{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2024-05-27T20:59:20.556391Z", "start_time": "2024-05-27T20:59:13.428959Z" } }, "source": [ "from pprint import pprint\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import numpy as np\n", "\n", "\n", "import hoho\n", "from hoho import compute_WED\n", "from hoho import vis\n", "from hoho.vis import line\n", "\n", "from scipy.spatial.distance import cdist\n", "import cv2\n", "hoho.LOCAL_DATADIR = hoho.setup(\"../data\")\n", "from handcrafted_solution import predict\n", "from tqdm import tqdm" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using ..\\data as the data directory (we are running locally)\n" ] } ], "execution_count": 1 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "# from ..s23dr-hoho-competition import handcrafted_solution", "id": "4e9860e9763942ac" }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "", "id": "679a2b4f8e46a308" }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T20:29:23.028196Z", "start_time": "2024-05-26T20:29:23.024659Z" } }, "cell_type": "code", "source": "", "id": "1146933b5aabff1c", "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:09:33.905658Z", "start_time": "2024-05-26T22:09:33.901147Z" } }, "cell_type": "code", "source": [ "split = \"val\"\n", "\n", "dataset = hoho.get_dataset(decode=None, split=split)\n", "import webdataset as wds\n", "\n", "targets = wds.WebDataset(\"../data/val/targets/hoho_v3_025-of-032.tar.gz\")" ], "id": "55c712587c694e96", "outputs": [], "execution_count": 6 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "", "id": "7daaea23230c7bb6" }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:54:26.635329Z", "start_time": "2024-05-26T21:54:19.176110Z" } }, "cell_type": "code", "source": [ "\n", "\n", "for i, sample in tqdm(enumerate(dataset)):\n", " # if i > 170:\n", " predict(sample, visualize=False, point_radius=40, max_angle=5)" ], "id": "f36ee7b8f0427f72", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2it [00:02, 1.25s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Not enough vertices or connections in image 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4it [00:07, 1.85s/it]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", "Cell \u001B[1;32mIn[4], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, sample \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28menumerate\u001B[39m(dataset)):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# if i > 170:\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m predict(sample, visualize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, point_radius\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m40\u001B[39m, max_angle\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m)\n", "File \u001B[1;32m~\\edu\\sem6\\cv\\huggingface\\s23dr-hoho-competition\\handcrafted_solution.py:354\u001B[0m, in \u001B[0;36mpredict\u001B[1;34m(entry, visualize, **kwargs)\u001B[0m\n\u001B[0;32m 352\u001B[0m gest_seg_np \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(gest_seg)\u001B[38;5;241m.\u001B[39mastype(np\u001B[38;5;241m.\u001B[39muint8)\n\u001B[0;32m 353\u001B[0m \u001B[38;5;66;03m# Metric3D\u001B[39;00m\n\u001B[1;32m--> 354\u001B[0m depth_np \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(depth) \u001B[38;5;241m/\u001B[39m \u001B[38;5;241m2.5\u001B[39m \u001B[38;5;66;03m# 2.5 is the scale estimation coefficient\u001B[39;00m\n\u001B[0;32m 355\u001B[0m vertices, connections \u001B[38;5;241m=\u001B[39m get_vertices_and_edges_from_segmentation(gest_seg_np, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 356\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\u001B[38;5;28mlen\u001B[39m(vertices) \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m2\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\u001B[38;5;28mlen\u001B[39m(connections) \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m1\u001B[39m):\n", "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\PIL\\Image.py:681\u001B[0m, in \u001B[0;36mImage.__array_interface__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 679\u001B[0m new[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtobytes(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mL\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 680\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 681\u001B[0m new[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtobytes()\n\u001B[0;32m 682\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 683\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e, (\u001B[38;5;167;01mMemoryError\u001B[39;00m, \u001B[38;5;167;01mRecursionError\u001B[39;00m)):\n", "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\PIL\\Image.py:740\u001B[0m, in \u001B[0;36mImage.tobytes\u001B[1;34m(self, encoder_name, *args)\u001B[0m\n\u001B[0;32m 737\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m encoder_name \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m args \u001B[38;5;241m==\u001B[39m ():\n\u001B[0;32m 738\u001B[0m args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmode\n\u001B[1;32m--> 740\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mload()\n\u001B[0;32m 742\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mwidth \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mheight \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m 743\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m\n", "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\PIL\\ImageFile.py:291\u001B[0m, in \u001B[0;36mImageFile.load\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 288\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m(msg)\n\u001B[0;32m 290\u001B[0m b \u001B[38;5;241m=\u001B[39m b \u001B[38;5;241m+\u001B[39m s\n\u001B[1;32m--> 291\u001B[0m n, err_code \u001B[38;5;241m=\u001B[39m decoder\u001B[38;5;241m.\u001B[39mdecode(b)\n\u001B[0;32m 292\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m n \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m 293\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", "\u001B[1;31mKeyboardInterrupt\u001B[0m: " ] } ], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:09:23.089424Z", "start_time": "2024-05-26T22:07:45.112072Z" } }, "cell_type": "code", "source": [ "from pathlib import Path\n", "\n", "solution = []\n", "from concurrent.futures import ProcessPoolExecutor\n", "with ProcessPoolExecutor(max_workers=11) as pool:\n", " results = []\n", " for i, sample in enumerate(tqdm(dataset)):\n", " results.append(pool.submit(predict, sample, visualize=False, point_radius=20, max_angle=5, extend=20))\n", "\n", " for i, result in enumerate(tqdm(results)):\n", " key, pred_vertices, pred_edges = result.result()\n", " solution.append({\n", " '__key__': key,\n", " 'wf_vertices': pred_vertices.tolist(),\n", " 'wf_edges': pred_edges\n", " })\n", " # if i % 100 == 0:\n", " # incrementally save the results in case we run out of time\n", " # print(f\"Processed {i} samples\")\n", " # save_submission(solution, Path(params['output_path']) / \"submission.parquet\") " ], "id": "c4ea239a996b87e6", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "346it [00:12, 28.11it/s] \n", "100%|██████████| 346/346 [01:24<00:00, 4.10it/s]\n" ] } ], "execution_count": 3 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:08:29.524770Z", "start_time": "2024-05-26T21:08:29.521449Z" } }, "cell_type": "code", "source": "from script import save_submission", "id": "b37a7973254c7706", "outputs": [], "execution_count": 13 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:08:30.661782Z", "start_time": "2024-05-26T21:08:30.546411Z" } }, "cell_type": "code", "source": [ "print('------------ Saving results ---------------')\n", "save_submission(solution, \"submission.parquet\")\n", "print(\"------------ Done ------------ \")\n" ], "id": "748cb844cc91e136", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------ Saving results ---------------\n", "Submission saved to submission.parquet\n", "------------ Done ------------ \n" ] } ], "execution_count": 14 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:03:46.261700Z", "start_time": "2024-05-26T21:03:46.092867Z" } }, "cell_type": "code", "source": "", "id": "f039ac3e98794019", "outputs": [ { "data": { "text/plain": [ "[{'__key__': 'ac398eacc02',\n", " 'wf_vertices': [[-656.7050599672618, -605.098754169388, 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[347.7062282319745, -270.4545534075031, 587.7216572289461],\n", " [445.8046156999212, 454.3629003526487, 503.14632335355816],\n", " [-456.84634853543037, -729.0402935378188, 362.5955846140709],\n", " [-515.1022471859739, -540.1013466222491, 391.3304980927534],\n", " [-606.2246562851407, -787.1882896060577, 251.25789963686262],\n", " [332.5070755892302, 401.45368076904174, 587.483004378154],\n", " [-1153.1849363695578, 366.8238983085173, 600.7481052528483],\n", " [-456.0222332921635, 306.5850166746998, 313.33029915243725],\n", " [-2051.1210994364446, 402.85927769794966, 296.062102046577],\n", " [-1254.8278982878473, 248.6501351112097, 346.9221726512117],\n", " [441.86568647210083, 151.23282068153662, 650.7893291943817],\n", " [441.48480272669246, -303.3560085903084, 511.1934757524319],\n", " [-396.61803793447575, 245.4105493619452, 343.9044781523911]],\n", " 'wf_edges': [(0, 1),\n", " (9, 10),\n", " (4, 0),\n", " (6, 8),\n", " (12, 13),\n", " (19, 16),\n", " (2, 3),\n", " (6, 7),\n", " 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[-1896.6955627088957, 74.20187122056655, 