From 53a56a84897a727a67d7c4e2fbff9137de5f6118 Mon Sep 17 00:00:00 2001 From: Alexander Puck Neuwirth Date: Fri, 11 Oct 2024 23:01:07 +0200 Subject: [PATCH] fixes 2d hist bin transofrm to 2 axis --- .../Untitled-checkpoint.ipynb | 6 + .../histo2d-checkpoint.ipynb | 2847 ++--------------- debug/Untitled.ipynb | 316 ++ debug/histo2d.ipynb | 58 +- debug/test_histo2d_v3.yoda | 2716 ++++++++++++++++ debug/test_histo2s_v3.yoda | 2716 ++++++++++++++++ src/babyyoda/grogu/histo1d_v3.py | 7 +- src/babyyoda/grogu/histo2d_v2.py | 7 +- src/babyyoda/grogu/histo2d_v3.py | 228 ++ src/babyyoda/grogu/read.py | 4 + src/babyyoda/histo1D.py | 3 +- src/babyyoda/histo2D.py | 62 +- src/babyyoda/test.py | 2 +- tests/babyyoda/test_read.py | 15 + tests/grogu/test_gg_read.py | 5 + tests/test_histo2d_v3.yoda | 2716 ++++++++++++++++ tests/yoda/test_yoda_vs_grogu.py | 46 +- tests/yoda/uhi/test_yd_histo2d_access.py | 20 +- 18 files changed, 9211 insertions(+), 2563 deletions(-) create mode 100644 debug/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 debug/Untitled.ipynb create mode 100644 debug/test_histo2d_v3.yoda create mode 100644 debug/test_histo2s_v3.yoda create mode 100644 src/babyyoda/grogu/histo2d_v3.py create mode 100644 tests/test_histo2d_v3.yoda diff --git a/debug/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/debug/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/debug/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/debug/.ipynb_checkpoints/histo2d-checkpoint.ipynb b/debug/.ipynb_checkpoints/histo2d-checkpoint.ipynb index a1a19cb..84b1f80 100644 --- a/debug/.ipynb_checkpoints/histo2d-checkpoint.ipynb +++ b/debug/.ipynb_checkpoints/histo2d-checkpoint.ipynb @@ -8,18 +8,87 @@ "outputs": [], "source": [ "import babyyoda as grogu\n", - "from matplotlib import pyplot as plot" + "from matplotlib import pyplot as plot\n", + "from babyyoda import loc\n", + "import yoda as yd" ] }, { "cell_type": "code", "execution_count": 2, - "id": "675e4143-2a6b-4d9e-81eb-7b672081a9ce", + "id": "62324cf2-abda-44c1-be2c-ab439577f4eb", + "metadata": {}, + "outputs": [], + "source": [ + "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", + "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", + "\n", + "yd.write([h.target],\"test_histo2d_v3.yoda\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6731d1bc-093b-4445-a213-eb3230e72536", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "0.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.binAt(300,4000).sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4ed6e9b2-8864-41be-bafd-15eb8da68982", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "loc 3.0 is not in the range of [(1e-06, 1.318257e-06), (1.318257e-06, 1.737801e-06), (1.737801e-06, 2.290868e-06), (2.290868e-06, 3.019952e-06), (3.019952e-06, 3.981072e-06), (3.981072e-06, 5.248075e-06), (5.248075e-06, 6.91831e-06), (6.91831e-06, 9.120108e-06), (9.120108e-06, 1.202264e-05), (1.202264e-05, 1.584893e-05), (1.584893e-05, 2.089296e-05), (2.089296e-05, 2.754229e-05), (2.754229e-05, 3.630781e-05), (3.630781e-05, 4.786301e-05), (4.786301e-05, 6.309573e-05), (6.309573e-05, 8.317638e-05), (8.317638e-05, 0.0001096478), (0.0001096478, 0.000144544), (0.000144544, 0.0001905461), (0.0001905461, 0.0002511886), (0.0002511886, 0.0003311311), (0.0003311311, 0.0004365158), (0.0004365158, 0.0005754399), (0.0005754399, 0.0007585776), (0.0007585776, 0.001), (0.001, 0.001318257), (0.001318257, 0.001737801), (0.001737801, 0.002290868), (0.002290868, 0.003019952), (0.003019952, 0.003981072), (0.003981072, 0.005248075), (0.005248075, 0.00691831), (0.00691831, 0.009120108), (0.009120108, 0.01202264), (0.01202264, 0.01584893), (0.01584893, 0.02089296), (0.02089296, 0.02754229), (0.02754229, 0.03630781), (0.03630781, 0.04786301), (0.04786301, 0.06309573), (0.06309573, 0.08317638), (0.08317638, 0.1096478), (0.1096478, 0.144544), (0.144544, 0.1905461), (0.1905461, 0.2511886), (0.2511886, 0.3311311), (0.3311311, 0.4365158), (0.4365158, 0.5754399), (0.5754399, 0.7585776), (0.7585776, 1.0)]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mh\u001b[49m\u001b[43m[\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4.\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msumW()\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:161\u001b[0m, in \u001b[0;36mHisto2D.__getitem__\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slices, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(slices) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 161\u001b[0m ix, iy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ix, \u001b[38;5;28mint\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(iy, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_by_indices(ix, iy)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:151\u001b[0m, in \u001b[0;36mHisto2D.__get_indices\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_indices\u001b[39m(\u001b[38;5;28mself\u001b[39m, slices):\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_x_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_y_index(slices[\u001b[38;5;241m1\u001b[39m])\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:139\u001b[0m, in \u001b[0;36mHisto2D.__get_x_index\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 137\u001b[0m ix \u001b[38;5;241m=\u001b[39m slices\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slices, loc):\n\u001b[0;32m--> 139\u001b[0m ix \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_index_by_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ix\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:132\u001b[0m, in \u001b[0;36mHisto2D.__get_index_by_loc\u001b[0;34m(self, loc, bins)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m loc\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mand\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m<\u001b[39m b:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mindex((a, b)) \u001b[38;5;241m+\u001b[39m loc\u001b[38;5;241m.\u001b[39moffset\n\u001b[0;32m--> 132\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloc \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloc\u001b[38;5;241m.\u001b[39mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not in the range of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbins\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: loc 3.0 is not in the range of [(1e-06, 1.318257e-06), (1.318257e-06, 1.737801e-06), (1.737801e-06, 2.290868e-06), (2.290868e-06, 3.019952e-06), (3.019952e-06, 3.981072e-06), (3.981072e-06, 5.248075e-06), (5.248075e-06, 6.91831e-06), (6.91831e-06, 9.120108e-06), (9.120108e-06, 1.202264e-05), (1.202264e-05, 1.584893e-05), (1.584893e-05, 2.089296e-05), (2.089296e-05, 2.754229e-05), (2.754229e-05, 3.630781e-05), (3.630781e-05, 4.786301e-05), (4.786301e-05, 6.309573e-05), (6.309573e-05, 8.317638e-05), (8.317638e-05, 0.0001096478), (0.0001096478, 0.000144544), (0.000144544, 0.0001905461), (0.0001905461, 0.0002511886), (0.0002511886, 0.0003311311), (0.0003311311, 0.0004365158), (0.0004365158, 0.0005754399), (0.0005754399, 0.0007585776), (0.0007585776, 0.001), (0.001, 0.001318257), (0.001318257, 0.001737801), (0.001737801, 0.002290868), (0.002290868, 0.003019952), (0.003019952, 0.003981072), (0.003981072, 0.005248075), (0.005248075, 0.00691831), (0.00691831, 0.009120108), (0.009120108, 0.01202264), (0.01202264, 0.01584893), (0.01584893, 0.02089296), (0.02089296, 0.02754229), (0.02754229, 0.03630781), (0.03630781, 0.04786301), (0.04786301, 0.06309573), (0.06309573, 0.08317638), (0.08317638, 0.1096478), (0.1096478, 0.144544), (0.144544, 0.1905461), (0.1905461, 0.2511886), (0.2511886, 0.3311311), (0.3311311, 0.4365158), (0.4365158, 0.5754399), (0.5754399, 0.7585776), (0.7585776, 1.0)]" + ] + } + ], + "source": [ + "h[loc(3.),loc(4.)].sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a3dc2ab-b718-4fc2-9d34-2e0e9a74071b", + "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": [ "
