{ "cells": [ { "cell_type": "markdown", "id": "fcca5058", "metadata": {}, "source": [ "# Result writing" ] }, { "cell_type": "code", "execution_count": 1, "id": "b583970e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2.9.7\n", "~/git/resummino_releases\n" ] } ], "source": [ "import hepi\n", "print(hepi.__version__)\n", "import smpl\n", "import numpy as np\n", "from hepi .run import resummino as rs\n", "import hepi.util as util\n", "import matplotlib.pyplot as plt\n", "rs.set_path(\"resummino\")\n", "print (rs.get_path())" ] }, { "cell_type": "code", "execution_count": 2, "id": "7d048757", "metadata": {}, "outputs": [], "source": [ "params = [\n", " \"mastercode_with_gm2.in\",\n", "]\n", "pss = [ \n", " (1000011,-1000011),\n", " ]" ] }, { "cell_type": "markdown", "id": "2dd526dd", "metadata": {}, "source": [ "## Print" ] }, { "cell_type": "code", "execution_count": 3, "id": "ed7216a4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/hepi/util.py:101: UserWarning: LHAPDF python binding not installed? Make sure you set PYTHONPATH correctly (i.e. correct python version) if you want to compute PDF uncertainties.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f1f3830644e4980a8de75c90824964d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | Checking input: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3b010a0c1eb54ebc997faa4a8b35f2f4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "PROCESSING TASKS | Checking input: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "48436c1b2f5d412d85461a4638a3c379", "version_major": 2, "version_minor": 0 }, "text/plain": [ "COLLECTING RESULTS | Checking input: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2684ff6bff224b9fa4486104deeda4d1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | Preparing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "db2e6b2df49b49549d9c4595d1b59f0b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "PROCESSING TASKS | Preparing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "57cf75179a2a40acb603a15a367ef590", "version_major": 2, "version_minor": 0 }, "text/plain": [ "COLLECTING RESULTS | Preparing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Skipped: 9 Not skipped: 0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a32736fd91044e5b9091eac7e0eb697a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | Running: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "01b3560a0bdb443d842df8b04d16006e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "PROCESSING TASKS | Running: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a255f125450742daacf1148750c791ab", "version_major": 2, "version_minor": 0 }, "text/plain": [ "COLLECTING RESULTS | Running: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9ca87a40a1ef425cb5186582173229b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | Parsing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2a13c3e292c42e6b1274f0d69684d78", "version_major": 2, "version_minor": 0 }, "text/plain": [ "PROCESSING TASKS | Parsing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8d05054331b44cfba939db2b5bd904aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ "COLLECTING RESULTS | Parsing: 0%| | 0/9 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " LO NLO NLO_PLUS_NLL \n", "0 0.20208+/-0.00029 0.2679+/-0.0004 0.0+/-0 \\\n", "1 0.013713+/-0.000018 0.017043+/-0.000020 0.0+/-0 \n", "2 0.0026091+/-0.0000031 0.0031281+/-0.0000035 0.0+/-0 \n", "3 0.0007347+/-0.0000008 0.0008593+/-0.0000009 0.0+/-0 \n", "4 0.00025475+/-0.00000028 0.00029242+/-0.00000029 0.0+/-0 \n", "5 0.00010047+/-0.00000011 0.00011369+/-0.00000011 0.0+/-0 \n", "6 (4.322+/-0.004)e-05 (4.841+/-0.005)e-05 0.0+/-0 \n", "7 (1.9790+/-0.0020)e-05 (2.2010+/-0.0020)e-05 0.0+/-0 \n", "8 (9.489+/-0.009)e-06 (1.0515+/-0.0009)e-05 0.0+/-0 \n", "\n", " aNNLO_PLUS_NNLL K_LO \n", "0 None 1.00000000000000000000+/-0.00000000000000000026 \\\n", "1 None 1.0+/-0 \n", "2 None 1.0+/-0 \n", "3 None 1.0+/-0 \n", "4 None 1.00000000000000000000+/-0.00000000000000000013 \n", "5 None 1.0+/-0 \n", "6 None 1.0+/-0 \n", "7 None 1.0+/-0 \n", "8 None 1.0+/-0 \n", "\n", " K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL \n", "0 1.3255+/-0.0026 0.0+/-0 0.0+/-0 None \\\n", "1 1.2429+/-0.0022 0.0+/-0 0.0+/-0 None \n", "2 1.1989+/-0.0020 0.0+/-0 0.0+/-0 None \n", "3 1.1697+/-0.0018 0.0+/-0 0.0+/-0 None \n", "4 1.1479+/-0.0017 0.0+/-0 0.0+/-0 None \n", "5 1.1316+/-0.0016 0.0+/-0 0.0+/-0 None \n", "6 1.1199+/-0.0016 0.0+/-0 0.0+/-0 None \n", "7 1.1122+/-0.0015 0.0+/-0 0.0+/-0 None \n", "8 1.1081+/-0.0015 0.0+/-0 0.0+/-0 None \n", "\n", " aNNLO_PLUS_NNLL_OVER_NLO ... precision max_iters invariant_mass pt \n", "0 None ... 