379.9724374910851],\n", " [-801.4819178203063, -487.0168430878406, 343.5196962865578],\n", " [-1045.63708956366, -451.16381449433027, 344.70034073436307],\n", " [-1745.6295823215883, -1447.6475467280743, 1169.818732263586],\n", " [-3452.480208623993, -1252.904883107523, 1115.5429002430776],\n", " [-797.3075416620355, -736.5770077123367, 498.9663367613991],\n", " [-1020.1332997754822, -647.1190636182381, 499.83047044961035],\n", " [-775.5560859086726, -869.0588480831124, 320.59956207135406],\n", " [-1930.6340841747337, 216.80298905863572, 405.67288671885166],\n", " [-2051.674336250897, 296.49444519204144, 314.9892082810337],\n", " [-1185.638046928896, -242.5421290831439, 473.9320080922606],\n", " [64.19423404028316, -371.74963991009645, 428.6367789354599],\n", " [-1352.839278640722, 161.4951858199986, 776.0212432435723],\n", " [2668.888671726199, 1821.150730906863, 1766.195729207637],\n", " [-1717.0707836361876, 113.33210611101254, 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'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (3, 3, 3), 'R': (3, 3, 3), 't': (3, 3), 'images': , 'points3d': , 'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (3, 3, 3), 'R': (3, 3, 3), 't': (3, 3), 'images': , 'points3d': , 'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (3, 3, 3), 'R': (3, 3, 3), 't': (3, 3), 'images': , 'points3d': , 'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (4, 3, 3), 'R': (4, 3, 3), 't': (4, 3), 'images': , 'points3d': , 'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (3, 3, 3), 'R': (3, 3, 3), 't': (3, 3), 'images': , 'points3d': , 'cameras': }\n", "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (3, 3, 3), 'R': (3, 3, 3), 't': (3, 3), 'images': , 'points3d': , 'cameras': }\n" ] }, { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[1;31mIndexError\u001B[0m Traceback (most recent call last)", "Cell \u001B[1;32mIn[19], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m*\u001B[39mhoho\u001B[38;5;241m.\u001B[39mget_dataset(decode\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, split\u001B[38;5;241m=\u001B[39msplit)]:\n\u001B[1;32m----> 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(i)\n", "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\hoho\\hoho.py:248\u001B[0m, in \u001B[0;36mSample.__repr__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 247\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__repr__\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 248\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mstr\u001B[39m({k: v\u001B[38;5;241m.\u001B[39mshape \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(v, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshape\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m [\u001B[38;5;28mtype\u001B[39m(v[\u001B[38;5;241m0\u001B[39m])] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(v, \u001B[38;5;28mlist\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(v) \u001B[38;5;28;01mfor\u001B[39;00m k,v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mitems()})\n", "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\hoho\\hoho.py:248\u001B[0m, in \u001B[0;36m\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m 247\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__repr__\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 248\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mstr\u001B[39m({k: v\u001B[38;5;241m.\u001B[39mshape \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(v, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshape\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m [\u001B[38;5;28mtype\u001B[39m(v[\u001B[38;5;241m0\u001B[39m])] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(v, \u001B[38;5;28mlist\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(v) \u001B[38;5;28;01mfor\u001B[39;00m k,v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mitems()})\n", "\u001B[1;31mIndexError\u001B[0m: list index out of range" ] } ], "execution_count": 19 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:14:27.365166Z", "start_time": "2024-05-26T21:14:27.360281Z" } }, "cell_type": "code", "source": "", "id": "f42a452f2b2f4dad", "outputs": [ { "data": { "text/plain": [ "345" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 21 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T21:16:23.529153Z", "start_time": "2024-05-26T21:16:23.495286Z" } }, "cell_type": "code", "source": [ "for i in hoho.get_dataset(decode=None, split=split):\n", " print(i)\n", " break" ], "id": "212ee677d51dfa21", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'__key__': , '__imagekey__': [], '__url__': , 'ade20k': [], 'depthcm': [], 'gestalt': [], 'K': (4, 3, 3), 'R': (4, 3, 3), 't': (4, 3), 'images': , 'points3d': , 'cameras': }\n" ] } ], "execution_count": 25 }, { "metadata": {}, "cell_type": "code", "source": "", "id": "580ba3192e54e5b", "outputs": [], "execution_count": null }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:00:51.350586Z", "start_time": "2024-05-26T22:00:51.345200Z" } }, "cell_type": "code", "source": "", "id": "f5606c5561673040", "outputs": [], "execution_count": 21 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:09:44.223177Z", "start_time": "2024-05-26T22:09:44.012204Z" } }, "cell_type": "code", "source": [ "import io\n", "solution_dict = {}\n", "for d in solution[:173]:\n", " solution_dict[d[\"__key__\"]] = d\n", "# for d in solution[173:]:\n", "# solution_dict[d[\"__key__\"]] = {\"wf_vertices\":[[0,100,0], [0,-100,0], [100,0,0], [-100,0,0]], \"wf_edges\": [(0, 2), (2, 1), (1, 3), (3, 0)]}\n", "WEDs = []\n", "\n", "for i in targets:\n", " try:\n", " j = solution_dict[i[\"__key__\"]]\n", " i = dict(np.load(io.BytesIO(i[\"wireframe.npz\"])))\n", " WEDs.append(hoho.compute_WED(j[\"wf_vertices\"], j[\"wf_edges\"], i[\"wf_vertices\"], i[\"wf_edges\"]))\n", " except KeyError:\n", " pass\n", " " ], "id": "825b2b42001620ed", "outputs": [], "execution_count": 7 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:03:51.284737Z", "start_time": "2024-05-26T22:03:51.278671Z" } }, "cell_type": "code", "source": "WEDs", "id": "3755a6f94a6cfee", "outputs": [ { "data": { "text/plain": [ "[2.676034058193981,\n", " 1.9350219092383154,\n", " 1.8641915552589836,\n", " 3.015960463447758,\n", " 2.6667933167949074,\n", " 1.232501046717596,\n", " 1.876056598343252,\n", " 3.231162116017275,\n", " 2.4075650268031743,\n", " 2.763288700454399,\n", " 2.4096106118660536,\n", " 2.766497985653837,\n", " 3.3857620156098456,\n", " 2.309454744250443,\n", " 2.338425158810963,\n", " 2.6979664103316043,\n", " 2.2129751544721206,\n", " 2.6537780479649222,\n", " 2.2122967796132564,\n", " 2.3715253041919144,\n", " 2.382578793019074,\n", " 2.664659634690259,\n", " 2.6368028763474283,\n", " 2.0494952355773743,\n", " 2.4238855485528115,\n", " 2.886649171133775,\n", " 2.533104053762428,\n", " 2.154532536609055,\n", " 2.0658467160149874,\n", " 2.756268408052827,\n", " 2.94621099259946,\n", " 2.779834796092198,\n", " 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"2024-05-26T22:09:52.941973Z", "start_time": "2024-05-26T22:09:52.440750Z" } }, "cell_type": "code", "source": [ "import scipy.stats as ss\n", "\n", "ss.describe(WEDs)" ], "id": "9fa1f52b8c1b08fe", "outputs": [ { "data": { "text/plain": [ "DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3922155425953786, variance=0.22032420003471148, skewness=0.14340784428616138, kurtosis=-0.5120992116516523)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 8 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-26T22:01:09.223414Z", "start_time": "2024-05-26T22:01:09.042800Z" } }, "cell_type": "code", "source": "plt.hist(WEDs)", "id": "752385cb053a2467", "outputs": [ { "data": { "text/plain": [ "(array([ 6., 12., 24., 37., 35., 17., 23., 10., 6., 3.]),\n", " array([1.2885549 , 1.54589969, 1.80324448, 2.06058926, 2.31793405,\n", " 2.57527884, 2.83262362, 3.08996841, 3.34731319, 3.60465798,\n", " 3.86200277]),\n", " )" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
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" 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" (10, 11),\n", " (19, 20),\n", " (12, 13),\n", " (14, 10),\n", " (22, 17),\n", " (22, 23),\n", " (2, 3),\n", " (15, 16),\n", " (21, 15),\n", " (8, 9),\n", " (5, 0),\n", " (5, 6),\n", " (7, 2),\n", " (24, 19),\n", " (17, 18),\n", " (12, 8)]},\n", " {'__key__': '2ec2db283cc',\n", " 'wf_vertices': [[-31027.364374321824, -5209.06382229566, 200.4667723141025],\n", " [-30961.626756250047, -5314.2163668281555, 190.92917915876092],\n", " [-28954.08267247521, -5207.31108437679, 208.59820790027072],\n", " [-29185.291210871394, -5281.769475747844, 198.34163711146394],\n", " [-28872.254342311484, -5048.088791628908, 239.38287061770427],\n", " [-28921.082722977237, -5089.921431295339, 226.27209665241782],\n", " [-30817.68707903639, -3806.291711671991, 242.02277771661085],\n", " [-30573.519361300438, -3721.985645909336, 211.04930499236548],\n", " [-30987.007809902152, -3885.060833588129, 236.4240412132351],\n", " [-30897.23512653352, -3857.850911689621, 256.41812679805906],\n", " [-30986.238402738425, -3885.011912843952, 238.73699571320833]],\n", " 'wf_edges': [(0, 1), (9, 10), (9, 6), (2, 3), (6, 7), (4, 5), (8, 9)]},\n", " {'__key__': '4a9db3f01fc',\n", " 'wf_vertices': [[140.5923077588643, 568.0331971822718, 621.7213277217998],\n", " [-1938.6389415643314, 727.3924865285123, 673.4728209475492],\n", " [352.09664391080037, 797.6620314491772, 304.51673716594087],\n", " [-369.22958674440576, 306.9829993424984, 