" ] @@ -29,20 +98,25 @@ } ], "source": [ - "hists = grogu.read_grogu(\"../tests/test_histo2d_v2.yoda\")\n", - "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", "h" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "43507e18-f9d1-4d2e-be93-727e8f3c4c33", + "execution_count": 14, + "id": "3cde4aa1-7bdf-4836-83cc-282edcd1661c", "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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" ] @@ -52,2553 +126,256 @@ } ], "source": [ - "hists[\"/ALICE_2020_I1797621/GGPdfXQ\"].plot()\n", - "# plot.yscale(\"log\")\n", + "h[:,:].plot()\n", "plot.xscale(\"log\")" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "0fb00e2b-9a94-4026-babb-96a6c00bcb9e", + "execution_count": 19, + "id": "78a4bbaf-990f-45d4-b432-cf55173f3782", "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 1.318257e-06 9.0 9.6\n", - "1e-06 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29.4 30.0\n", - "0.3311311 0.4365158 0.0 0.6\n", - "0.3311311 0.4365158 0.6 1.2\n", - "0.3311311 0.4365158 1.2 1.8\n", - "0.3311311 0.4365158 1.8 2.4\n", - "0.3311311 0.4365158 2.4 3.0\n", - "0.3311311 0.4365158 3.0 3.6\n", - "0.3311311 0.4365158 3.6 4.2\n", - "0.3311311 0.4365158 4.2 4.8\n", - "0.3311311 0.4365158 4.8 5.4\n", - "0.3311311 0.4365158 5.4 6.0\n", - "0.3311311 0.4365158 6.0 6.6\n", - "0.3311311 0.4365158 6.6 7.2\n", - "0.3311311 0.4365158 7.2 7.8\n", - "0.3311311 0.4365158 7.8 8.4\n", - "0.3311311 0.4365158 8.4 9.0\n", - "0.3311311 0.4365158 9.0 9.6\n", - "0.3311311 0.4365158 9.6 10.2\n", - "0.3311311 0.4365158 10.2 10.8\n", - "0.3311311 0.4365158 10.8 11.4\n", - "0.3311311 0.4365158 11.4 12.0\n", - "0.3311311 0.4365158 12.0 12.6\n", - "0.3311311 0.4365158 12.6 13.2\n", - "0.3311311 0.4365158 13.2 13.8\n", - "0.3311311 0.4365158 13.8 14.4\n", - "0.3311311 0.4365158 14.4 15.0\n", - "0.3311311 0.4365158 15.0 15.6\n", - "0.3311311 0.4365158 15.6 16.2\n", - "0.3311311 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2.4 3.0\n", - "0.4365158 0.5754399 3.0 3.6\n", - "0.4365158 0.5754399 3.6 4.2\n", - "0.4365158 0.5754399 4.2 4.8\n", - "0.4365158 0.5754399 4.8 5.4\n", - "0.4365158 0.5754399 5.4 6.0\n", - "0.4365158 0.5754399 6.0 6.6\n", - "0.4365158 0.5754399 6.6 7.2\n", - "0.4365158 0.5754399 7.2 7.8\n", - "0.4365158 0.5754399 7.8 8.4\n", - "0.4365158 0.5754399 8.4 9.0\n", - "0.4365158 0.5754399 9.0 9.6\n", - "0.4365158 0.5754399 9.6 10.2\n", - "0.4365158 0.5754399 10.2 10.8\n", - "0.4365158 0.5754399 10.8 11.4\n", - "0.4365158 0.5754399 11.4 12.0\n", - "0.4365158 0.5754399 12.0 12.6\n", - "0.4365158 0.5754399 12.6 13.2\n", - "0.4365158 0.5754399 13.2 13.8\n", - "0.4365158 0.5754399 13.8 14.4\n", - "0.4365158 0.5754399 14.4 15.0\n", - "0.4365158 0.5754399 15.0 15.6\n", - "0.4365158 0.5754399 15.6 16.2\n", - "0.4365158 0.5754399 16.2 16.8\n", - "0.4365158 0.5754399 16.8 17.4\n", - "0.4365158 0.5754399 17.4 18.0\n", - "0.4365158 0.5754399 18.0 18.6\n", - "0.4365158 0.5754399 18.6 19.2\n", - "0.4365158 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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 0.7585776 19.2 19.8\n", - "0.5754399 0.7585776 19.8 20.4\n", - "0.5754399 0.7585776 20.4 21.0\n", - "0.5754399 0.7585776 21.0 21.6\n", - "0.5754399 0.7585776 21.6 22.2\n", - 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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" + "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": [ + "h[:,loc(0):loc(5)].plot()\n", + "plot.xscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "592c2468-513f-47ae-ac2a-9c977a6874dc", + "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": [ + "h[loc(10**-5):loc(10**-2),:].plot()\n", + "plot.xscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c5aedf2a-a987-4f70-aeea-dcd29989de66", + "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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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/usr/lib/python3.12/site-packages/IPython/core/formatters.py:925\u001b[0m, in \u001b[0;36mIPythonDisplayFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 923\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 925\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:230\u001b[0m, in \u001b[0;36mHisto2D._ipython_display_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_ipython_display_\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:225\u001b[0m, in \u001b[0;36mHisto2D.plot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([b\u001b[38;5;241m.\u001b[39msumW() \u001b[38;5;241m/\u001b[39m b\u001b[38;5;241m.\u001b[39mdVol() \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbins()])\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m 221\u001b[0m (\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m0\u001b[39m]), \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m 222\u001b[0m )\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m=\u001b[39m temp_values\n\u001b[0;32m--> 225\u001b[0m \u001b[43mhep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist2dplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m=\u001b[39m saved_values\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/mplhep/plot.py:748\u001b[0m, in \u001b[0;36mhist2dplot\u001b[0;34m(H, xbins, ybins, labels, cbar, cbarsize, cbarpad, cbarpos, cbarextend, cmin, cmax, ax, flow, **kwargs)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ax, plt\u001b[38;5;241m.\u001b[39mAxes):\n\u001b[1;32m 746\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max must be a matplotlib Axes object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 748\u001b[0m h \u001b[38;5;241m=\u001b[39m \u001b[43mhist_object_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mybins\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 750\u001b[0m \u001b[38;5;66;03m# TODO: use Histogram everywhere\u001b[39;00m\n\u001b[1;32m 752\u001b[0m H \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcopy(h\u001b[38;5;241m.\u001b[39mvalues())\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/mplhep/utils.py:59\u001b[0m, in \u001b[0;36mhist_object_handler\u001b[0;34m(hist, *bins)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 57\u001b[0m hist_obj \u001b[38;5;241m=\u001b[39m ensure_plottable_histogram((hist, \u001b[38;5;241m*\u001b[39mbins))\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43mhist_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust have only 1 or 2 axes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hist_obj\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:21\u001b[0m, in \u001b[0;36mHisto2D.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# yoda-1 has overflow but yoda-2 does not so we patch it in here\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), name):\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:97\u001b[0m, in \u001b[0;36mHisto2D.axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21maxes\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxMins\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxMaxs())),\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myMins(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myMaxs())),\n\u001b[1;32m 99\u001b[0m ]\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:69\u001b[0m, in \u001b[0;36mHisto2D.xMins\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mxMins\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxEdges\u001b[49m()[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:24\u001b[0m, in \u001b[0;36mHisto2D.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name)\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 25\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object and target have no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 26\u001b[0m )\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Histo2D' object and target have no attribute 'xEdges'" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "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())" + "h" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "f185a905-d513-4fdb-b604-81839d6ea884", + "execution_count": 9, + "id": "43507e18-f9d1-4d2e-be93-727e8f3c4c33", "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'yoda'", + "ename": "AttributeError", + "evalue": "'Histo2D' object and target have no attribute 'xEdges'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m hists \u001b[38;5;241m=\u001b[39m \u001b[43mgrogu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_yoda\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../tests/test_histo2d_v2.yoda\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m h \u001b[38;5;241m=\u001b[39m hists[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/ALICE_2020_I1797621/GGPdfXQ\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 3\u001b[0m h\n", - "File \u001b[0;32m~/git/babyyoda/src/babyyoda/read.py:22\u001b[0m, in \u001b[0;36mread_yoda\u001b[0;34m(file_path)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_yoda\u001b[39m(file_path: \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 19\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124;03m Wrap yoda histograms in the by HISTO1D_V2 class\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01myoda\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01myd\u001b[39;00m\n\u001b[1;32m 24\u001b[0m ret \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m yd\u001b[38;5;241m.\u001b[39mread(file_path)\u001b[38;5;241m.\u001b[39mitems():\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'yoda'" + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhists\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/ALICE_2020_I1797621/GGPdfXQ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# plot.yscale(\"log\")\u001b[39;00m\n\u001b[1;32m 3\u001b[0m plot\u001b[38;5;241m.