06 6.6 7.2\n", + "1e-06 1.318257e-06 7.2 7.8\n", + "1e-06 1.318257e-06 7.8 8.4\n", + "1e-06 1.318257e-06 8.4 9.0\n", + "1e-06 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13.8 14.4\n", + "0.7585776 1.0 14.4 15.0\n", + "0.7585776 1.0 15.0 15.6\n", + "0.7585776 1.0 15.6 16.2\n", + "0.7585776 1.0 16.2 16.8\n", + "0.7585776 1.0 16.8 17.4\n", + "0.7585776 1.0 17.4 18.0\n", + "0.7585776 1.0 18.0 18.6\n", + "0.7585776 1.0 18.6 19.2\n", + "0.7585776 1.0 19.2 19.8\n", + "0.7585776 1.0 19.8 20.4\n", + "0.7585776 1.0 20.4 21.0\n", + "0.7585776 1.0 21.0 21.6\n", + "0.7585776 1.0 21.6 22.2\n", + "0.7585776 1.0 22.2 22.8\n", + "0.7585776 1.0 22.8 23.4\n", + "0.7585776 1.0 23.4 24.0\n", + "0.7585776 1.0 24.0 24.6\n", + "0.7585776 1.0 24.6 25.2\n", + "0.7585776 1.0 25.2 25.8\n", + "0.7585776 1.0 25.8 26.4\n", + "0.7585776 1.0 26.4 27.0\n", + "0.7585776 1.0 27.0 27.6\n", + "0.7585776 1.0 27.6 28.2\n", + "0.7585776 1.0 28.2 28.8\n", + "0.7585776 1.0 28.8 29.4\n", "0.7585776 1.0 29.4 30.0\n" ] } diff --git a/src/babyyoda/Histo2D_v2.py b/src/babyyoda/Histo2D_v2.py index 202ee10..83b0ffb 100644 --- a/src/babyyoda/Histo2D_v2.py +++ b/src/babyyoda/Histo2D_v2.py @@ -48,11 +48,11 @@ def __call__(self, *args, **kwargs): def overflow(self): # This is a YODA-1 feature that is not present in YODA-2 - return self.bins()[-1] + return self.bins(includeOverflows=True)[-1] def underflow(self): # This is a YODA-1 feature that is not present in YODA-2 - return self.bins()[0] + return self.bins(includeOverflows=True)[0] def xMins(self): return np.array(sorted(list(set([b.xMin() for b in self.bins()])))) diff --git a/src/babyyoda/grogu/histo2d_v2.py b/src/babyyoda/grogu/histo2d_v2.py index ccb3734..957bea1 100644 --- a/src/babyyoda/grogu/histo2d_v2.py +++ b/src/babyyoda/grogu/histo2d_v2.py @@ -108,7 +108,7 @@ def bins(self): # YODA-1 # return sorted(self.d_bins, key=lambda b: (b.d_xmin, b.d_ymin)) # YODA-2 - return sorted(self.d_bins, key=lambda b: (b.d_ymin, b.d_xmin)) + return sorted(self.d_bins, key=lambda b: (b.d_xmin, b.d_ymin)) def bin(self, index): return self.bins()[index]