0.01 50 auto auto \\\n", "1 None ... 0.01 50 auto auto \n", "2 None ... 0.01 50 auto auto \n", "3 None ... 0.01 50 auto auto \n", "4 None ... 0.01 50 auto auto \n", "5 None ... 0.01 50 auto auto \n", "6 None ... 0.01 50 auto auto \n", "7 None ... 0.01 50 auto auto \n", "8 None ... 0.01 50 auto auto \n", "\n", " result id model mu mass_1000011 runner \n", "0 total 5 100.0 100.0 ResumminoRunner-resummino git-latest \n", "1 total 5 212.5 212.5 ResumminoRunner-resummino git-latest \n", "2 total 5 325.0 325.0 ResumminoRunner-resummino git-latest \n", "3 total 5 437.5 437.5 ResumminoRunner-resummino git-latest \n", "4 total 5 550.0 550.0 ResumminoRunner-resummino git-latest \n", "5 total 5 662.5 662.5 ResumminoRunner-resummino git-latest \n", "6 total 5 775.0 775.0 ResumminoRunner-resummino git-latest \n", "7 total 5 887.5 887.5 ResumminoRunner-resummino git-latest \n", "8 total 5 1000.0 1000.0 ResumminoRunner-resummino git-latest \n", "\n", "[9 rows x 41 columns]\n" ] } ], "source": [ "for pa,pb in pss:\n", " for param in params:\n", " i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,id=\"5\")\n", " #i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"CT14lo\",\"CT14lo\",1., 1.,model_path=model_path,id=\"5\")\n", " li = [i]\n", " li = hepi.mass_scan([i],pa, np.linspace(100,1000,9))\n", " rs_dl = rs.run(li,noskip=False)\n", " print(rs_dl)" ] }, { "cell_type": "markdown", "id": "6ffe4795", "metadata": {}, "source": [ "## Pandas" ] }, { "cell_type": "code", "execution_count": 4, "id": "7dedc960", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | LO | \n", "NLO | \n", "NLO_PLUS_NLL | \n", "aNNLO_PLUS_NNLL | \n", "K_LO | \n", "K_NLO | \n", "K_NLO_PLUS_NLL | \n", "NLO_PLUS_NLL_OVER_NLO | \n", "K_aNNLO_PLUS_NNLL | \n", "aNNLO_PLUS_NNLL_OVER_NLO | \n", "... | \n", "precision | \n", "max_iters | \n", "invariant_mass | \n", "pt | \n", "result | \n", "id | \n", "model | \n", "mu | \n", "mass_1000011 | \n", "runner | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "0.20208+/-0.00029 | \n", "0.2679+/-0.0004 | \n", "0.0+/-0 | \n", "None | \n", "1.00000000000000000000+/-0.00000000000000000026 | \n", "1.3255+/-0.0026 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 100.0 | \n", "100.0 | \n", "ResumminoRunner-resummino git-latest | \n", "
1 | \n", "0.013713+/-0.000018 | \n", "0.017043+/-0.000020 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.2429+/-0.0022 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 212.5 | \n", "212.5 | \n", "ResumminoRunner-resummino git-latest | \n", "
2 | \n", "0.0026091+/-0.0000031 | \n", "0.0031281+/-0.0000035 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1989+/-0.0020 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 325.0 | \n", "325.0 | \n", "ResumminoRunner-resummino git-latest | \n", "
3 | \n", "0.0007347+/-0.0000008 | \n", "0.0008593+/-0.0000009 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1697+/-0.0018 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 437.5 | \n", "437.5 | \n", "ResumminoRunner-resummino git-latest | \n", "
4 | \n", "0.00025475+/-0.00000028 | \n", "0.00029242+/-0.00000029 | \n", "0.0+/-0 | \n", "None | \n", "1.00000000000000000000+/-0.00000000000000000013 | \n", "1.1479+/-0.0017 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 550.0 | \n", "550.0 | \n", "ResumminoRunner-resummino git-latest | \n", "
5 | \n", "0.00010047+/-0.00000011 | \n", "0.00011369+/-0.00000011 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1316+/-0.0016 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 662.5 | \n", "662.5 | \n", "ResumminoRunner-resummino git-latest | \n", "
6 | \n", "(4.322+/-0.004)e-05 | \n", "(4.841+/-0.005)e-05 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1199+/-0.0016 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 775.0 | \n", "775.0 | \n", "ResumminoRunner-resummino git-latest | \n", "
7 | \n", "(1.9790+/-0.0020)e-05 | \n", "(2.2010+/-0.0020)e-05 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1122+/-0.0015 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 887.5 | \n", "887.5 | \n", "ResumminoRunner-resummino git-latest | \n", "
8 | \n", "(9.489+/-0.009)e-06 | \n", "(1.0515+/-0.0009)e-05 | \n", "0.0+/-0 | \n", "None | \n", "1.0+/-0 | \n", "1.1081+/-0.0015 | \n", "0.0+/-0 | \n", "0.0+/-0 | \n", "None | \n", "None | \n", "... | \n", "0.01 | \n", "50 | \n", "auto | \n", "auto | \n", "total | \n", "5 | \n", "\n", " | 1000.0 | \n", "1000.0 | \n", "ResumminoRunner-resummino git-latest | \n", "
9 rows × 41 columns
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