\u001b[39mxscale(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:225\u001b[0m, in \u001b[0;36mHisto2D.plot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([b\u001b[38;5;241m.\u001b[39msumW() \u001b[38;5;241m/\u001b[39m b\u001b[38;5;241m.\u001b[39mdVol() \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbins()])\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m 221\u001b[0m (\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m0\u001b[39m]), \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m 222\u001b[0m )\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m=\u001b[39m temp_values\n\u001b[0;32m--> 225\u001b[0m \u001b[43mhep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist2dplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m=\u001b[39m saved_values\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/mplhep/plot.py:748\u001b[0m, in \u001b[0;36mhist2dplot\u001b[0;34m(H, xbins, ybins, labels, cbar, cbarsize, cbarpad, cbarpos, cbarextend, cmin, cmax, ax, flow, **kwargs)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ax, plt\u001b[38;5;241m.\u001b[39mAxes):\n\u001b[1;32m 746\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max must be a matplotlib Axes object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 748\u001b[0m h \u001b[38;5;241m=\u001b[39m \u001b[43mhist_object_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mybins\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 750\u001b[0m \u001b[38;5;66;03m# TODO: use Histogram everywhere\u001b[39;00m\n\u001b[1;32m 752\u001b[0m H \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcopy(h\u001b[38;5;241m.\u001b[39mvalues())\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/mplhep/utils.py:59\u001b[0m, in \u001b[0;36mhist_object_handler\u001b[0;34m(hist, *bins)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 57\u001b[0m hist_obj \u001b[38;5;241m=\u001b[39m ensure_plottable_histogram((hist, \u001b[38;5;241m*\u001b[39mbins))\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43mhist_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust have only 1 or 2 axes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hist_obj\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:21\u001b[0m, in \u001b[0;36mHisto2D.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# yoda-1 has overflow but yoda-2 does not so we patch it in here\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), name):\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:97\u001b[0m, in \u001b[0;36mHisto2D.axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21maxes\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxMins\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxMaxs())),\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myMins(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myMaxs())),\n\u001b[1;32m 99\u001b[0m ]\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:69\u001b[0m, in \u001b[0;36mHisto2D.xMins\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mxMins\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxEdges\u001b[49m()[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo2D.py:24\u001b[0m, in \u001b[0;36mHisto2D.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name)\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 25\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object and target have no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 26\u001b[0m )\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Histo2D' object and target have no attribute 'xEdges'" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], + "source": [ + "hists[\"/ALICE_2020_I1797621/GGPdfXQ\"].plot()\n", + "# plot.yscale(\"log\")\n", + "plot.xscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fb00e2b-9a94-4026-babb-96a6c00bcb9e", + "metadata": {}, + "outputs": [], + "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": null, + "id": "f185a905-d513-4fdb-b604-81839d6ea884", + "metadata": {}, + "outputs": [], "source": [ "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", @@ -2638,6 +415,26 @@ "id": "308df577-7671-4de3-ba5e-20af39bfc3f9", "metadata": {}, "outputs": [], + "source": [ + "h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb5e9e62-99d8-4c27-940e-da98b1877076", + "metadata": {}, + "outputs": [], + "source": [ + "h[:,:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b13455dc-5660-45ca-879f-3ef24ad1f7a1", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/debug/Untitled.ipynb b/debug/Untitled.ipynb new file mode 100644 index 0000000..9902483 --- /dev/null +++ b/debug/Untitled.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "675f1972-4479-4684-9201-9781affafd71", + "metadata": {}, + "outputs": [], + "source": [ + "import babyyoda as by" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d2f9b427-f300-4d81-aa03-c7e2473564bd", + "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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AIGgEkwAAwFgeOYwowVi+fLmSkpIUHh6u1NRUvfnmmy22LykpUWpqqsLDw3X++edr5cqVPt8//fTTcjgcfuXEiRNnNO6ZIpgEAADGanoDjt2lrTZs2KCcnBzNmzdP5eXlGjVqlMaNG6eqqqqA7SsrK3XNNddo1KhRKi8v10MPPaSZM2dq48aNPu0iIyN16NAhnxIeHh70uKFAMAkAABBiS5cu1V133aW7775bycnJKioqUnx8vFasWBGw/cqVK5WQkKCioiIlJyfr7rvv1m9+8xs9+uijPu0cDodiY2N9ypmMGwoEkwAAwFhNB3DsLpJUV1fnU+rr6wPOuaGhQWVlZcrKyvKpz8rK0o4dOwL2KS0t9Ws/ZswY7d27V99995237tixY0pMTNR5552n8ePHq7y8/IzGDQWCSQAAYCyPHN5XKtpW/rVnMj4+Xi6Xy1sKCgoCzvnw4cNqbGxUTEyMT31MTIzcbnfAPm63O2D7kydP6vDhw5KkQYMG6emnn9bLL7+sF154QeHh4Ro5cqQ++OCDoMcNBR4NBAAA0ArV1dWKjIz0fnY6nS22dzh891paluVXd7r2368fMWKERowY4f1+5MiRSklJ0R//+Ec99thjQY97pggmAQCAsawzOE0dyjlIpw6/fD+YbE5UVJTCwsL8soE1NTV+WcMmsbGxAdt37dpVffv2DdinS5cuuuSSS7yZyWDGDQWWuQEAgLFsX+L+V2mL7t27KzU1VcXFxT71xcXFyszMDNgnIyPDr/3WrVuVlpambt26BexjWZYqKirUr1+/oMcNBTKTAADAWGfrG3Byc3OVnZ2ttLQ0ZWRkaNWqVaqqqtK0adMkSXPnztWnn36qZ599VpI0bdo0LVu2TLm5uZoyZYpKS0u1Zs0avfDCC95rLly4UCNGjNCAAQNUV1enxx57TBUVFXr88cdbPW57IJgEAAAIsYkTJ+rIkSNatGiRDh06pKFDh2rz5s1KTEyUJB06dMjn2Y9JSUnavHmzZs+erccff1xxcXF67LHHdNNNN3nbfPXVV7rnnnvkdrvlcrk0fPhwbdu2TZdeemmrx20PDqtpd6ch6urq5HK5lFj4iLp87yGcAADAXJ4TJ3RwznzV1ta2al/h6TTFAzds/Y269eoeghkG77tvGvRS1tqQ3VtHQ2YSAAAY60xeZxjKOaB5HMABAABA0MhMAgAAYwVzmro95oDmEUwCAABjEUyaj2VuAAAABI3MJAAAMBaZSfMRTAIAAGMRTJqPZW4AAAAEjcwkAAAwliX7n/No1NtdDEQwCQAAjMUyt/kIJgEAgLEIJs3HnkkAAAAEjcwkAAAwFplJ8xFMAgAAYxFMmo9lbgAAAASNzCQAADCWZTlk2ZwZtHt80xFMAgAAY3nksP05k3aPbzqWuQEAABA0MpMAAMBYHMAxH8EkAAAwFnsmzccyNwAAAIJGZhIAABiLZW7zEUwCAABjscxtPoJJAABgLMuAzCTBZMvYMwkAAICgkZkEAADGsiRZlv1zQPMIJgEAgLE8csjBG3CMxjI3AAAAgkZmEgAAGIvT3OYjmAQAAMbyWA45eM6k0VjmBgAAQNDITAIAAGNZlgGnuTnO3SKCSQAAYCz2TJqPZW4AAAAEjcwkAAAwFplJ8xFMAgAAY3Ga23wscwMAAGM1HcCxuwRj+fLlSkpKUnh4uFJTU/Xmm2+22L6kpESpqakKDw/X+eefr5UrV/p8v3r1ao0aNUq9e/dW7969ddVVV2n37t0+bfLy8uRwOHxKbGxscDfQSgSTAAAAIbZhwwbl5ORo3rx5Ki8v16hRozRu3DhVVVUFbF9ZWalrrrlGo0aNUnl5uR566CHNnDlTGzdu9LZ54403dOutt+pvf/ubSktLlZCQoKysLH366ac+1xoyZIgOHTrkLe+++2673ivL3AAAwFinMoN275k89b91dXU+9U6nU06nM2CfpUuX6q677tLdd98tSSoqKtKrr76qFStWqKCgwK/9ypUrlZCQoKKiIklScnKy9u7dq0cffVQ33XSTJOn555/36bN69Wr9+c9/1uuvv6477rjDW9+1a9d2z0Z+H5lJAABgrKYDOHYXSYqPj5fL5fKWQEGhJDU0NKisrExZWVk+9VlZWdqxY0fAPqWlpX7tx4wZo7179+q7774L2Ofbb7/Vd999pz59+vjUf/DBB4qLi1NSUpImTZqkjz76qFU/62CRmQQAAGiF6upqRUZGej83l5U8fPiwGhsbFRMT41MfExMjt9sdsI/b7Q7Y/uTJkzp8+LD69evn1+fBBx/UT3/6U1111VXeuvT0dD377LP62c9+ps8//1yPPPKIMjMztW/fPvXt27fV99oWBJMAAMBY1r+K3XOQpMjISJ9g8nQcDt/lecuy/OpO1z5QvSQtWbJEL7zwgt544w2Fh4d768eNG+f994UXXqiMjAxdcMEFeuaZZ5Sbm9vqubcFwSQAADDW2ficyaioKIWFhfllIWtqavyyj01iY2MDtu/atatfRvHRRx9Vfn6+XnvtNV100UUtzqVXr1668MIL9cEHH7TpHtqCPZMAAAAh1L17d6Wmpqq4uNinvri4WJmZmQH7ZGRk+LXfunWr0tLS1K1bN2/dH/7wB/3+97/Xli1blJaWdtq51NfXa//+/QGXyUOFYBIAAJjLMqS0UW5urp588kmtXbtW+/fv1+zZs1VVVaVp06ZJkubOnetzAnvatGk6ePCgcnNztX//fq1du1Zr1qzR/fff722zZMkSzZ8/X2vXrlX//v3ldrvldrt17Ngxb5v7779fJSUlqqys1K5du3TzzTerrq5Od955Z9tvopXaJZj89NNPdfvtt6tv377q2bOnhg0bprKysvYYCgAAdGQmnOQOYpl94sSJKioq0qJFizRs2DBt27ZNmzdvVmJioiTp0KFDPs+cTEpK0ubNm/XGG29o2LBh+v3vf6/HHnvM+1gg6dRD0BsaGnTzzTerX79+3vLoo49623zyySe69dZbNXDgQN14443q3r27du7c6R23PYR8z+TRo0c1cuRIXXnllXrllVcUHR2tf/7zn/rJT34S6qEAAEAHdyZvoAnlHIIxffp0TZ8+PeB3Tz/9tF/dFVdcobfeeqvZ63388cenHXP9+vWtnV7IhDyYLCwsVHx8vJ566ilvXf/+/UM9DAAAAAwQ8mXul19+WWlpabrlllsUHR2t4cOHa/Xq1c22r6+vV11dnU8BAACQzHpoOQILeTD50UcfacWKFRowYIBeffVVTZs2TTNnztSzzz4bsH1BQYHP0+Tj4+NDPSUAAHC2atqzaHdBs0IeTHo8HqWkpCg/P1/Dhw/X1KlTNWXKFK1YsSJg+7lz56q2ttZbqqurQz0lAAAAtJOQ75ns16+fBg8e7FOXnJysjRs3Bmzf0kvSAQBA53Y2H8DpLEIeTI4cOVIHDhzwqXv//ffb9Ug6AADooEx6nyICCvky9+zZs7Vz507l5+frww8/1Lp167Rq1Srde++9oR4KAAAANgt5ZvKSSy7Rpk2bNHfuXC1atEhJSUkqKirSbbfdFuqhAABAB2fCaWq7xzddyINJSRo/frzGjx/fHpcGAACdDcvMRuPd3AAAAAhau2QmAQAAQoFlbvMRTAIAAHNxmtt4BJMAAMBgjn8Vu+eA5rBnEgAAAEEjMwkAAMzFMrfxCCYBAIC5CCaNxzI3AAAAgkZmEgAAmMtynCp2zwHNIpgEAADGsqxTxe45oHkscwMAACBoZCYBAIC5OIBjPIJJAABgLvZMGo9lbgAAAASNzCQAADCWwzpV7J4DmkcwCQAAzMWeSeMRTAIAAHOxZ9J47JkEAABA0MhMAgAAc7HMbTyCSQAAYC6CSeOxzA0AAICgkZkEAADmIjNpPIJJAABgLk5zG49lbgAAAASNzCQAADAWb8AxH8EkAAAwF3smjccyNwAAQDtYvny5kpKSFB4ertTUVL355pstti8pKVFqaqrCw8N1/vnna+XKlX5tNm7cqMGDB8vpdGrw4MHatGnTGY97pggmAQAAQmzDhg3KycnRvHnzVF5erlGjRmncuHGqqqoK2L6yslLXXHONRo0apfLycj300EOaOXOmNm7c6G1TWlqqiRMnKjs7W2+//bays7P1y1/+Urt27Qp63FBwWJZlVPK2rq5OLpdLiYWPqEt4uN3TAQAAreA5cUIH58xXbW2tIiMjz/h6JsUDTfdWXV3tc29Op1NOpzNgn/T0dKWkpGjFihXeuuTkZE2YMEEFBQV+7efMmaOXX35Z+/fv99ZNmzZNb7/9tkpLSyVJEydOVF1dnV555RVvm7Fjx6p379564YUXgho3FMhMAgAAczU9GsjuIik+Pl4ul8tbmgvOGhoaVFZWpqysLJ/6rKws7dixI2Cf0tJSv/ZjxozR3r179d1337XYpumawYwbChzAAQAAaIVAmclADh8+rMbGRsXExPjUx8TEyO12B+zjdrsDtj958qQOHz6sfv36Ndum6ZrBjBsKBJMAAMBcBp3mjoyMbNMSvsPh+7Bzy7L86k7X/of1rblmW8c9UwSTAADAXAYFk60VFRWlsLAwv2xgTU2NX9awSWxsbMD2Xbt2Vd++fVts03TNYMYNBfZMAgAAhFD37t2Vmpqq4uJin/ri4mJlZmYG7JORkeHXfuvWrUpLS1O3bt1abNN0zWDGDQUykwAAwFhn6xtwcnNzlZ2drbS0NGVkZGjVqlWqqqrStGnTJElz587Vp59+qmeffVbSqZPby5YtU25urqZMmaLS0lKtWbPGe0pbkmbNmqXLL79chYWFuuGGG/TSSy/ptdde0/bt21s9bnsgmAQAAOY6C5e5pVOP8Tly5IgWLVqkQ4cOaejQodq8ebMSExMlSYcOHfJ59mNSUpI2b96s2bNn6/HHH1dcXJwee+wx3XTTTd42mZmZWr9+vebPn6+HH35YF1xwgTZs2KD09PRWj9seeM4kAAA4Y+31nMn+jyy2PR7wnDihj+fPC9m9dTRkJgEAgLnO0sxkZ0IwCQAAjHW27pnsTDjNDQAAgKCRmQQAAOb63usMbZ0DmkUwCQAAzMWeSeMRTAIAAGOxZ9J87JkEAABA0MhMAgAAc7HMbTyCSQAAYC4DlrkJJlvGMjcAAACCRmYSAACYi2Vu4xFMAgAAcxFMGo9lbgAAAASNzCQAADAWz5k0H5lJAAAABI1gEgAAAEFjmRsAAJiLAzjGI5gEAADGYs+k+QgmAQCA2QjmjMaeSQAAAASNzCQAADAXeyaNRzAJAACMxZ5J87HMDQAAgKCRmQQAAOZimdt4BJMAAMBYLHObj2VuAAAABK3dg8mCggI5HA7l5OS091AAAKCjsQwpaFa7LnPv2bNHq1at0kUXXdSewwAAgI7KhGDO7vEN126ZyWPHjum2227T6tWr1bt372bb1dfXq66uzqcAAADg7NBuweS9996ra6+9VldddVWL7QoKCuRyubwlPj6+vaYEAADOMk0HcOwuaF67BJPr16/XW2+9pYKCgtO2nTt3rmpra72lurq6PaYEAADORnbvlTRhmd1wId8zWV1drVmzZmnr1q0KDw8/bXun0ymn0xnqaQAAgI7AhGDO7vENF/JgsqysTDU1NUpNTfXWNTY2atu2bVq2bJnq6+sVFhYW6mEBAABgg5AHkz//+c/17rvv+tT9+te/1qBBgzRnzhwCSQAA0Gom7Fm0e3zThTyYjIiI0NChQ33qevXqpb59+/rVAwAAtIhlbuPxBhwAAACbHD16VNnZ2d6n2mRnZ+urr75qsY9lWcrLy1NcXJx69Oih0aNHa9++fd7vv/zyS913330aOHCgevbsqYSEBM2cOVO1tbU+1+nfv78cDodPefDBB9t8Dz/Ku7nfeOONH2MYAADQwXT0Ze7Jkyfrk08+0ZYtWyRJ99xzj7Kzs/XXv/612T5LlizR0qVL9fTTT+tnP/uZHnnkEV199dU6cOCAIiIi9Nlnn+mzzz7To48+qsGDB+vgwYOaNm2aPvvsM/35z3/2udaiRYs0ZcoU7+dzzjmnzffwowSTAAAAQenAy9z79+/Xli1btHPnTqWnp0uSVq9erYyMDB04cEADBw70n4plqaioSPPmzdONN94oSXrmmWcUExOjdevWaerUqRo6dKg2btzo7XPBBRdo8eLFuv3223Xy5El17fr/wr+IiAjFxsae0X2wzA0AANAKP3xjX319/Rldr7S0VC6XyxtIStKIESPkcrm0Y8eOgH0qKyvldruVlZXlrXM6nbriiiua7SNJtbW1ioyM9AkkJamwsFB9+/bVsGHDtHjxYjU0NLT5PshMAgAAcxmUmfzhW/oWLFigvLy8oC/rdrsVHR3tVx8dHS23291sH0mKiYnxqY+JidHBgwcD9jly5Ih+//vfa+rUqT71s2bNUkpKinr37q3du3dr7ty5qqys1JNPPtmm+yCYBAAAxnL8q9g9B+nUi1kiIyO99c29dCUvL08LFy5s8Zp79uw5dW2H/91ZlhWw3mdOP/i+uT51dXW69tprNXjwYC1YsMDnu9mzZ3v/fdFFF6l37966+eabvdnK1iKYBAAAaIXIyEifYLI5M2bM0KRJk1ps079/f73zzjv6/PPP/b774osv/DKPTZr2N7rdbvXr189bX1NT49fn66+/1tixY3XOOedo06ZN6tatW4tzGjFihCTpww8/JJgEAAAdhEHL3K0VFRWlqKio07bLyMhQbW2tdu/erUsvvVSStGvXLtXW1iozMzNgn6SkJMXGxqq4uFjDhw+XJDU0NKikpESFhYXednV1dRozZoycTqdefvnlVr3iury8XJJ8gtTWIJgEAADG6siPBkpOTtbYsWM1ZcoUPfHEE5JOPRpo/PjxPie5Bw0apIKCAv3iF7+Qw+FQTk6O8vPzNWDAAA0YMED5+fnq2bOnJk+eLOlURjIrK0vffvutnnvuOe+BIUk699xzFRYWptLSUu3cuVNXXnmlXC6X9uzZo9mzZ+v6669XQkJCm+6DYBIAAJjrLMxMtsXzzz+vmTNnek9nX3/99Vq2bJlPmwMHDvg8cPyBBx7Q8ePHNX36dB09elTp6enaunWrIiIiJEllZWXatWuXJOnf/u3ffK5VWVmp/v37y+l0asOGDVq4cKHq6+uVmJioKVOm6IEHHmjzPTgsy7L7V+Sjrq5OLpdLiYWPqEsrUrIAAMB+nhMndHDOfO8jaM5UUzwwZGq+wpz2xgON9Se074mHQnZvHQ2ZSQAAYDaj0l74IYJJAABgrI68Z7Kj4A04AAAACBqZSQAAYK4OfgCnIyCYBAAAxmKZ23wscwMAACBoZCYBAIC5WOY2HsEkAAAwFsvc5mOZGwAAAEEjMwkAAMzFMrfxCCYBAIC5CCaNRzAJAACMxZ5J87FnEgAAAEEjMwkAAMzFMrfxCCYBAICxHJYlh2VvNGf3+KZjmRsAAABBIzMJAADMxTK38QgmAQCAsTjNbT6WuQEAABA0MpMAAMBcLHMbj2ASAAAYi2Vu87HMDQAAgKCRmQQAAOZimdt4BJMAAMBYLHObj2ASAACYi8yk8dgzCQAAgKCRmQQAAEZjmdlsBJMAAMBclnWq2D0HNItlbgAAAASNzCQAADAWp7nNRzAJAADMxWlu47HMDQAAgKCRmQQAAMZyeE4Vu+eA5hFMAgAAc7HMbTyWuQEAABA0gkkAAGCsptPcdpf2cvToUWVnZ8vlcsnlcik7O1tfffVVi30sy1JeXp7i4uLUo0cPjR49Wvv27fNpM3r0aDkcDp8yadKkMx47EIJJAABgrqaHlttd2snkyZNVUVGhLVu2aMuWLaqoqFB2dnaLfZYsWaKlS5dq2bJl2rNnj2JjY3X11Vfr66+/9mk3ZcoUHTp0yFueeOKJMx47EPZMAgAAY3Xk50zu379fW7Zs0c6dO5Weni5JWr16tTIyMnTgwAENHDjQr49lWSoqKtK8efN04403SpKeeeYZxcTEaN26dZo6daq3bc+ePRUbGxuysZtDZhIAAKAV6urqfEp9ff0ZXa+0tFQul8sbzEnSiBEj5HK5tGPHjoB9Kisr5Xa7lZWV5a1zOp264oor/Po8//zzioqK0pAhQ3T//ff7ZC6DGbs5ZCYBAIC5DDrNHR8f71O9YMEC5eXlBX1Zt9ut6Ohov/ro6Gi53e5m+0hSTEyMT31MTIwOHjzo/XzbbbcpKSlJsbGxeu+99zR37ly9/fbbKi4uDnrs5hBMAgAAY5m0zF1dXa3IyEhvvdPpDNg+Ly9PCxcubPGae/bsOXVth8PvO8uyAtb7zOkH3/+wz5QpU7z/Hjp0qAYMGKC0tDS99dZbSklJOaOxf4hgEgAAoBUiIyN9gsnmzJgxw+/k9A/1799f77zzjj7//HO/77744gu/zGOTpj2Qbrdb/fr189bX1NQ020eSUlJS1K1bN33wwQdKSUlRbGxsm8duDsEkAAAwVzufpm71HNogKipKUVFRp22XkZGh2tpa7d69W5deeqkkadeuXaqtrVVmZmbAPk1L18XFxRo+fLgkqaGhQSUlJSosLGx2rH379um7777zBqDBjN0cDuAAAABj2f18yfZcZk9OTtbYsWM1ZcoU7dy5Uzt37tSUKVM0fvx4n9PUgwYN0qZNm079PBwO5eTkKD8/X5s2bdJ7772nX/3qV+rZs6cmT54sSfrnP/+pRYsWae/evfr444+1efNm3XLLLRo+fLhGjhzZprFbg8wkAACATZ5//nnNnDnTezr7+uuv17Jly3zaHDhwQLW1td7PDzzwgI4fP67p06fr6NGjSk9P19atWxURESFJ6t69u15//XX913/9l44dO6b4+Hhde+21WrBggcLCwto0dms4LMvu3LGvuro6uVwuJRY+oi7h4XZPBwAAtILnxAkdnDNftbW1rdpXeDpN8UDG2EXq2s3eeODkdydUuuV3Ibu3jobMJAAAMJZJp7kRGHsmAQAAEDQykwAAwFwe61Sxew5oFsEkAAAwl0FvwEFgBJMAAMBYDtm/Z7Ft74PpfNgzCQAAgKCRmQQAAOY6C9+A09kQTAIAAGPxaCDzhXyZu6CgQJdccokiIiIUHR2tCRMm6MCBA6EeBgAAAAYIeTBZUlKie++9Vzt37lRxcbFOnjyprKwsffPNN6EeCgAAdHSWIQXNCvky95YtW3w+P/XUU4qOjlZZWZkuv/xyv/b19fWqr6/3fq6rqwv1lAAAwFnKYVly2Lxn0e7xTdfup7mbXkzep0+fgN8XFBTI5XJ5S3x8fHtPCQAAACHSrsGkZVnKzc3VZZddpqFDhwZsM3fuXNXW1npLdXV1e04JAACcTTyGFDSrXU9zz5gxQ++88462b9/ebBun0ymn09me0wAAAGcplrnN127B5H333aeXX35Z27Zt03nnnddewwAAAMBGIQ8mLcvSfffdp02bNumNN95QUlJSqIcAAACdhQmnqe0e33AhDybvvfderVu3Ti+99JIiIiLkdrslSS6XSz169Aj1cAAAoCPjDTjGC3kwuWLFCknS6NGjfeqfeuop/epXvwr1cAAAoAPjDTjma5dlbgAAAHQOvJsbAACYi2Vu4xFMAgAAYzk8p4rdc0Dz2v0NOAAAAOi4yEwCAABzscxtPIJJAABgLp4zaTyWuQEAABA0MpMAAMBYvJvbfASTAADAXOyZNB7L3AAAAAgamUkAAGAuS5Ldz3kkMdkigkkAAGAs9kyaj2ASAACYy5L9exaJJVvEnkkAAAAEjcwkAAAwF6e5jUcwCQAAzOWR5DBgDmgWy9wAAAAIGsEkAAAwVtNpbrtLezl69Kiys7PlcrnkcrmUnZ2tr776qsU+lmUpLy9PcXFx6tGjh0aPHq19+/Z5v//444/lcDgClv/5n//xtuvfv7/f9w8++GCb74FgEgAAmKtpz6TdpZ1MnjxZFRUV2rJli7Zs2aKKigplZ2e32GfJkiVaunSpli1bpj179ig2NlZXX321vv76a0lSfHy8Dh065FMWLlyoXr16ady4cT7XWrRokU+7+fPnt/ke2DMJAABgg/3792vLli3auXOn0tPTJUmrV69WRkaGDhw4oIEDB/r1sSxLRUVFmjdvnm688UZJ0jPPPKOYmBitW7dOU6dOVVhYmGJjY336bdq0SRMnTtQ555zjUx8REeHXtq3ITAIAAHPZnZH8Xmayrq7Op9TX15/RrZWWlsrlcnkDSUkaMWKEXC6XduzYEbBPZWWl3G63srKyvHVOp1NXXHFFs33KyspUUVGhu+66y++7wsJC9e3bV8OGDdPixYvV0NDQ5vsgMwkAAMxl0KOB4uPjfaoXLFigvLy8oC/rdrsVHR3tVx8dHS23291sH0mKiYnxqY+JidHBgwcD9lmzZo2Sk5OVmZnpUz9r1iylpKSod+/e2r17t+bOnavKyko9+eSTbboPgkkAAIBWqK6uVmRkpPez0+kM2C4vL08LFy5s8Vp79uyRJDkc/s89siwrYP33/fD75vocP35c69at08MPP+z33ezZs73/vuiii9S7d2/dfPPN3mxlaxFMAgAAcxn0nMnIyEifYLI5M2bM0KRJk1ps079/f73zzjv6/PPP/b774osv/DKPTZr2N7rdbvXr189bX1NTE7DPn//8Z3377be64447TjvvESNGSJI+/PBDgkkAANAxtPejeVo7h7aIiopSVFTUadtlZGSotrZWu3fv1qWXXipJ2rVrl2pra/2WpJskJSUpNjZWxcXFGj58uCSpoaFBJSUlKiws9Gu/Zs0aXX/99Tr33HNPO5/y8nJJ8glSW4NgEgAAmMugPZOhlpycrLFjx2rKlCl64oknJEn33HOPxo8f73OSe9CgQSooKNAvfvELORwO5eTkKD8/XwMGDNCAAQOUn5+vnj17avLkyT7X//DDD7Vt2zZt3rzZb+zS0lLt3LlTV155pVwul/bs2aPZs2fr+uuvV0JCQpvug2ASAADAJs8//7xmzpzpPZ19/fXXa9myZT5tDhw4oNraWu/nBx54QMePH9f06dN19OhRpaena+vWrYqIiPDpt3btWv30pz/1OfndxOl0asOGDVq4cKHq6+uVmJioKVOm6IEHHmjzPTgsy+5w31ddXZ1cLpcSCx9Rl/Bwu6cDAABawXPihA7Oma/a2tpW7Ss8naZ44KoLctQ1LPBBlx/LycZ6vfbPopDdW0dDZhIAAJirAy9zdxQ8tBwAAABBIzMJAAAMZkBmUnaPbzaCSQAAYC6WuY3HMjcAAACCRmYSAACYy2PJ9mVmD5nJlhBMAgAAc1meU8XuOaBZLHMDAAAgaGQmAQCAuTiAYzyCSQAAYC72TBqPYBIAAJiLzKTx2DMJAACAoJGZBAAA5rJkf2aQxGSLCCYBAIC5WOY2HsvcAAAACBqZSQAAYC6PR5LNDw338NDylhBMAgAAc7HMbTyWuQEAABA0MpMAAMBcZCaNRzAJAADMxRtwjMcyNwAAAIJGZhIAABjLsjyyLHtPU9s9vukIJgEAgLksy/5lZvZMtohgEgAAmMsyYM8kwWSL2DMJAACAoJGZBAAA5vJ4JIfNexbZM9kigkkAAGAulrmNxzI3AAAAgkZmEgAAGMvyeGTZvMzNo4FaRjAJAADMxTK38VjmBgAAQNDITAIAAHN5LMlBZtJkBJMAAMBcliXJ7kcDEUy2hGVuAAAABI3MJAAAMJblsWTZvMxtkZlsEcEkAAAwl+WR/cvcPBqoJe22zL18+XIlJSUpPDxcqampevPNN9trKAAA0EFZHsuI0l6OHj2q7OxsuVwuuVwuZWdn66uvvmqxz4svvqgxY8YoKipKDodDFRUVfm3q6+t13333KSoqSr169dL111+vTz755IzHDqRdgskNGzYoJydH8+bNU3l5uUaNGqVx48apqqqqPYYDAAA4K02ePFkVFRXasmWLtmzZooqKCmVnZ7fY55tvvtHIkSP1H//xH822ycnJ0aZNm7R+/Xpt375dx44d0/jx49XY2HhGYwfisNphI0B6erpSUlK0YsUKb11ycrImTJiggoICn7b19fWqr6/3fq6trVVCQoLiF85Xl/DwUE8NAAC0A8+JE6pe8Ii++uoruVyuM75eXV2dXC6XLtM16qpuIZhh8E7qO23XZlVXVysyMtJb73Q65XQ6g77u/v37NXjwYO3cuVPp6emSpJ07dyojI0P/93//p4EDB7bY/+OPP1ZSUpLKy8s1bNgwb31tba3OPfdc/elPf9LEiRMlSZ999pni4+O1efNmjRkz5ozH9mGFWH19vRUWFma9+OKLPvUzZ860Lr/8cr/2CxYsaHq0PYVCoVAolLO8/POf/wxJPHH8+HErNjbW9vtpKuecc45f3YIFC87oHtesWWO5XC6/epfLZa1du/a0/SsrKy1JVnl5uU/966+/bkmyvvzyS5/6iy66yPrd734XkrG/L+QHcA4fPqzGxkbFxMT41MfExMjtdvu1nzt3rnJzc72fv/rqKyUmJqqqqiok/2WDM1NXV6f4+Hi//xrDj4/fhTn4XZiD34U5mlYW+/TpE5LrhYeHq7KyUg0NDSG53pmyLEsOh8On7kyykpLkdrsVHR3tVx8dHR0wZmrLdbt3767evXv71H8/Fgvl2O12mvuHP/BAvwSp+RSxy+Xi/xgMEhkZye/DEPwuzMHvwhz8LszRpUvojmOEh4cr/Czc8paXl6eFCxe22GbPnj2S/OMlqfmY6Uz98LqhGjvkwWRUVJTCwsL8otqamhq/bCUAAEBHM2PGDE2aNKnFNv3799c777yjzz//3O+7L7744oxiptjYWDU0NOjo0aM+2cmamhplZmZ624Rq7JCf5u7evbtSU1NVXFzsU19cXOy9AQAAgI4qKipKgwYNarGEh4crIyNDtbW12r17t7fvrl27VFtbe0YxU2pqqrp16+YTix06dEjvvfee97qhHLtdlrlzc3OVnZ2ttLQ0ZWRkaNWqVaqqqtK0adNO29fpdGrBggVnvA8BocHvwxz8LszB78Ic/C7Mwe+i7ZKTkzV27FhNmTJFTzzxhCTpnnvu0fjx431OUw8aNEgFBQX6xS9+IUn68ssvVVVVpc8++0ySdODAAUmnso2xsbFyuVy666679O///u/q27ev+vTpo/vvv18XXnihrrrqqjaN3SptOq7TBo8//riVmJhode/e3UpJSbFKSkraaygAAICz0pEjR6zbbrvNioiIsCIiIqzbbrvNOnr0qE8bSdZTTz3l/fzUU08FPHH+/dPlx48ft2bMmGH16dPH6tGjhzV+/HirqqqqzWO3Rrs8ZxIAAACdQ7u9ThEAAAAdH8EkAAAAgkYwCQAAgKARTAIAACBoxgWTy5cvV1JSksLDw5Wamqo333zT7il1OgUFBbrkkksUERGh6OhoTZgwwfvYAdiroKBADodDOTk5dk+l0/r00091++23q2/fvurZs6eGDRumsrIyu6fV6Zw8eVLz589XUlKSevToofPPP1+LFi2Sx+Oxe2od3rZt23TdddcpLi5ODodDf/nLX3y+tyxLeXl5iouLU48ePTR69Gjt27fPnsniR2FUMLlhwwbl5ORo3rx5Ki8v16hRozRu3DhVVVXZPbVOpaSkRPfee6927typ4uJinTx5UllZWfrmm2/snlqntmfPHq1atUoXXXSR3VPptI4ePaqRI0eqW7dueuWVV/SPf/xD//mf/6mf/OQndk+t0yksLNTKlSu1bNky7d+/X0uWLNEf/vAH/fGPf7R7ah3eN998o4svvljLli0L+P2SJUu0dOlSLVu2THv27FFsbKyuvvpqff311z/yTPFjMerRQOnp6UpJSdGKFSu8dcnJyZowYYIKCgpsnFnn9sUXXyg6OlolJSW6/PLL7Z5Op3Ts2DGlpKRo+fLleuSRRzRs2DAVFRXZPa1O58EHH9Tf//53VkwMMH78eMXExGjNmjXeuptuukk9e/bUn/70Jxtn1rk4HA5t2rRJEyZMkHQqKxkXF6ecnBzNmTNHklRfX6+YmBgVFhZq6tSpNs4W7cWYzGRDQ4PKysqUlZXlU5+VlaUdO3bYNCtIUm1trSSpT58+Ns+k87r33nt17bXXet9cAHu8/PLLSktL0y233KLo6GgNHz5cq1evtntandJll12m119/Xe+//74k6e2339b27dt1zTXX2Dyzzq2yslJut9vnb7nT6dQVV1zB3/IOrF1epxiMw4cPq7Gx0e/l4jExMXK73TbNCpZlKTc3V5dddpmGDh1q93Q6pfXr1+utt97Snj177J5Kp/fRRx9pxYoVys3N1UMPPaTdu3dr5syZcjqduuOOO+yeXqcyZ84c1dbWatCgQQoLC1NjY6MWL16sW2+91e6pdWpNf68D/S0/ePCgHVPCj8CYYLKJw+Hw+WxZll8dfjwzZszQO++8o+3bt9s9lU6purpas2bN0tatWxUeHm73dDo9j8ejtLQ05efnS5KGDx+uffv2acWKFQSTP7INGzboueee07p16zRkyBBVVFQoJydHcXFxuvPOO+2eXqfH3/LOxZhgMioqSmFhYX5ZyJqaGr//wsGP47777tPLL7+sbdu26bzzzrN7Op1SWVmZampqlJqa6q1rbGzUtm3btGzZMtXX1yssLMzGGXYu/fr10+DBg33qkpOTtXHjRptm1Hn99re/1YMPPqhJkyZJki688EIdPHhQBQUFBJM2io2NlXQqQ9mvXz9vPX/LOzZj9kx2795dqampKi4u9qkvLi5WZmamTbPqnCzL0owZM/Tiiy/qf//3f5WUlGT3lDqtn//853r33XdVUVHhLWlpabrttttUUVFBIPkjGzlypN9jst5//30lJibaNKPO69tvv1WXLr5/wsLCwng0kM2SkpIUGxvr87e8oaFBJSUl/C3vwIzJTEpSbm6usrOzlZaWpoyMDK1atUpVVVWaNm2a3VPrVO69916tW7dOL730kiIiIrzZYpfLpR49etg8u84lIiLCb69qr1691LdvX/aw2mD27NnKzMxUfn6+fvnLX2r37t1atWqVVq1aZffUOp3rrrtOixcvVkJCgoYMGaLy8nItXbpUv/nNb+yeWod37Ngxffjhh97PlZWVqqioUJ8+fZSQkKCcnBzl5+drwIABGjBggPLz89WzZ09NnjzZxlmjXVmGefzxx63ExESre/fuVkpKilVSUmL3lDodSQHLU089ZffUYFnWFVdcYc2aNcvuaXRaf/3rX62hQ4daTqfTGjRokLVq1Sq7p9Qp1dXVWbNmzbISEhKs8PBw6/zzz7fmzZtn1dfX2z21Du9vf/tbwL8Rd955p2VZluXxeKwFCxZYsbGxltPptC6//HLr3XfftXfSaFdGPWcSAAAAZxdj9kwCAADg7EMwCQAAgKARTAIAACBoBJMAAAAIGsEkAAAAgkYwCQAAgKARTAIAACBoBJMAAAAIGsEkAAAAgkYwCQAAgKARTAIAACBo/z9PZqvCWX78PwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h = by.Histo2D(10, 0, 10, 10, 0, 10)\n", + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2707406c-b066-4bb6-8e2b-27d6ce2856f1", + "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": [ + "h.fill(2, 5)\n", + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "380ada5f-71bc-4076-b09a-e0bbf4b9c7b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.binAt(2, 5).sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a765697-d1f2-4879-9bf0-5a8abfe2963d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h[2, 5].sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a4e6242f-23d6-4717-94b8-c35f513c1dfc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h[by.loc(2), by.loc(5)].sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "113a87ff-1135-42ee-9f2e-d2a089a163b2", + "metadata": {}, + "outputs": [], + "source": [ + "from babyyoda.histo2D import Histo2D\n", + "from babyyoda.grogu.histo2d_v2 import GROGU_HISTO2D_V2\n", + "from babyyoda.util import loc" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b51dce74-2b89-4abc-94e4-c37369f04f9d", + "metadata": {}, + "outputs": [], + "source": [ + "def create_histo2d():\n", + " import yoda as yd\n", + "\n", + " h = yd.Histo2D(10, 0, 10, 10, 0, 10, title=\"test\")\n", + "\n", + " g = GROGU_HISTO2D_V2(\n", + " d_title=\"test\",\n", + " d_bins=[\n", + " GROGU_HISTO2D_V2.Bin(\n", + " d_xmin=hb.xMin(), d_xmax=hb.xMax(), d_ymin=hb.yMin(), d_ymax=hb.yMax()\n", + " )\n", + " for hb in h.bins()\n", + " ],\n", + " d_underflow=GROGU_HISTO2D_V2.Bin(),\n", + " d_overflow=GROGU_HISTO2D_V2.Bin(),\n", + " )\n", + "\n", + " for i in range(12):\n", + " for j in range(12):\n", + " for _ in range(i * j):\n", + " h.fill(i, j)\n", + " g.fill(i, j)\n", + " return Histo2D(h), Histo2D(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "218d38e8-129f-4825-b241-bace660bc799", + "metadata": {}, + "outputs": [], + "source": [ + "h, g = create_histo2d()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ce251732-1254-4eb3-9419-418a5c75ea9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h[loc(3), loc(4)].sumWX()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "171736b9-913b-45fb-ba98-6ed898bb82fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.binAt(3, 4).sumWX()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c83ce7bf-9f5a-4059-a36a-a639cf170d3d", + "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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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ac5be2a-5383-4275-a07f-0521bb07ae37", + "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": [ + "g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca32c001-bde4-4bed-9e7a-700e19d7411e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/debug/histo2d.ipynb b/debug/histo2d.ipynb index da57369..8946f0f 100644 --- a/debug/histo2d.ipynb +++ b/debug/histo2d.ipynb @@ -2,25 +2,75 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "id": "45563c8b-4735-4fd4-940a-e3ed0b6eaf41", "metadata": {}, "outputs": [], "source": [ "import babyyoda as grogu\n", "from matplotlib import pyplot as plot\n", - "from babyyoda import loc" + "from babyyoda import loc\n", + "import yoda as yd" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "62324cf2-abda-44c1-be2c-ab439577f4eb", "metadata": {}, "outputs": [], "source": [ "hists = grogu.read_yoda(\"../tests/test_histo2d_v2.yoda\")\n", - "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]" + "h = hists[\"/ALICE_2020_I1797621/GGPdfXQ\"]\n", + "\n", + "yd.write([h.target], \"test_histo2d_v3.yoda\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6731d1bc-093b-4445-a213-eb3230e72536", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.binAt(300, 4000).sumW()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4ed6e9b2-8864-41be-bafd-15eb8da68982", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "loc 3.0 is not in the range of [(1e-06, 1.318257e-06), (1.318257e-06, 1.737801e-06), (1.737801e-06, 2.290868e-06), (2.290868e-06, 3.019952e-06), (3.019952e-06, 3.981072e-06), (3.981072e-06, 5.248075e-06), (5.248075e-06, 6.91831e-06), (6.91831e-06, 9.120108e-06), (9.120108e-06, 1.202264e-05), (1.202264e-05, 1.584893e-05), (1.584893e-05, 2.089296e-05), (2.089296e-05, 2.754229e-05), (2.754229e-05, 3.630781e-05), (3.630781e-05, 4.786301e-05), (4.786301e-05, 6.309573e-05), (6.309573e-05, 8.317638e-05), (8.317638e-05, 0.0001096478), (0.0001096478, 0.000144544), (0.000144544, 0.0001905461), (0.0001905461, 0.0002511886), (0.0002511886, 0.0003311311), (0.0003311311, 0.0004365158), (0.0004365158, 0.0005754399), (0.0005754399, 0.0007585776), (0.0007585776, 0.001), (0.001, 0.001318257), (0.001318257, 0.001737801), (0.001737801, 0.002290868), (0.002290868, 0.003019952), (0.003019952, 0.003981072), (0.003981072, 0.005248075), (0.005248075, 0.00691831), (0.00691831, 0.009120108), (0.009120108, 0.01202264), (0.01202264, 0.01584893), (0.01584893, 0.02089296), (0.02089296, 0.02754229), (0.02754229, 0.03630781), (0.03630781, 0.04786301), (0.04786301, 0.06309573), (0.06309573, 0.08317638), (0.08317638, 0.1096478), (0.1096478, 0.144544), (0.144544, 0.1905461), (0.1905461, 0.2511886), (0.2511886, 0.3311311), (0.3311311, 0.4365158), (0.4365158, 0.5754399), (0.5754399, 0.7585776), (0.7585776, 1.0)]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mh\u001b[49m\u001b[43m[\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4.\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msumW()\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:161\u001b[0m, in \u001b[0;36mHisto2D.__getitem__\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slices, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(slices) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 161\u001b[0m ix, iy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ix, \u001b[38;5;28mint\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(iy, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_by_indices(ix, iy)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:151\u001b[0m, in \u001b[0;36mHisto2D.__get_indices\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_indices\u001b[39m(\u001b[38;5;28mself\u001b[39m, slices):\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_x_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_y_index(slices[\u001b[38;5;241m1\u001b[39m])\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:139\u001b[0m, in \u001b[0;36mHisto2D.__get_x_index\u001b[0;34m(self, slices)\u001b[0m\n\u001b[1;32m 137\u001b[0m ix \u001b[38;5;241m=\u001b[39m slices\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slices, loc):\n\u001b[0;32m--> 139\u001b[0m ix \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_index_by_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ix\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/histo2D.py:132\u001b[0m, in \u001b[0;36mHisto2D.__get_index_by_loc\u001b[0;34m(self, loc, bins)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m loc\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mand\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m<\u001b[39m b:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mindex((a, b)) \u001b[38;5;241m+\u001b[39m loc\u001b[38;5;241m.\u001b[39moffset\n\u001b[0;32m--> 132\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloc \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloc\u001b[38;5;241m.\u001b[39mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not in the range of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbins\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: loc 3.0 is not in the range of [(1e-06, 1.318257e-06), (1.318257e-06, 1.737801e-06), (1.737801e-06, 2.290868e-06), (2.290868e-06, 3.019952e-06), (3.019952e-06, 3.981072e-06), (3.981072e-06, 5.248075e-06), (5.248075e-06, 6.91831e-06), (6.91831e-06, 9.120108e-06), (9.120108e-06, 1.202264e-05), (1.202264e-05, 1.584893e-05), (1.584893e-05, 2.089296e-05), (2.089296e-05, 2.754229e-05), (2.754229e-05, 3.630781e-05), (3.630781e-05, 4.786301e-05), (4.786301e-05, 6.309573e-05), (6.309573e-05, 8.317638e-05), (8.317638e-05, 0.0001096478), (0.0001096478, 0.000144544), (0.000144544, 0.0001905461), (0.0001905461, 0.0002511886), (0.0002511886, 0.0003311311), (0.0003311311, 0.0004365158), (0.0004365158, 0.0005754399), (0.0005754399, 0.0007585776), (0.0007585776, 0.001), (0.001, 0.001318257), (0.001318257, 0.001737801), (0.001737801, 0.002290868), (0.002290868, 0.003019952), (0.003019952, 0.003981072), (0.003981072, 0.005248075), (0.005248075, 0.00691831), (0.00691831, 0.009120108), (0.009120108, 0.01202264), (0.01202264, 0.01584893), (0.01584893, 0.02089296), (0.02089296, 0.02754229), (0.02754229, 0.03630781), (0.03630781, 0.04786301), (0.04786301, 0.06309573), (0.06309573, 0.08317638), (0.08317638, 0.1096478), (0.1096478, 0.144544), (0.144544, 0.1905461), (0.1905461, 0.2511886), (0.2511886, 0.3311311), (0.3311311, 0.4365158), (0.4365158, 0.5754399), (0.5754399, 0.7585776), (0.7585776, 1.0)]" + ] + } + ], + "source": [ + "h[loc(3.0), loc(4.0)].sumW()" ] }, { diff --git a/debug/test_histo2d_v3.yoda b/debug/test_histo2d_v3.yoda new file mode 100644 index 0000000..d26244f --- /dev/null +++ b/debug/test_histo2d_v3.yoda @@ -0,0 +1,2716 @@ +BEGIN YODA_HISTO2D_V3 /ALICE_2020_I1797621/GGPdfXQ +Path: /ALICE_2020_I1797621/GGPdfXQ +ScaledBy: 1.00000000000000002e-02 +Title: ~ +Type: Histo2D +--- +# Mean: (5.833908e-03, 2.987324e+00) +# Integral: 1.765968e+08 +Edges(A1): [1.000000e-06, 1.318257e-06, 1.737801e-06, 2.290868e-06, 3.019952e-06, 3.981072e-06, 5.248075e-06, 6.918310e-06, 9.120108e-06, 1.202264e-05, 1.584893e-05, 2.089296e-05, 2.754229e-05, 3.630781e-05, 4.786301e-05, 6.309573e-05, 8.317638e-05, 1.096478e-04, 1.445440e-04, 1.905461e-04, 2.511886e-04, 3.311311e-04, 4.365158e-04, 5.754399e-04, 7.585776e-04, 1.000000e-03, 1.318257e-03, 1.737801e-03, 2.290868e-03, 3.019952e-03, 3.981072e-03, 5.248075e-03, 6.918310e-03, 9.120108e-03, 1.202264e-02, 1.584893e-02, 2.089296e-02, 2.754229e-02, 3.630781e-02, 4.786301e-02, 6.309573e-02, 8.317638e-02, 1.096478e-01, 1.445440e-01, 1.905461e-01, 2.511886e-01, 3.311311e-01, 4.365158e-01, 5.754399e-01, 7.585776e-01, 1.000000e+00] +Edges(A2): [0.000000e+00, 6.000000e-01, 1.200000e+00, 1.800000e+00, 2.400000e+00, 3.000000e+00, 3.600000e+00, 4.200000e+00, 4.800000e+00, 5.400000e+00, 6.000000e+00, 6.600000e+00, 7.200000e+00, 7.800000e+00, 8.400000e+00, 9.000000e+00, 9.600000e+00, 1.020000e+01, 1.080000e+01, 1.140000e+01, 1.200000e+01, 1.260000e+01, 1.320000e+01, 1.380000e+01, 1.440000e+01, 1.500000e+01, 1.560000e+01, 1.620000e+01, 1.680000e+01, 1.740000e+01, 1.800000e+01, 1.860000e+01, 1.920000e+01, 1.980000e+01, 2.040000e+01, 2.100000e+01, 2.160000e+01, 2.220000e+01, 2.280000e+01, 2.340000e+01, 2.400000e+01, 2.460000e+01, 2.520000e+01, 2.580000e+01, 2.640000e+01, 2.700000e+01, 2.760000e+01, 2.820000e+01, 2.880000e+01, 2.940000e+01, 3.000000e+01] +# sumW sumW2 sumW(A1) sumW2(A1) sumW(A2) sumW2(A2) sumW(A1,A2) numEntries +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 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0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +END YODA_HISTO2D_V3 diff --git a/debug/test_histo2s_v3.yoda b/debug/test_histo2s_v3.yoda new file mode 100644 index 0000000..d26244f --- /dev/null +++ b/debug/test_histo2s_v3.yoda @@ -0,0 +1,2716 @@ +BEGIN YODA_HISTO2D_V3 /ALICE_2020_I1797621/GGPdfXQ +Path: /ALICE_2020_I1797621/GGPdfXQ +ScaledBy: 1.00000000000000002e-02 +Title: ~ +Type: Histo2D +--- +# Mean: (5.833908e-03, 2.987324e+00) +# Integral: 1.765968e+08 +Edges(A1): [1.000000e-06, 1.318257e-06, 1.737801e-06, 2.290868e-06, 3.019952e-06, 3.981072e-06, 5.248075e-06, 6.918310e-06, 9.120108e-06, 1.202264e-05, 1.584893e-05, 2.089296e-05, 2.754229e-05, 3.630781e-05, 4.786301e-05, 6.309573e-05, 8.317638e-05, 1.096478e-04, 1.445440e-04, 1.905461e-04, 2.511886e-04, 3.311311e-04, 4.365158e-04, 5.754399e-04, 7.585776e-04, 1.000000e-03, 1.318257e-03, 1.737801e-03, 2.290868e-03, 3.019952e-03, 3.981072e-03, 5.248075e-03, 6.918310e-03, 9.120108e-03, 1.202264e-02, 1.584893e-02, 2.089296e-02, 2.754229e-02, 3.630781e-02, 4.786301e-02, 6.309573e-02, 8.317638e-02, 1.096478e-01, 1.445440e-01, 1.905461e-01, 2.511886e-01, 3.311311e-01, 4.365158e-01, 5.754399e-01, 7.585776e-01, 1.000000e+00] +Edges(A2): [0.000000e+00, 6.000000e-01, 1.200000e+00, 1.800000e+00, 2.400000e+00, 3.000000e+00, 3.600000e+00, 4.200000e+00, 4.800000e+00, 5.400000e+00, 6.000000e+00, 6.600000e+00, 7.200000e+00, 7.800000e+00, 8.400000e+00, 9.000000e+00, 9.600000e+00, 1.020000e+01, 1.080000e+01, 1.140000e+01, 1.200000e+01, 1.260000e+01, 1.320000e+01, 1.380000e+01, 1.440000e+01, 1.500000e+01, 1.560000e+01, 1.620000e+01, 1.680000e+01, 1.740000e+01, 1.800000e+01, 1.860000e+01, 1.920000e+01, 1.980000e+01, 2.040000e+01, 2.100000e+01, 2.160000e+01, 2.220000e+01, 2.280000e+01, 2.340000e+01, 2.400000e+01, 2.460000e+01, 2.520000e+01, 2.580000e+01, 2.640000e+01, 2.700000e+01, 2.760000e+01, 2.820000e+01, 2.880000e+01, 2.940000e+01, 3.000000e+01] +# sumW sumW2 sumW(A1) sumW2(A1) sumW(A2) sumW2(A2) sumW(A1,A2) numEntries +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 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0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +END YODA_HISTO2D_V3 diff --git a/src/babyyoda/grogu/histo1d_v3.py b/src/babyyoda/grogu/histo1d_v3.py index 700b174..cf17ae3 100644 --- a/src/babyyoda/grogu/histo1d_v3.py +++ b/src/babyyoda/grogu/histo1d_v3.py @@ -154,6 +154,8 @@ def from_string(cls, string: str) -> "GROGU_HISTO1D_V3.Bin": def __post_init__(self): self.d_type = "Histo1D" + # one more edge than bins, subtract 2 for underflow and overflow + assert len(self.d_edges) == len(self.d_bins) + 1 - 2 ############################################ # YODA compatibilty code @@ -265,7 +267,10 @@ def from_string(cls, file_content: str, key: str = "") -> "GROGU_HISTO1D_V3": continue if line.startswith("Edges"): - numbers_as_strings = re.findall(r"[-+]?\d*\.\d+e[+-]?\d+|\d+", line) + content = re.findall(r"\[(.*?)\]", line) + numbers_as_strings = re.findall( + r"[-+]?\d*\.\d+e[+-]?\d+|\d+", content[0] + ) edges = [float(i) for i in numbers_as_strings] continue diff --git a/src/babyyoda/grogu/histo2d_v2.py b/src/babyyoda/grogu/histo2d_v2.py index 131a2aa..6ea98b4 100644 --- a/src/babyyoda/grogu/histo2d_v2.py +++ b/src/babyyoda/grogu/histo2d_v2.py @@ -38,11 +38,6 @@ def fill(self, x: float, y: float, weight: float = 1.0, fraction=1.0): self.d_numentries += fraction def set_bin(self, bin): - # TODO allow modify those? - # self.d_xmin = bin.xMin() - # self.d_xmax = bin.xMax() - # self.d_ymin = bin.yMin() - # self.d_ymax = bin.yMax() self.d_sumw = bin.sumW() self.d_sumw2 = bin.sumW2() self.d_sumwx = bin.sumWX() @@ -178,7 +173,7 @@ 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_xmin, b.d_ymin)) + return sorted(self.d_bins, key=lambda b: (b.d_ymin, b.d_xmin)) def bin(self, index): return self.bins()[index] diff --git a/src/babyyoda/grogu/histo2d_v3.py b/src/babyyoda/grogu/histo2d_v3.py new file mode 100644 index 0000000..6b69ab5 --- /dev/null +++ b/src/babyyoda/grogu/histo2d_v3.py @@ -0,0 +1,228 @@ +import re +from dataclasses import dataclass, field +from typing import List + +from babyyoda.grogu.analysis_object import GROGU_ANALYSIS_OBJECT + + +@dataclass +class GROGU_HISTO2D_V3(GROGU_ANALYSIS_OBJECT): + @dataclass + class Bin: + d_sumw: float = 0.0 + d_sumw2: float = 0.0 + d_sumwx: float = 0.0 + d_sumwx2: float = 0.0 + d_sumwy: float = 0.0 + d_sumwy2: float = 0.0 + d_sumwxy: float = 0.0 + d_numentries: float = 0.0 + + def fill(self, x: float, y: float, weight: float = 1.0, fraction=1.0): + sf = fraction * weight + self.d_sumw += sf + self.d_sumw2 += sf * weight + self.d_sumwx += sf * x + self.d_sumwx2 += sf * x**2 + self.d_sumwy += sf * y + self.d_sumwy2 += sf * y**2 + self.d_sumwxy += sf * x * y + self.d_numentries += fraction + + def set_bin(self, bin): + self.d_sumw = bin.sumW() + self.d_sumw2 = bin.sumW2() + self.d_sumwx = bin.sumWX() + self.d_sumwx2 = bin.sumWX2() + self.d_sumwy = bin.sumWY() + self.d_sumwy2 = bin.sumWY2() + self.d_sumwxy = bin.sumWXY() + self.d_numentries = bin.numEntries() + + def set( + self, + numEntries: float, + sumW: List[float], + sumW2: List[float], + sumWcross: List[float], + ): + assert len(sumW) == 3 + assert len(sumW2) == 3 + assert len(sumWcross) == 1 + self.d_sumw = sumW[0] + self.d_sumw2 = sumW2[0] + self.d_sumwx = sumW[1] + self.d_sumwx2 = sumW2[1] + self.d_sumwy = sumW[2] + self.d_sumwy2 = sumW2[2] + self.d_sumwxy = sumWcross[0] + self.d_numentries = numEntries + + def sumW(self): + return self.d_sumw + + def sumW2(self): + return self.d_sumw2 + + def sumWX(self): + return self.d_sumwx + + def sumWX2(self): + return self.d_sumwx2 + + def sumWY(self): + return self.d_sumwy + + def sumWY2(self): + return self.d_sumwy2 + + def sumWXY(self): + return self.d_sumwxy + + def crossTerm(self, x, y): + assert (x == 0 and y == 1) or (x == 1 and y == 0) + return self.sumWXY() + + def numEntries(self): + return self.d_numentries + + def to_string(self) -> str: + return ( + f"{self.d_sumw:.6e}\t{self.d_sumw2:.6e}\t{self.d_sumwx:.6e}\t{self.d_sumwx2:.6e}\t" + f"{self.d_sumwy:.6e}\t{self.d_sumwy2:.6e}\t{self.d_sumwxy:.6e}\t{self.d_numentries:.6e}" + ) + + @classmethod + def from_string(cls, line: str) -> "GROGU_HISTO2D_V3.Bin": + values = re.split(r"\s+", line.strip()) + sumw, sumw2, sumwx, sumwx2, sumwy, sumwy2, sumwxy, numEntries = map( + float, values + ) + return cls(sumw, sumw2, sumwx, sumwx2, sumwy, sumwy2, sumwxy, numEntries) + + d_bins: List[Bin] = field(default_factory=list) + d_edges: List[List[float]] = field(default_factory=list) + + def __post_init__(self): + self.d_type = "Histo2D" + + # + # YODA compatibilty code + # + + def xEdges(self): + return self.d_edges[0] + + def yEdges(self): + return self.d_edges[1] + + def fill(self, x, y, weight=1.0, fraction=1.0): + for i, b in enumerate(self.bins()): + if ( + self.xEdges()[i] <= x < self.xEdges()[i + 1] + and self.yEdges()[i] <= y < self.yEdges()[i + 1] + ): + b.fill(x, y, weight, fraction) + return # should be done here + raise ValueError( + f"fill({x}, {y}, ...) out of range, Overflow not implemented yet" + ) + if x >= self.xMax() and self.d_overflow is not None: + self.d_overflow.fill(x, y, weight, fraction) + if x < self.xMin() and self.d_underflow is not None: + self.d_underflow.fill(x, y, weight, fraction) + + def xMax(self): + assert max(self.xEdges()) == self.xEdges()[-1], "xMax is not the last edge" + return self.xEdges()[-1] + + def xMin(self): + assert min(self.xEdges()) == self.xEdges()[0], "xMin is not the first edge" + return self.xEdges()[0] + + def yMax(self): + assert max(self.yEdges()) == self.yEdges()[-1], "yMax is not the last edge" + return self.yEdges()[-1] + + def yMin(self): + assert min(self.yEdges()) == self.yEdges()[0], "yMin is not the first edge" + return self.yEdges()[0] + + def bins(self, includeFlows=False): + if includeFlows: + return self.d_bins + # TODO think of a smart way to map here + return self.d_bins[1:-1] + + def to_string(self) -> str: + """Convert a YODA_HISTO2D_V3 object to a formatted string.""" + header = ( + f"BEGIN YODA_HISTO2D_V3 {self.d_key}\n" + f"Path: {self.d_path}\n" + f"Title: {self.d_title}\n" + f"Type: {self.d_type}\n" + f"---\n" + ) + + # TODO stats + stats = "" + # stats= ( + # f"# Mean: {self.mean()}\n" + # f"# Area: {self.area()}\n" + # ) + + legend = ( + "# sumw\t sumw2\t sumwx\t sumwx2\t sumwy\t sumwy2\t sumwxy\t numEntries\n" + ) + bin_data = "\n".join(b.to_string() for b in self.d_bins) + footer = "END YODA_HISTO2D_V3\n" + + return f"{header}{stats}{legend}{bin_data}{footer}" + + @classmethod + def from_string(cls, file_content: str, key: str = "") -> "GROGU_HISTO2D_V3": + lines = file_content.strip().splitlines() + + # Extract metadata (path, title) + path = "" + title = "" + for line in lines: + if line.startswith("Path:"): + path = line.split(":")[1].strip() + elif line.startswith("Title:"): + title = line.split(":")[1].strip() + elif line.startswith("---"): + break + + # Extract bins and overflow/underflow + bins = [] + edges = [] + data_section_started = False + + for line in lines: + if line.startswith("#"): + continue + if line.startswith("---"): + data_section_started = True + continue + if not data_section_started: + continue + + if line.startswith("Edges"): + content = re.findall(r"\[(.*?)\]", line) + numbers_as_strings = re.findall( + r"[-+]?\d*\.\d+e[+-]?\d+|\d+", content[0] + ) + edges += [[float(i) for i in numbers_as_strings]] + continue + + bins.append(GROGU_HISTO2D_V3.Bin.from_string(line)) + + # Create and return the YODA_HISTO1D_V2 object + return GROGU_HISTO2D_V3( + d_key=key, + d_path=path, + d_title=title, + d_bins=bins, + d_edges=edges, + ) diff --git a/src/babyyoda/grogu/read.py b/src/babyyoda/grogu/read.py index 73a3829..16aaf3e 100644 --- a/src/babyyoda/grogu/read.py +++ b/src/babyyoda/grogu/read.py @@ -3,6 +3,7 @@ from babyyoda.grogu.histo1d_v2 import GROGU_HISTO1D_V2 from babyyoda.grogu.histo1d_v3 import GROGU_HISTO1D_V3 from babyyoda.grogu.histo2d_v2 import GROGU_HISTO2D_V2 +from babyyoda.grogu.histo2d_v3 import GROGU_HISTO2D_V3 def read(file_path: str): @@ -24,6 +25,9 @@ def read(file_path: str): elif hist_type == "YODA_HISTO2D_V2": hist = GROGU_HISTO2D_V2.from_string(body, name) histograms[name] = hist + elif hist_type == "YODA_HISTO2D_V3": + hist = GROGU_HISTO2D_V3.from_string(body, name) + histograms[name] = hist else: # Add other parsing logic for different types if necessary print(f"Unknown type: {hist_type}, skipping...") diff --git a/src/babyyoda/histo1D.py b/src/babyyoda/histo1D.py index 3d96cf1..84df3fb 100644 --- a/src/babyyoda/histo1D.py +++ b/src/babyyoda/histo1D.py @@ -176,7 +176,8 @@ def axes(self): @property def kind(self): - return "MEAN" + # TODO reeavaluate this + return "COUNT" def counts(self): return np.array([b.numEntries() for b in self.bins()]) diff --git a/src/babyyoda/histo2D.py b/src/babyyoda/histo2D.py index cc807f8..6531a50 100644 --- a/src/babyyoda/histo2D.py +++ b/src/babyyoda/histo2D.py @@ -1,13 +1,32 @@ import sys import numpy as np +import babyyoda from babyyoda.util import loc, overflow, rebin, underflow class Histo2D: - def __init__(self, target): + def __init__(self, *args, backend=None, **kwargs): """ target is either a yoda or grogu HISTO2D_V2 """ + if len(args) == 1: + target = args[0] + # Store the target object where calls and attributes will be forwarded + else: + # Pick faster backend if possible + if backend is None: + try: + import yoda + + backend = yoda.Histo2D + except ImportError: + backend = babyyoda.grogu.Histo2D_v3 + target = backend(*args, **kwargs) + + # unwrap target + while isinstance(target, Histo2D): + target = target.target + # Store the target object where calls and attributes will be forwarded super().__setattr__("target", target) @@ -50,12 +69,17 @@ def __call__(self, *args, **kwargs): def clone(self): return Histo2D(self.target.clone()) - def bins(self): + def bins(self, *args, **kwargs): # fix order - return np.array(sorted(self.target.bins(), key=lambda b: (b.xMin(), b.yMin()))) + return self.target.bins(*args, **kwargs) + return np.array( + sorted( + self.target.bins(*args, **kwargs), key=lambda b: (b.xMin(), b.yMin()) + ) + ) - def bin(self, *indices): - return self.bins()[indices] + # def bin(self, *indices): + # return self.bins()[indices] # def overflow(self): # # This is a YODA-1 feature that is not present in YODA-2 @@ -100,26 +124,34 @@ def axes(self): @property def kind(self): + # TODO reeavaluate this return "COUNT" def values(self): - return self.sumWs().reshape((len(self.axes[0]), len(self.axes[1]))) + return self.sumWs().reshape((len(self.axes[1]), len(self.axes[0]))).T def variances(self): - return np.array([b.sumW2() for b in self.bins()]).reshape( - (len(self.axes[0]), len(self.axes[1])) + return ( + np.array([b.sumW2() for b in self.bins()]) + .reshape((len(self.axes[1]), len(self.axes[0]))) + .T ) def counts(self): - return np.array([b.numEntries() for b in self.bins()]).reshape( - (len(self.axes[0]), len(self.axes[1])) + return ( + np.array([b.numEntries() for b in self.bins()]) + .reshape((len(self.axes[1]), len(self.axes[0]))) + .T ) def __single_index(self, ix, iy): - return ix * len(self.axes[1]) + iy + return iy * len(self.axes[0]) + ix + # return ix * len(self.axes[1]) + iy def __get_by_indices(self, ix, iy): - return self.bin(self.__single_index(ix, iy)) + return self.bins()[ + self.__single_index(ix, iy) + ] # THIS is the fault with/without overflows! def __get_index_by_loc(self, loc, bins): # find the index in bin where loc is @@ -217,8 +249,10 @@ def plot(self, *args, **kwargs): saved_values = self.values def temp_values(): - return np.array([b.sumW() / b.dVol() for b in self.bins()]).reshape( - (len(self.axes[0]), len(self.axes[1])) + return ( + np.array([b.sumW() / b.dVol() for b in self.bins()]) + .reshape((len(self.axes[1]), len(self.axes[0]))) + .T ) self.values = temp_values diff --git a/src/babyyoda/test.py b/src/babyyoda/test.py index 1c5ca93..357d112 100644 --- a/src/babyyoda/test.py +++ b/src/babyyoda/test.py @@ -85,7 +85,7 @@ def assert_bin2d(gb, yb): def assert_value2d(gb, yb): assert gb.sumW() == yb.sumW() assert gb.sumW2() == yb.sumW2() - assert gb.sumWX() == yb.sumWX() + assert gb.sumWX() == yb.sumWX(), f"{gb.sumWX()} != {yb.sumWX()}" assert gb.sumWX2() == yb.sumWX2() assert gb.sumWY() == yb.sumWY() assert gb.sumWY2() == yb.sumWY2() diff --git a/tests/babyyoda/test_read.py b/tests/babyyoda/test_read.py index da67693..8a3b981 100644 --- a/tests/babyyoda/test_read.py +++ b/tests/babyyoda/test_read.py @@ -5,3 +5,18 @@ def test_read_histo1d_v2(): hists = babyyoda.read("tests/test_histo1d_v2.yoda") assert len(hists) == 1 + + +def test_read_histo1d_v3(): + hists = babyyoda.read("tests/test_histo1d_v3.yoda") + assert len(hists) == 1 + + +def test_read_histo2d_v2(): + hists = babyyoda.read("tests/test_histo2d_v2.yoda") + assert len(hists) == 1 + + +def test_read_histo2d_v3(): + hists = babyyoda.read("tests/test_histo2d_v3.yoda") + assert len(hists) == 1 diff --git a/tests/grogu/test_gg_read.py b/tests/grogu/test_gg_read.py index e8659ee..19324fb 100644 --- a/tests/grogu/test_gg_read.py +++ b/tests/grogu/test_gg_read.py @@ -14,3 +14,8 @@ def test_gg_read_histo1d_v3(): def test_gg_read_histo2d_v2(): hists = read("tests/test_histo2d_v2.yoda") assert len(hists) == 1 + + +def test_gg_read_histo2d_v3(): + hists = read("tests/test_histo2d_v3.yoda") + assert len(hists) == 1 diff --git a/tests/test_histo2d_v3.yoda b/tests/test_histo2d_v3.yoda new file mode 100644 index 0000000..d26244f --- /dev/null +++ b/tests/test_histo2d_v3.yoda @@ -0,0 +1,2716 @@ +BEGIN YODA_HISTO2D_V3 /ALICE_2020_I1797621/GGPdfXQ +Path: /ALICE_2020_I1797621/GGPdfXQ +ScaledBy: 1.00000000000000002e-02 +Title: ~ +Type: Histo2D +--- +# Mean: (5.833908e-03, 2.987324e+00) +# Integral: 1.765968e+08 +Edges(A1): [1.000000e-06, 1.318257e-06, 1.737801e-06, 2.290868e-06, 3.019952e-06, 3.981072e-06, 5.248075e-06, 6.918310e-06, 9.120108e-06, 1.202264e-05, 1.584893e-05, 2.089296e-05, 2.754229e-05, 3.630781e-05, 4.786301e-05, 6.309573e-05, 8.317638e-05, 1.096478e-04, 1.445440e-04, 1.905461e-04, 2.511886e-04, 3.311311e-04, 4.365158e-04, 5.754399e-04, 7.585776e-04, 1.000000e-03, 1.318257e-03, 1.737801e-03, 2.290868e-03, 3.019952e-03, 3.981072e-03, 5.248075e-03, 6.918310e-03, 9.120108e-03, 1.202264e-02, 1.584893e-02, 2.089296e-02, 2.754229e-02, 3.630781e-02, 4.786301e-02, 6.309573e-02, 8.317638e-02, 1.096478e-01, 1.445440e-01, 1.905461e-01, 2.511886e-01, 3.311311e-01, 4.365158e-01, 5.754399e-01, 7.585776e-01, 1.000000e+00] +Edges(A2): [0.000000e+00, 6.000000e-01, 1.200000e+00, 1.800000e+00, 2.400000e+00, 3.000000e+00, 3.600000e+00, 4.200000e+00, 4.800000e+00, 5.400000e+00, 6.000000e+00, 6.600000e+00, 7.200000e+00, 7.800000e+00, 8.400000e+00, 9.000000e+00, 9.600000e+00, 1.020000e+01, 1.080000e+01, 1.140000e+01, 1.200000e+01, 1.260000e+01, 1.320000e+01, 1.380000e+01, 1.440000e+01, 1.500000e+01, 1.560000e+01, 1.620000e+01, 1.680000e+01, 1.740000e+01, 1.800000e+01, 1.860000e+01, 1.920000e+01, 1.980000e+01, 2.040000e+01, 2.100000e+01, 2.160000e+01, 2.220000e+01, 2.280000e+01, 2.340000e+01, 2.400000e+01, 2.460000e+01, 2.520000e+01, 2.580000e+01, 2.640000e+01, 2.700000e+01, 2.760000e+01, 2.820000e+01, 2.880000e+01, 2.940000e+01, 3.000000e+01] +# sumW sumW2 sumW(A1) sumW2(A1) sumW(A2) sumW2(A2) sumW(A1,A2) numEntries +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 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+0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 +END YODA_HISTO2D_V3 diff --git a/tests/yoda/test_yoda_vs_grogu.py b/tests/yoda/test_yoda_vs_grogu.py index 33353e0..2ebf35f 100644 --- a/tests/yoda/test_yoda_vs_grogu.py +++ b/tests/yoda/test_yoda_vs_grogu.py @@ -22,11 +22,12 @@ def test_ao(): assert_ao(gh2, yh2) -def test_histo1(): +def test_histo1d_v2(): gh1 = next(iter(by.read_grogu("tests/test_histo1d_v2.yoda").values())) yh1 = next(iter(by.read_yoda("tests/test_histo1d_v2.yoda").values())) assert len(gh1.bins()) == len(yh1.bins()) + assert all(x == y for x, y in zip(gh1.xEdges(), yh1.xEdges())) for gb, yb in zip(gh1.bins(), yh1.bins()): assert gb.xMin() == yb.xMin() @@ -36,18 +37,37 @@ def test_histo1(): assert gb.sumWX() == yb.sumWX() assert gb.sumWX2() == yb.sumWX2() assert gb.numEntries() == yb.numEntries() - # TODO test overflow and underflow -def test_histo2(): +def test_histo1d_v3(): + gh1 = next(iter(by.read_grogu("tests/test_histo1d_v3.yoda").values())) + yh1 = next(iter(by.read_yoda("tests/test_histo1d_v3.yoda").values())) + + assert len(gh1.bins()) == len(yh1.bins()) + assert len(gh1.bins(True)) == len(yh1.bins(True)) + assert all( + x == y for x, y in zip(gh1.xEdges(), yh1.xEdges()) + ), f"{gh1.xEdges()} != {yh1.xEdges()}" + + for gb, yb in zip(gh1.bins(), yh1.bins()): + assert gb.sumW() == yb.sumW() + assert gb.sumW2() == yb.sumW2() + assert gb.sumWX() == yb.sumWX() + assert gb.sumWX2() == yb.sumWX2() + assert gb.numEntries() == yb.numEntries() + + +def test_histo2d_v2(): gh2 = next(iter(by.read_grogu("tests/test_histo2d_v2.yoda").values())) yh2 = next(iter(by.read_yoda("tests/test_histo2d_v2.yoda").values())) assert len(gh2.bins()) == len(yh2.bins()) + assert all(x == y for x, y in zip(gh2.xEdges(), yh2.xEdges())) + assert all(x == y for x, y in zip(gh2.yEdges(), yh2.yEdges())) for i, (gb, yb) in enumerate(zip(gh2.bins(), yh2.bins())): - assert gb.xMin() == yb.xMin() + assert gb.xMin() == yb.xMin(), f"at index {i}" assert gb.xMax() == yb.xMax() assert gb.yMin() == yb.yMin() assert gb.yMax() == yb.yMax() @@ -59,6 +79,24 @@ def test_histo2(): # TODO test overflow and underflow +def test_histo2d_v3(): + gh2 = next(iter(by.read_grogu("tests/test_histo2d_v3.yoda").values())) + yh2 = next(iter(by.read_yoda("tests/test_histo2d_v3.yoda").values())) + + assert all( + x == y for x, y in zip(gh2.xEdges(), yh2.xEdges()) + ), f"{gh2.xEdges()} != {yh2.xEdges()}" + assert all( + x == y for x, y in zip(gh2.yEdges(), yh2.yEdges()) + ), f"{gh2.yEdges()} != {yh2.yEdges()}" + assert len(gh2.bins(True)) == len(yh2.bins(True)) + + for i, (gb, yb) in enumerate(zip(gh2.bins(True), yh2.bins(True))): + assert gb.sumW() == yb.sumW(), f"at index {i}" + assert gb.sumW2() == yb.sumW2() + assert gb.numEntries() == yb.numEntries() + + def test_create_histo1d(): import yoda diff --git a/tests/yoda/uhi/test_yd_histo2d_access.py b/tests/yoda/uhi/test_yd_histo2d_access.py index 738cea1..b8e5701 100644 --- a/tests/yoda/uhi/test_yd_histo2d_access.py +++ b/tests/yoda/uhi/test_yd_histo2d_access.py @@ -1,7 +1,7 @@ import pytest from babyyoda.histo2D import Histo2D from babyyoda.grogu.histo2d_v2 import GROGU_HISTO2D_V2 -from babyyoda.test import assert_bin2d +from babyyoda.test import assert_value2d from babyyoda.util import loc pytest.importorskip("yoda") @@ -35,15 +35,21 @@ def create_histo2d(): def test_access_index(): h, g = create_histo2d() - assert_bin2d(h[1, 0], h.bin(10)) - assert_bin2d(h[0, 2], h.bin(2)) + assert_value2d(h[1, 0], h.bin(10)) + assert_value2d(h[0, 2], h.bin(2)) - assert_bin2d(g[1, 0], g.bin(10)) - assert_bin2d(g[0, 2], g.bin(2)) + assert_value2d(g[1, 0], g.bin(10)) + assert_value2d(g[0, 2], g.bin(2)) def test_access_loc(): h, g = create_histo2d() - assert_bin2d(h[loc(3), loc(4)], h.binAt(3, 4)) - assert_bin2d(g[loc(3), loc(4)], g.binAt(3, 4)) + ( + assert_value2d(h[loc(3), loc(4)], h.binAt(3, 4)), + f"{h[loc(3),loc(4)]} != {h.binAt(3, 4)}", + ) + ( + assert_value2d(g[loc(3), loc(4)], g.binAt(3, 4)), + f"{g[loc(3),loc(4)]} != {g.binAt(3, 4)}", + )