299.66072180165315],\n", " [482.52147715156593, 445.4772873378937, 258.1562243249268],\n", " [1769.441559711204, -2621.4528244736516, 1336.312512107657],\n", " [421.89667701209396, -255.41250350988764, 540.3143548415402],\n", " [-478.5545211884521, -539.3357932369696, 276.6640528865236],\n", " [-415.95339402878005, -181.32986044981385, 306.425500472507],\n", " [271.84984840734546, -166.60757381389283, 302.7586025288566],\n", " [1020.33761244525, -220.54415532979158, 289.1018606939951],\n", " [771.1094699183764, 2718.210217826132, 1174.7441717324211],\n", " [1085.8969237543995, 374.29270187162206, 541.6004841080353],\n", " [-603.0762305300042, 327.46210619097633, 256.18819823158174],\n", " [-563.5121681916913, 631.6628251175741, 329.82350529649887],\n", " [-927.6425850986291, -143.22057397789536, 189.66336407615893]],\n", " 'wf_edges': [(0, 1),\n", " (9, 10),\n", " (3, 4),\n", " (12, 10),\n", " (11, 13),\n", " (5, 7),\n", " (11, 12),\n", " (0, 2),\n", " (8, 9),\n", " (5, 6),\n", " (5, 3),\n", " (11, 8),\n", " (14, 15)]},\n", " {'__key__': '6c83831d360',\n", " 'wf_vertices': [[1053.4540307519378, 25.091843778120932, 227.5736874452652],\n", " [1064.53337121514, -194.38222695067319, 218.9287586659969],\n", " [1080.5867286767673, 112.16115189725429, 218.00474822099113],\n", " [343.24487171974556, -241.74773642517965, 502.36712101532555],\n", " [370.60698128246736, 313.1586951087999, 497.2633456717723],\n", " [1202.9672188039226, 166.83239256954448, 186.1143172567506],\n", " [670.6001740553069, 146.49864244585802, 389.0384574396731],\n", " [851.715648973214, 748.6089137714589, 286.87421471534776],\n", " [763.3586967683427, 474.8545426638081, 328.7468354660439],\n", " [807.9062878715849, 605.1637833364994, 273.8604782482685],\n", " [917.2200080158791, 697.2534918047868, 212.46487768437822],\n", " [865.5328677851082, -163.56868062500507, 282.1257635347928],\n", " [847.115733843268, -312.42709271231047, 227.60565902432324],\n", " [896.105680095847, 592.741641006601, 230.4227418153909],\n", " [963.4149976799919, 562.0940230433072, 217.07586249020602],\n", " [-933.0611526262298, 403.56212558684473, 236.25909571193284],\n", " [-919.9244168309109, 79.30238316061309, 241.2568451689206],\n", " [-598.3186458481438, 408.6394807920102, 419.52397330306724],\n", " [165.90991812602192, -559.3875914506883, 774.6691028678476]],\n", " 'wf_edges': [(0, 1),\n", " (13, 14),\n", " (3, 4),\n", " (6, 5),\n", " (7, 10),\n", " (18, 16),\n", " (8, 10),\n", " (11, 12),\n", " (15, 16),\n", " (4, 5),\n", " (0, 2),\n", " (8, 9),\n", " (17, 15),\n", " (6, 0),\n", " (7, 8)]}]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 56 }, { "metadata": {}, "cell_type": "markdown", "source": "# grid search", "id": "b276132bf3fa7390" }, { "metadata": {}, "cell_type": "code", "source": [ "wed_stats = {}\n", "\n", "\n", "for radius in range(10, 50, 5):\n", " for angle in range(1, 10):\n", " for extend in range(0, 40, 10):\n", " solution = []\n", "\n", " with ProcessPoolExecutor(max_workers=12) as pool:\n", " results = []\n", " for i, sample in enumerate(tqdm(dataset)):\n", " results.append(pool.submit(predict, sample, visualize=False, point_radius=radius, max_angle=angle, extend=extend))\n", " \n", " for i, result in enumerate(tqdm(results)):\n", " key, pred_vertices, pred_edges = result.result()\n", " solution.append({\n", " '__key__': key,\n", " 'wf_vertices': pred_vertices.tolist(),\n", " 'wf_edges': pred_edges\n", " })\n", " \n", " solution_dict = {}\n", " for d in solution[:173]:\n", " solution_dict[d[\"__key__\"]] = d\n", " WEDs = []\n", " \n", " for i in targets:\n", " try:\n", " j = solution_dict[i[\"__key__\"]]\n", " i = dict(np.load(io.BytesIO(i[\"wireframe.npz\"])))\n", " WEDs.append(hoho.compute_WED(j[\"wf_vertices\"], j[\"wf_edges\"], i[\"wf_vertices\"], i[\"wf_edges\"]))\n", " except KeyError:\n", " pass\n", "\n", " wed_stats[(radius, angle, extend)] = ss.describe(WEDs)" ], "id": "b9fc75215dbcfb87", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 346/346 [01:16<00:00, 4.50it/s]\n", "346it [00:11, 29.01it/s] \n", "100%|██████████| 346/346 [01:14<00:00, 4.62it/s]\n", "346it [00:11, 29.20it/s] \n", "100%|██████████| 346/346 [01:14<00:00, 4.66it/s]\n", "346it [00:11, 29.43it/s] \n", "100%|██████████| 346/346 [01:16<00:00, 4.54it/s]\n", "346it [00:12, 28.72it/s] \n", "100%|██████████| 346/346 [01:14<00:00, 4.64it/s]\n", "346it [00:11, 29.44it/s] \n", "100%|██████████| 346/346 [01:18<00:00, 4.43it/s]\n", "346it [00:11, 29.12it/s] \n", "100%|██████████| 346/346 [01:18<00:00, 4.40it/s]\n" ] } ], "execution_count": 9 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T05:40:19.373552Z", "start_time": "2024-05-27T05:40:19.364100Z" } }, "cell_type": "code", "source": "print(wed_stats)", "id": "539b45cc86fa2d0b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{(10, 1, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.2688324308036), mean=2.918631217038793, variance=0.2655104551963414, skewness=-0.5301066567004962, kurtosis=0.6648724709751717), (10, 1, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.756003469399469, variance=0.28989047602798246, skewness=-0.2945369702420912, kurtosis=-0.02369081827086239), (10, 1, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.832138275353508), mean=2.7507068339662806, variance=0.25787775042947936, skewness=-0.37508484769566325, kurtosis=-0.03210901516830633), (10, 1, 30): DescribeResult(nobs=173, minmax=(1.7501416446074862, 4.191259803191946), mean=2.961261234054409, variance=0.2398959796022379, skewness=-0.1092311580556235, kurtosis=-0.26051346232955686), (10, 2, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.9003120200331316, variance=0.261910349388631, skewness=-0.5119892469577535, kurtosis=0.6424781625590157), (10, 2, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.703919186064029, variance=0.2910392479358703, skewness=-0.17569831800238767, kurtosis=-0.026657277400341073), (10, 2, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.681594986261088, variance=0.27842546188147577, skewness=-0.38497515584895964, kurtosis=-0.1392300490851719), (10, 2, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.931984625553082, variance=0.2494577589000683, skewness=-0.11959221264031848, kurtosis=-0.13815791364404895), (10, 3, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.894245688555616, variance=0.26682389539585616, skewness=-0.5105429044672873, kurtosis=0.6087195136790573), (10, 3, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.683735530723601, variance=0.2893644793141911, skewness=-0.24196472267608216, kurtosis=-0.01272835493523905), (10, 3, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.660704229257497, variance=0.26893152564304285, skewness=-0.33487550920479664, kurtosis=-0.16293684332682234), (10, 3, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.92518079836633, variance=0.24887539377691065, skewness=-0.14225847098489716, kurtosis=-0.08017538671679647), (10, 4, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8886994646476283, variance=0.27163703068449624, skewness=-0.5564449875702622, kurtosis=0.7075542805925097), (10, 4, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.676455843721493, variance=0.2937927870249197, skewness=-0.2234006371411064, kurtosis=-0.09048904303855432), (10, 4, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6498871800519215, variance=0.26492318590273994, skewness=-0.32446593956630665, kurtosis=-0.17512206191987056), (10, 4, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9231854043619783, variance=0.24314027663665091, skewness=-0.1893019934136203, kurtosis=-0.14083355822754395), (10, 5, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873477731939214, variance=0.2728824437420165, skewness=-0.5642008052784352, kurtosis=0.7097326337896264), (10, 5, 10): DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669744008221317, variance=0.29841910879887634, skewness=-0.22225993120261048, kurtosis=-0.0472763227484263), (10, 5, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6471995488373823, variance=0.26381906477320244, skewness=-0.320655946681577, kurtosis=-0.169339018678476), (10, 5, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9227759884362543, variance=0.24341675218182648, skewness=-0.1876547463116731, kurtosis=-0.1468739255370366), (10, 6, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.886892246290024, variance=0.27395091325862625, skewness=-0.5656360125824968, kurtosis=0.7011024739716984), (10, 6, 10): DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669214639548226, variance=0.2935420680896115, skewness=-0.25504270121568223, kurtosis=-0.05367597717982964), (10, 6, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.646515331167755, variance=0.2625843266390292, skewness=-0.33129530187229717, kurtosis=-0.17352394193686305), (10, 6, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 7, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 7, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.667071442043656, variance=0.29269861161342803, skewness=-0.23300886540344137, kurtosis=-0.09841994971348544), (10, 7, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6441927741135607, variance=0.2594721361604495, skewness=-0.34778411539796744, kurtosis=-0.15659274696374137), (10, 7, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 8, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 8, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853), (10, 8, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.644217801251108, variance=0.2594927958428506, skewness=-0.3477939113557061, kurtosis=-0.15687301104545748), (10, 8, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 9, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 9, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853), (10, 9, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6445636740626393, variance=0.25947719317193274, skewness=-0.34986189727592815, kurtosis=-0.15558152824255256), (10, 9, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (15, 1, 0): DescribeResult(nobs=173, minmax=(1.4871436032569523, 4.281798203709453), mean=2.836863894959103, variance=0.2729460542498244, skewness=-0.20357139230435106, kurtosis=0.09873747800409838), (15, 1, 10): DescribeResult(nobs=173, minmax=(1.3160117650280163, 3.6053806394700256), mean=2.601205819621667, variance=0.27289267701422726, skewness=-0.4952990792320176, kurtosis=-0.03882736218116145), (15, 1, 20): DescribeResult(nobs=173, minmax=(1.469281283482776, 3.6261780809782476), mean=2.5122558974038944, variance=0.2440282829471233, skewness=-0.12522757705704293, kurtosis=-0.48285495809641654), (15, 1, 30): DescribeResult(nobs=173, minmax=(1.2588005585814288, 4.191259803191946), mean=2.7124903538315728, variance=0.28517216483889607, skewness=-0.21273665971149405, kurtosis=-0.06564314346805888), (15, 2, 0): DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7671814146840363, variance=0.27029380686229254, skewness=-0.2091631733056473, kurtosis=0.0691644289164639), (15, 2, 10): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.5448537261245736), mean=2.5393145110575412, variance=0.26180386621894675, skewness=-0.33957640223485785, kurtosis=-0.20905919146227614), (15, 2, 20): DescribeResult(nobs=173, minmax=(1.2259766995447088, 3.6261780809782476), mean=2.4641723063091154, variance=0.2159265085681024, skewness=-0.010343951137930279, kurtosis=-0.30470002743739455), (15, 2, 30): DescribeResult(nobs=173, minmax=(1.3898060381764048, 3.688916269874316), mean=2.609196850447406, variance=0.2716643462073717, skewness=-0.2642797975682462, kurtosis=-0.2848939064366114), (15, 3, 0): DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7458533421389415, variance=0.2675799369586586, skewness=-0.1809451788868796, kurtosis=0.05540112858880075), (15, 3, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5172126530759686, variance=0.2807755090016337, skewness=-0.36304760594914764, kurtosis=-0.09794600674215204), (15, 3, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.441581110876761, variance=0.22090427842471203, skewness=-0.03683980809263881, kurtosis=-0.37562510182765596), (15, 3, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.688916269874316), mean=2.580109759802578, variance=0.2821922595914078, skewness=-0.15430239150351965, kurtosis=-0.5342614515549466), (15, 4, 0): DescribeResult(nobs=173, minmax=(1.1184268068034193, 4.095658947639928), mean=2.728873529740259, variance=0.2771105516332162, skewness=-0.31565304601887006, kurtosis=0.26495658847041126), (15, 4, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.514454351924287, variance=0.2641923391146466, skewness=-0.2689525157400004, kurtosis=-0.16619392726022308), (15, 4, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.425698034696807, variance=0.21914834068542002, skewness=-0.06652177289464448, kurtosis=-0.3290842422376308), (15, 4, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.5666447985570335, variance=0.26843038578214184, skewness=-0.14633910677551268, kurtosis=-0.560670544130895), (15, 5, 0): DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.7177283637337104, variance=0.2723959536913965, skewness=-0.33348323143825864, kurtosis=0.24648247402512613), (15, 5, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5104525144988883, variance=0.258237832865022, skewness=-0.2592235877859567, kurtosis=-0.11572394159871147), (15, 5, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4254430153975073, variance=0.2139334478379118, skewness=-0.09371038896209857, kurtosis=-0.2511108814365266), (15, 5, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.554533540655466, variance=0.269587546488235, skewness=-0.10541615752597985, kurtosis=-0.5658111536655612), (15, 6, 0): DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.711894351727476, variance=0.278369156594265, skewness=-0.30086035835336405, kurtosis=0.2023964929072064), (15, 6, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.507056878471702, variance=0.2500685023613651, skewness=-0.2455081158344568, kurtosis=-0.06388626860450675), (15, 6, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4237830561768567, variance=0.2129392320893983, skewness=-0.08376772978701559, kurtosis=-0.2716619126555728), (15, 6, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.549041398461515, variance=0.2728931242961085, skewness=-0.15514934615427897, kurtosis=-0.505909867544049), (15, 7, 0): DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.7078749214557574, variance=0.2795285538018334, skewness=-0.2666613713523985, kurtosis=0.0995119864201448), (15, 7, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.502045838854079, variance=0.24585856220767538, skewness=-0.24920723946558182, kurtosis=-0.08569310815061959), (15, 7, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4252546445919934, variance=0.2075275053147006, skewness=-0.07069685472679284, kurtosis=-0.24599343619613867), (15, 7, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5504816395542744, variance=0.2695209952065683, skewness=-0.12792988710767947, kurtosis=-0.5327642254880787), (15, 8, 0): DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.704509705407346, variance=0.27797811695164676, skewness=-0.3262617881322571, kurtosis=0.09119239683428315), (15, 8, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.499706581469198, variance=0.2470089007313356, skewness=-0.24007391357504945, kurtosis=-0.10916436838477361), (15, 8, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250234741780834, variance=0.20909704825092762, skewness=-0.06777194176027414, kurtosis=-0.27625274525067756), (15, 8, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5485245903964615, variance=0.26862635121426187, skewness=-0.12194093267641852, kurtosis=-0.5241112363445328), (15, 9, 0): DescribeResult(nobs=173, minmax=(1.3957580288394014, 3.9147376529767435), mean=2.7075434147500044, variance=0.27093066675680394, skewness=-0.25501352814444467, kurtosis=-0.08732045907999852), (15, 9, 10): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.66441849400411), mean=2.4991184440663403, variance=0.24559035270808768, skewness=-0.21622575184806114, kurtosis=-0.16251586619206204), (15, 9, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250708875937916, variance=0.20883053530286166, skewness=-0.07138164793039069, kurtosis=-0.27475050421781333), (15, 9, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5464305599413226, variance=0.26795495027539973, skewness=-0.11469074924572617, kurtosis=-0.517523709902524), (20, 1, 0): DescribeResult(nobs=173, minmax=(1.3423468584053975, 3.897319842199613), mean=2.684073393901844, variance=0.2807864899381598, skewness=-0.2364426189045562, kurtosis=-0.20265061882354019), (20, 1, 10): DescribeResult(nobs=173, minmax=(1.3362673027274208, 3.6261780809782476), mean=2.514185778093504, variance=0.24260229343779774, skewness=-0.13507501269991837, kurtosis=-0.2711732535949607), (20, 1, 20): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.461412940346188, variance=0.22261073233179457, skewness=0.013054843197617676, kurtosis=-0.4048394269957174), (20, 1, 30): DescribeResult(nobs=173, minmax=(1.2827429687623266, 3.6506824010059473), mean=2.506670301022558, variance=0.2521143544916601, skewness=-0.09572168770654672, kurtosis=-0.3066998890567092), (20, 2, 0): DescribeResult(nobs=173, minmax=(1.2722541239378204, 3.8656333191299255), mean=2.5990973697762323, variance=0.26878379224054205, skewness=-0.08572147612813517, kurtosis=-0.12335277334607442), (20, 2, 10): DescribeResult(nobs=173, minmax=(1.3251742111209324, 3.6261780809782476), mean=2.4703947628727625, variance=0.23315199031846423, skewness=-0.008292557084480892, kurtosis=-0.3128243051811035), (20, 2, 20): DescribeResult(nobs=173, minmax=(1.270472247990643, 3.6261780809782476), mean=2.395570462065189, variance=0.22346972511382623, skewness=0.11329897943777814, kurtosis=-0.3234502459445663), (20, 2, 30): DescribeResult(nobs=173, minmax=(1.0487238909712968, 3.5605306008619384), mean=2.42725248440732, variance=0.240912705804242, skewness=-0.027445577863958494, kurtosis=-0.2989541627256087), (20, 3, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.7118106452900848), mean=2.5648878081388222, variance=0.288071300712369, skewness=-0.1560031922606656, kurtosis=-0.110412069870931), (20, 3, 10): DescribeResult(nobs=173, minmax=(1.2702796676835728, 3.6261780809782476), mean=2.4429333572379126, variance=0.2317515347098559, skewness=0.030755596810590057, kurtosis=-0.38533107980366), (20, 3, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3788947741669393, variance=0.2264973495722334, skewness=0.09025408327789222, kurtosis=-0.42256332202067437), (20, 3, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.392065057934599, variance=0.23365257607206494, skewness=-0.04666906316083106, kurtosis=-0.474086583223138), (20, 4, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5435791505822083, variance=0.28893024616184776, skewness=-0.16093508869298748, kurtosis=-0.08748636816873168), (20, 4, 10): DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4355294363069016, variance=0.22844370985525814, skewness=0.04564180713737524, kurtosis=-0.4708318500785569), (20, 4, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.383163900730137, variance=0.2199302363476837, skewness=0.13714925299665656, kurtosis=-0.47939364685394814), (20, 4, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.386627232078584, variance=0.22454729567294118, skewness=-0.07781410951642269, kurtosis=-0.3910997560468208), (20, 5, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5374620593106982, variance=0.2851672776793523, skewness=-0.13276170000964915, kurtosis=-0.08378526032506972), (20, 5, 10): DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4385881982087474, variance=0.22429320966832825, skewness=0.04935373950656626, kurtosis=-0.4198054833251561), (20, 5, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3922155425953786, variance=0.22032420003471148, skewness=0.14340784428616138, kurtosis=-0.5120992116516523), (20, 5, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3814874348247117, variance=0.21826226065443943, skewness=-0.031130696417601945, kurtosis=-0.3603365136513217), (20, 6, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5335298487193905, variance=0.277852958678635, skewness=-0.06653145571842661, kurtosis=-0.09408401959396251), (20, 6, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.42404563849526, variance=0.2245852469553631, skewness=0.07550445732280753, kurtosis=-0.33011654543771085), (20, 6, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.392327612986733, variance=0.22581619892859958, skewness=0.19485470098090532, kurtosis=-0.4507202547441471), (20, 6, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3800291337752557, variance=0.21008321180837491, skewness=-0.014567494878515918, kurtosis=-0.38431393219096144), (20, 7, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.53252174939801, variance=0.2771156373425166, skewness=-0.07208813987939296, kurtosis=-0.1032661224771787), (20, 7, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.420607944985054, variance=0.22213448574790956, skewness=0.07369247045913387, kurtosis=-0.3204623682911296), (20, 7, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3895829009651486, variance=0.22363397726813797, skewness=0.20584534226522705, kurtosis=-0.4096143639898431), (20, 7, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.376325762160446, variance=0.2060657302187169, skewness=0.01250070338034022, kurtosis=-0.34430512545245584), (20, 8, 0): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.533041000736945, variance=0.2762960956844567, skewness=-0.0518493542005356, kurtosis=-0.14641200049739478), (20, 8, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.4224237705113114, variance=0.22372683062153817, skewness=0.07733561784015744, kurtosis=-0.3741548157951047), (20, 8, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.391465728756091, variance=0.22394370233911176, skewness=0.1902320233296623, kurtosis=-0.41359222542326357), (20, 8, 30): DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.378366762261831, variance=0.202805898739558, skewness=0.044863602165060454, kurtosis=-0.40796058479310604), (20, 9, 0): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.530455916115353, variance=0.277895703511729, skewness=-0.05581159504734332, kurtosis=-0.14130198306143615), (20, 9, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.421994549752394, variance=0.22368579559809232, skewness=0.07766446553557484, kurtosis=-0.37632005523926537), (20, 9, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3897760002726582, variance=0.2225776378698793, skewness=0.18575382982618197, kurtosis=-0.40539773457991934), (20, 9, 30): DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.377521039722907, variance=0.20392050998976574, skewness=0.04554219008201783, kurtosis=-0.4196129929250443), (25, 1, 0): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.66441849400411), mean=2.56181421211636, variance=0.2601095214113185, skewness=-0.32632240498411913, kurtosis=-0.12005309910500106), (25, 1, 10): DescribeResult(nobs=173, minmax=(1.454272571633673, 3.6261780809782476), mean=2.473339889745894, variance=0.2233933922107472, skewness=0.017829377188284472, kurtosis=-0.4460671739578528), (25, 1, 20): DescribeResult(nobs=173, minmax=(1.3793123740968751, 3.7195088150342186), mean=2.4150328016456037, variance=0.22965534837872242, skewness=0.11268451204073322, kurtosis=-0.3615049140619653), (25, 1, 30): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.4086178137902587, variance=0.23777282252768617, skewness=-0.018916891642525895, kurtosis=-0.21225144452113565), (25, 2, 0): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.6261780809782476), mean=2.4906008151320926, variance=0.248685931657691, skewness=-0.15565003519874476, kurtosis=-0.2276167953822883), (25, 2, 10): DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.419065699428399, variance=0.21591709226718184, skewness=0.2058330707307416, kurtosis=-0.3095627615564962), (25, 2, 20): DescribeResult(nobs=173, minmax=(1.3187205857224815, 3.7195088150342186), mean=2.3789786957469414, variance=0.23032806261527008, skewness=0.20786286650802807, kurtosis=-0.25287251641055786), (25, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3744699364369355, variance=0.2149962896981116, skewness=0.22963219434874785, kurtosis=-0.39122511587047537), (25, 3, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.6268822743745335), mean=2.4567771801373315, variance=0.26685758977245705, skewness=-0.13106930458537935, kurtosis=-0.23530203208601286), (25, 3, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4108899944823015, variance=0.21198194312074198, skewness=0.24085917574500354, kurtosis=-0.46285314189413684), (25, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.3796113176549283, variance=0.2313326274790801, skewness=0.18020655451793677, kurtosis=-0.3976050568640255), (25, 3, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5793431626907006), mean=2.365799431760512, variance=0.2283485251258321, skewness=0.14581362181613933, kurtosis=-0.4966945138162737), (25, 4, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.450736724300251, variance=0.255481258716427, skewness=-0.0916393786706801, kurtosis=-0.1946484500943786), (25, 4, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.410518885127268, variance=0.20550593327046196, skewness=0.2499724220330842, kurtosis=-0.4559192902865674), (25, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3795244890339258, variance=0.2267076428562555, skewness=0.18073800339766158, kurtosis=-0.46599314212218124), (25, 4, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5559859764406214), mean=2.367685477902936, variance=0.22928262970110813, skewness=0.1234810295855286, kurtosis=-0.6546653426007194), (25, 5, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.441599693225722, variance=0.25279254900744635, skewness=-0.049430446846301976, kurtosis=-0.23987523776176367), (25, 5, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.409439772520956, variance=0.20459373614192744, skewness=0.2593292649700985, kurtosis=-0.43989262943361895), (25, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3813709841735355, variance=0.23045884130101402, skewness=0.23712341428131212, kurtosis=-0.40079076893774035), (25, 5, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.369952540791647, variance=0.23642155447313948, skewness=0.15339830751247546, kurtosis=-0.6673466083790669), (25, 6, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4356151895352216, variance=0.2545402506829705, skewness=-0.014315337290249688, kurtosis=-0.23985265026039926), (25, 6, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.3997149663071773, variance=0.21211940441425559, skewness=0.3057379221458508, kurtosis=-0.4542345695303873), (25, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.375564585578571, variance=0.23205540022105775, skewness=0.191174590767773, kurtosis=-0.39762545239110425), (25, 6, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.3758489971585637, variance=0.24544595203606948, skewness=0.20399683624555676, kurtosis=-0.6183066207894132), (25, 7, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4332410339185766, variance=0.2491228344995086, skewness=-0.01261753485038881, kurtosis=-0.2375642444532593), (25, 7, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.396406456777108, variance=0.21087635212900227, skewness=0.3024016257197271, kurtosis=-0.40876460192220465), (25, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3704425282816417, variance=0.23182957468994056, skewness=0.22228340584379075, kurtosis=-0.3626070143296296), (25, 7, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.373490069485539, variance=0.24398496452787613, skewness=0.2351830610301328, kurtosis=-0.5279495534648584), (25, 8, 0): DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.4366509101256306, variance=0.24380395324511875, skewness=0.055522021814290286, kurtosis=-0.3745228956384281), (25, 8, 10): DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.401775050693766, variance=0.21072810372063794, skewness=0.29902784407963034, kurtosis=-0.4643048528579099), (25, 8, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3666799964968566, variance=0.23256287638977155, skewness=0.2351978824288662, kurtosis=-0.34650790205426807), (25, 8, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.371012587748782, variance=0.2450203543725082, skewness=0.2469304978724812, kurtosis=-0.4951650893264814), (25, 9, 0): DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.43477140368765, variance=0.2432307330156745, skewness=0.05414275019076038, kurtosis=-0.36907364977950285), (25, 9, 10): DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.398615207826399, variance=0.20887753685860447, skewness=0.3187548013407416, kurtosis=-0.4229798846403865), (25, 9, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.368605800644157, variance=0.2294246885522206, skewness=0.27285275332363895, kurtosis=-0.39286936432663166), (25, 9, 30): DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.6427458988029624), mean=2.365431389151285, variance=0.24408016546768208, skewness=0.24970843688533947, kurtosis=-0.47489943956804215), (30, 1, 0): DescribeResult(nobs=173, minmax=(1.1532672794627605, 3.6261780809782476), mean=2.4826310994850456, variance=0.23833070883681687, skewness=-0.05751429986772294, kurtosis=-0.27576009544920854), (30, 1, 10): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.433282778268923, variance=0.22134877479162188, skewness=0.08024460232414445, kurtosis=-0.3947248921866984), (30, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3975186504066417, variance=0.22094968513618016, skewness=0.07480434328399128, kurtosis=-0.16335538209499267), (30, 1, 30): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3861556873839587, variance=0.22842621158531876, skewness=0.1290414047412115, kurtosis=-0.07336495164965884), (30, 2, 0): DescribeResult(nobs=173, minmax=(1.4082365099263328, 3.6261780809782476), mean=2.438737352325921, variance=0.22805965113028293, skewness=0.1429818073521336, kurtosis=-0.4555229006368986), (30, 2, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.3902302733627527, variance=0.2306056632981275, skewness=0.13261356954170908, kurtosis=-0.27069709723457525), (30, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.637570667436926), mean=2.3786084222442034, variance=0.22573612749333788, skewness=0.288059435082738, kurtosis=-0.40815147247155315), (30, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3740534257011907, variance=0.2220012467665858, skewness=0.23492208208246168, kurtosis=-0.40217807803242067), (30, 3, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4271405903951044, variance=0.23033291768137476, skewness=0.1631811023842746, kurtosis=-0.5828797184614842), (30, 3, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.384897415418424, variance=0.22637196926022032, skewness=0.19173071548573675, kurtosis=-0.43595503689616644), (30, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.637570667436926), mean=2.3829987003334803, variance=0.2311443756171956, skewness=0.14685703983398077, kurtosis=-0.5259863508655851), (30, 3, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.562558606945364), mean=2.3860009450938464, variance=0.23103302426248906, skewness=0.11204332345204361, kurtosis=-0.5490414886033808), (30, 4, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4275089000561385, variance=0.22327316852087933, skewness=0.17835340203663447, kurtosis=-0.5397748344422348), (30, 4, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.397930773143809, variance=0.22641718268691954, skewness=0.17481864720402482, kurtosis=-0.4152277535960023), (30, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.617632303538528), mean=2.383797434796488, variance=0.23380033902387592, skewness=0.18900269218480067, kurtosis=-0.5808358084581036), (30, 4, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5455110669933947), mean=2.3917898129538484, variance=0.24078771833868412, skewness=0.19864827152416367, kurtosis=-0.5474088816866427), (30, 5, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4345756829691187, variance=0.22113762404024856, skewness=0.14077885323381606, kurtosis=-0.5306541114660988), (30, 5, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3941387003152848, variance=0.23003758231830418, skewness=0.21058066331754136, kurtosis=-0.379609944610138), (30, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3790364690020165, variance=0.23647421694312673, skewness=0.22090866080940696, kurtosis=-0.514964156012657), (30, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.385768838953655, variance=0.25181681694295693, skewness=0.28409827803721455, kurtosis=-0.3134374893329581), (30, 6, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.432507639466392, variance=0.2202826193726728, skewness=0.18051045766742263, kurtosis=-0.510441313183494), (30, 6, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3879080309744856, variance=0.22786302055822993, skewness=0.23902741677955017, kurtosis=-0.4056748763379603), (30, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716579318006623, variance=0.2422788765943085, skewness=0.20670554315360368, kurtosis=-0.5289317019408943), (30, 6, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.576250985624627), mean=2.3850795520255197, variance=0.24493882723561317, skewness=0.2203755344910728, kurtosis=-0.4931265530057929), (30, 7, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.429406136909439, variance=0.21858010355750818, skewness=0.2000876621727689, kurtosis=-0.5077100524895188), (30, 7, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389402864252692, variance=0.2320812051198164, skewness=0.3130484267618166, kurtosis=-0.32815775713868645), (30, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716701561009708, variance=0.24731604266709206, skewness=0.22941321516711177, kurtosis=-0.5283369198285905), (30, 7, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.381611757332351, variance=0.2506468837033553, skewness=0.26675328203354126, kurtosis=-0.402185686093512), (30, 8, 0): DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4298489368050427, variance=0.21888683945586074, skewness=0.20063596567110728, kurtosis=-0.5176138357954292), (30, 8, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389938416093227, variance=0.2285287200579085, skewness=0.33902253204824595, kurtosis=-0.32922349087198244), (30, 8, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.372573018808825, variance=0.248495576144028, skewness=0.26669420512826, kurtosis=-0.5578124932452173), (30, 8, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3846008662163936, variance=0.2547504201897208, skewness=0.29648509192680983, kurtosis=-0.4623848643623356), (30, 9, 0): DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4262628008241856, variance=0.21831525479458277, skewness=0.22263078724693997, kurtosis=-0.48808525476895026), (30, 9, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389975063842067, variance=0.22608397671643873, skewness=0.35686742328669135, kurtosis=-0.2997528177957811), (30, 9, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.377108364289762, variance=0.24495071976894514, skewness=0.3024991123083083, kurtosis=-0.5537863211020939), (30, 9, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3808467678446803, variance=0.25066438236687766, skewness=0.2895430956226167, kurtosis=-0.47877754061451405), (35, 1, 0): DescribeResult(nobs=173, minmax=(1.3273505012242865, 3.6261780809782476), mean=2.4442174494359254, variance=0.22270434251888466, skewness=0.06292702158159418, kurtosis=-0.41536878070008587), (35, 1, 10): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3906148388612825, variance=0.23612292896679513, skewness=0.06810877547812552, kurtosis=-0.30725869635176695), (35, 1, 20): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3776617261354445, variance=0.23247429066020053, skewness=0.10806676086801285, kurtosis=-0.16554652753591004), (35, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3945732197549288, variance=0.23483766651871996, skewness=0.16196716664718905, kurtosis=-0.10484682014817315), (35, 2, 0): DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.411574201138517, variance=0.21578778205407273, skewness=0.23369600888365885, kurtosis=-0.2867155946425828), (35, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3681322155779267, variance=0.2347349275743601, skewness=0.2734479351216136, kurtosis=-0.38666271102839156), (35, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.3748694433759123, variance=0.22888427449653234, skewness=0.28036566784897027, kurtosis=-0.5267513800129584), (35, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.6212506595263343), mean=2.3854325367926594, variance=0.23457032730509317, skewness=0.2604446162510226, kurtosis=-0.3349483534626043), (35, 3, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.405989752988432, variance=0.212938956410703, skewness=0.24447258131202554, kurtosis=-0.5271312715451346), (35, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.375368586764985, variance=0.23069279313015867, skewness=0.20332670930357677, kurtosis=-0.4795555488120784), (35, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.376378529726554, variance=0.2327546566086452, skewness=0.27519000763349377, kurtosis=-0.31771745086975933), (35, 3, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976350502222, variance=0.23944715281813153, skewness=0.17912464319890165, kurtosis=-0.2781964142653237), (35, 4, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.408989352750845, variance=0.20993256278594338, skewness=0.20771311895378974, kurtosis=-0.5114565720950774), (35, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3862119885427275, variance=0.22383472123068804, skewness=0.20644346651108006, kurtosis=-0.48916590957336537), (35, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3842603364378356, variance=0.22992856792091504, skewness=0.3132728720903736, kurtosis=-0.28223856418657345), (35, 4, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.400215914906969, variance=0.2465940458980044, skewness=0.24000447703882694, kurtosis=-0.2886091250801641), (35, 5, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.406311167879119, variance=0.20781769633334632, skewness=0.2642635361018867, kurtosis=-0.3980318498632758), (35, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3872945246573107, variance=0.2244550722608443, skewness=0.250064567983947, kurtosis=-0.38361445269250094), (35, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3922777542401183, variance=0.23879292523118395, skewness=0.32127679699013006, kurtosis=-0.2637818045632927), (35, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.3889835348948454, variance=0.2569148258105363, skewness=0.2728444039629951, kurtosis=-0.22732955648449993), (35, 6, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.397308803810493, variance=0.21627613395654613, skewness=0.3096471084250271, kurtosis=-0.41018903098186943), (35, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.382804047460243, variance=0.22894779192643214, skewness=0.2291410779208234, kurtosis=-0.41091063392043337), (35, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.576250985624627), mean=2.3937824872694407, variance=0.2376169204304254, skewness=0.26069901598295314, kurtosis=-0.40435481472120305), (35, 6, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.6212506595263343), mean=2.3904977600986737, variance=0.2507129621263109, skewness=0.21630205116480614, kurtosis=-0.3522875594025541), (35, 7, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.401718522915244, variance=0.21725510948778534, skewness=0.32112902558791273, kurtosis=-0.397168495428879), (35, 7, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3917090310591567, variance=0.23053829906762635, skewness=0.2568710565884056, kurtosis=-0.36570028673332855), (35, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7689006556014593), mean=2.394708380809293, variance=0.24331914134325108, skewness=0.3104609785236659, kurtosis=-0.34468360619940963), (35, 7, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.38699418676085, variance=0.2577835077161205, skewness=0.2766783845585309, kurtosis=-0.29476357152580457), (35, 8, 0): DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.4064772123154166, variance=0.21721735273717763, skewness=0.3199872090051091, kurtosis=-0.41302195380383466), (35, 8, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3908782343737354, variance=0.23036013923586704, skewness=0.28462534178743354, kurtosis=-0.38709455161808304), (35, 8, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3986405901311167, variance=0.24608385998671142, skewness=0.3549449464418198, kurtosis=-0.4001901870073876), (35, 8, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.39275698546612, variance=0.257313874546351, skewness=0.2951195589126923, kurtosis=-0.32469376243371917), (35, 9, 0): DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.402065366133696, variance=0.21630799827125896, skewness=0.3496875784127574, kurtosis=-0.38042501490776415), (35, 9, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3883592992402862, variance=0.23027758760347003, skewness=0.296765878163557, kurtosis=-0.34861854619750154), (35, 9, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3975590366723187, variance=0.24904851847767226, skewness=0.3568965017957376, kurtosis=-0.4302411959545829), (35, 9, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7689006556014593), mean=2.3920055363342487, variance=0.25763993287424103, skewness=0.30817427852592283, kurtosis=-0.3347297549631598), (40, 1, 0): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.7195088150342186), mean=2.4194208077125197, variance=0.22735926912395726, skewness=0.08411152670063812, kurtosis=-0.36976518164125993), (40, 1, 10): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3863502776797856, variance=0.22824894724073475, skewness=0.10516468851421981, kurtosis=-0.2594414493181243), (40, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3819968370307967, variance=0.23273599874155798, skewness=0.14303336257018318, kurtosis=-0.13731717359344797), (40, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3843476299533783, variance=0.23531452343703144, skewness=0.20823115450386134, kurtosis=-0.08506840332276866), (40, 2, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.388169259178502, variance=0.2272718361825683, skewness=0.14745033955498554, kurtosis=-0.3275151957127749), (40, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.374294140016307, variance=0.23088831111757532, skewness=0.28134191057856595, kurtosis=-0.45529187922921555), (40, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5721712499597116), mean=2.3793735172074504, variance=0.23659306029041108, skewness=0.2553454827884794, kurtosis=-0.4934855282228545), (40, 2, 30): DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.3880867134387906, variance=0.23677233140443266, skewness=0.2746348945969141, kurtosis=-0.21305949285139691), (40, 3, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.385897158360537, variance=0.23011231408637273, skewness=0.189040499666452, kurtosis=-0.42683198131482225), (40, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5793431626907006), mean=2.38261739715201, variance=0.23783393755543428, skewness=0.1711163972917373, kurtosis=-0.6522259873531335), (40, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3978819990437974, variance=0.23997789669492398, skewness=0.24892265230252733, kurtosis=-0.4017673562738979), (40, 3, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.407634797501566, variance=0.24371594638017943, skewness=0.2557220507793175, kurtosis=-0.15715980564058407), (40, 4, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.3919928188754604, variance=0.23184626352990195, skewness=0.16634687967225528, kurtosis=-0.4035931059127029), (40, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5559859764406214), mean=2.3886814716207545, variance=0.23275117890602048, skewness=0.22684340335139833, kurtosis=-0.5936785076680828), (40, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976595418096966, variance=0.23818181554736112, skewness=0.2784549893503289, kurtosis=-0.3637520804075818), (40, 4, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.412501835410017, variance=0.2504678890342812, skewness=0.23613524618695558, kurtosis=-0.2089049468054367), (40, 5, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3948665465964374, variance=0.23229474512206372, skewness=0.1883658604164852, kurtosis=-0.3117502418808762), (40, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3888673754435845, variance=0.23588824039933945, skewness=0.2881021402644574, kurtosis=-0.43324356622430704), (40, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.402673983236185, variance=0.2481837073735338, skewness=0.30838627997579465, kurtosis=-0.244559942461954), (40, 5, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7717977793967843), mean=2.4103906271074926, variance=0.2622513620731259, skewness=0.28525878762522766, kurtosis=-0.11029258861434332), (40, 6, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3839623101258454, variance=0.23607791753616203, skewness=0.22002487618359687, kurtosis=-0.37320922175372795), (40, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3895841432380847, variance=0.24536500899154293, skewness=0.2599897242497131, kurtosis=-0.5167545964253102), (40, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.4077533351492275, variance=0.25320749575495544, skewness=0.2812107479281849, kurtosis=-0.4215619870544707), (40, 6, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.724442042751297), mean=2.415071859981076, variance=0.26238842898246284, skewness=0.2633242071284276, kurtosis=-0.2445055517514012), (40, 7, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.386188689872926, variance=0.23893696990231386, skewness=0.2967800859202916, kurtosis=-0.28282915584685053), (40, 7, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3944423590294193, variance=0.2465065536857468, skewness=0.2721538250896667, kurtosis=-0.4792545836240327), (40, 7, 20): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406839724428981, variance=0.2603261771691146, skewness=0.3032186102576703, kurtosis=-0.3173295824803919), (40, 7, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7725779764160206), mean=2.4188623730204735, variance=0.26939255179747723, skewness=0.2788384614811644, kurtosis=-0.18288967289790214), (40, 8, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3930129707845498, variance=0.23215066111872443, skewness=0.2989655069270995, kurtosis=-0.2817878628513344), (40, 8, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403858377352788, variance=0.24749869146926223, skewness=0.3140850115861441, kurtosis=-0.528785556311719), (40, 8, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4124700839687243, variance=0.25532818691020326, skewness=0.35812542624780425, kurtosis=-0.3749700141329608), (40, 8, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.4264197520081887, variance=0.27049178724551853, skewness=0.318896684817541, kurtosis=-0.2539636343376732), (40, 9, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3957542543079233, variance=0.23310121388075183, skewness=0.30095341959348654, kurtosis=-0.2942558792821828), (40, 9, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403932248288499, variance=0.2487742084683387, skewness=0.3381611420772938, kurtosis=-0.5165431694838332), (40, 9, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4118508808603045, variance=0.26022119050841064, skewness=0.3682029744859333, kurtosis=-0.41218842818502033), (40, 9, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.425222538586002, variance=0.2758842502293917, skewness=0.33399224512560255, kurtosis=-0.27223767809946775), (45, 1, 0): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3924854857851394, variance=0.23275307803748638, skewness=0.07500074075038048, kurtosis=-0.2935617253280385), (45, 1, 10): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3744571686507063, variance=0.2355163304370561, skewness=0.0878789749320027, kurtosis=-0.1818687679043296), (45, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3792466167629636, variance=0.2422172698332015, skewness=0.18995496774253304, kurtosis=-0.12741963477250806), (45, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.378327126176745, variance=0.23824530454742882, skewness=0.19566459356086427, kurtosis=-0.1111469729646477), (45, 2, 0): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3766409284369105, variance=0.2295085075667106, skewness=0.24134106021315538, kurtosis=-0.37408846477628144), (45, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.373057696488171, variance=0.227657062156513, skewness=0.3100663573759112, kurtosis=-0.4733080035750321), (45, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.6212506595263343), mean=2.38632458240834, variance=0.23813819810123346, skewness=0.29652378295341514, kurtosis=-0.3672361799208925), (45, 2, 30): DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.388144182780888, variance=0.24112210638197287, skewness=0.23756936364283102, kurtosis=-0.2909234719207676), (45, 3, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.658925758785262), mean=2.385371374710671, variance=0.23433663582029468, skewness=0.14176451720155842, kurtosis=-0.5927851397233956), (45, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.38523972688129, variance=0.23853526447287604, skewness=0.27479949313624624, kurtosis=-0.37936201651186785), (45, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.4076371514178407, variance=0.24068682096802357, skewness=0.25787115006032574, kurtosis=-0.23278661069579876), (45, 3, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.412821308581424, variance=0.24551095940694598, skewness=0.2924398069064522, kurtosis=-0.07143088476505399), (45, 4, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6046958484870792), mean=2.390131984426462, variance=0.23610747813350952, skewness=0.22710720795114606, kurtosis=-0.5667969121056742), (45, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3914062869519253, variance=0.2393996096447048, skewness=0.3202604593262514, kurtosis=-0.2876938695682867), (45, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4112285800591664, variance=0.24211863480665663, skewness=0.3127233163899829, kurtosis=-0.14450499897441826), (45, 4, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.4269174633995525, variance=0.25948011673042015, skewness=0.2999659622583222, kurtosis=-0.06571939509751745), (45, 5, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.396039294508347, variance=0.23706545805278192, skewness=0.2683386111757052, kurtosis=-0.43250740115100994), (45, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3985213041603988, variance=0.24132064938614203, skewness=0.32115251612013973, kurtosis=-0.17855020892334617), (45, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4124482991603977, variance=0.248005365526009, skewness=0.35134624885668897, kurtosis=-0.007504347438727521), (45, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.9162282140460087), mean=2.432424925734303, variance=0.27393959139490665, skewness=0.373169478138468, kurtosis=0.006635598228321271), (45, 6, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.3993612139561167, variance=0.24377787252097266, skewness=0.22483344276706962, kurtosis=-0.4970600005238177), (45, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.405068165571497, variance=0.24646178290038326, skewness=0.2875176572527761, kurtosis=-0.34540566014415486), (45, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.414563246470491, variance=0.25243118749415416, skewness=0.32718722307293585, kurtosis=-0.1359966216084869), (45, 6, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9162282140460087), mean=2.436520062983622, variance=0.2731040454544077, skewness=0.3782243329854789, kurtosis=-0.038026118597547676), (45, 7, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.4051959917035837, variance=0.2450394814984553, skewness=0.23972419985337953, kurtosis=-0.4682877238864682), (45, 7, 10): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406087392078327, variance=0.2580389356909127, skewness=0.29136942528153364, kurtosis=-0.29872794085361143), (45, 7, 20): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.86758500469272), mean=2.418061266506169, variance=0.2617243674783607, skewness=0.3253382576002433, kurtosis=-0.06503236957059055), (45, 7, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.9643641477107323), mean=2.439372515277074, variance=0.2801733914247726, skewness=0.4032973011280843, kurtosis=0.004250745916431153), (45, 8, 0): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.4131517076091358, variance=0.24066940267301618, skewness=0.26131189253213666, kurtosis=-0.49633037705736216), (45, 8, 10): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4179154555308684, variance=0.25567498182680215, skewness=0.32706170459005446, kurtosis=-0.4164166171364596), (45, 8, 20): DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.4346991775070603, variance=0.2619768174726941, skewness=0.3410719204865325, kurtosis=-0.1540722090125799), (45, 8, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4478526744775175, variance=0.2808214793691462, skewness=0.45054333863678114, kurtosis=-0.022699653611266246), (45, 9, 0): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.410540212549997, variance=0.2396217756873817, skewness=0.26811073141192715, kurtosis=-0.4597579615051637), (45, 9, 10): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.416366874484952, variance=0.25708950801601954, skewness=0.3347859277339084, kurtosis=-0.41942847638872305), (45, 9, 20): DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.434922529220423, variance=0.26497488722201096, skewness=0.3487337194660302, kurtosis=-0.13525730518814028), (45, 9, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4480861377214076, variance=0.28607402615953886, skewness=0.46832740215081625, kurtosis=-0.013192895201957988)}\n" ] } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T05:47:19.852077Z", "start_time": "2024-05-27T05:47:19.846831Z" } }, "cell_type": "code", "source": [ "min_mean_idx = np.argmin([i.mean for i in wed_stats.values()])\n", "min_mean_idx" ], "id": "db1e1dba3dda9dd9", "outputs": [ { "data": { "text/plain": [ "143" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 24 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T05:47:47.480595Z", "start_time": "2024-05-27T05:47:47.475232Z" } }, "cell_type": "code", "source": "list(wed_stats.keys())[min_mean_idx]", "id": "bd23be6c6c8e8a40", "outputs": [ { "data": { "text/plain": [ "(25, 9, 30)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 29 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T07:34:11.948907Z", "start_time": "2024-05-27T07:34:11.943280Z" } }, "cell_type": "code", "source": "len(wed_stats)", "id": "9b75523b47a950da", "outputs": [ { "data": { "text/plain": [ "288" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 34 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T21:00:34.696827Z", "start_time": "2024-05-27T21:00:34.693311Z" } }, "cell_type": "code", "source": "import pickle", "id": "2a63560b980ed175", "outputs": [], "execution_count": 3 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T07:35:34.525747Z", "start_time": "2024-05-27T07:35:34.516556Z" } }, "cell_type": "code", "source": [ "with open(\"27-03-24_08_41_gridsearch.pkl\", \"wb\") as f:\n", " pickle.dump(wed_stats, f)" ], "id": "a0c0d1247345ce8f", "outputs": [], "execution_count": 37 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T21:00:37.543885Z", "start_time": "2024-05-27T21:00:36.891049Z" } }, "cell_type": "code", "source": [ "with open(\"27-03-24_08_41_gridsearch.pkl\", \"rb\") as f:\n", " wed_stats= pickle.load(f)" ], "id": "2b1bf6b71366261", "outputs": [], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2024-05-27T21:01:35.844300Z", "start_time": "2024-05-27T21:01:35.821404Z" } }, "cell_type": "code", "source": "sorted(wed_stats.items(), key=lambda x: x[1].mean)", "id": "ef3a11e047073f63", "outputs": [ { "data": { "text/plain": [ "[((25, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.6427458988029624), mean=2.365431389151285, variance=0.24408016546768208, skewness=0.24970843688533947, kurtosis=-0.47489943956804215)),\n", " ((25, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5793431626907006), mean=2.365799431760512, variance=0.2283485251258321, skewness=0.14581362181613933, kurtosis=-0.4966945138162737)),\n", " ((25, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3666799964968566, variance=0.23256287638977155, skewness=0.2351978824288662, kurtosis=-0.34650790205426807)),\n", " ((25, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5559859764406214), mean=2.367685477902936, variance=0.22928262970110813, skewness=0.1234810295855286, kurtosis=-0.6546653426007194)),\n", " ((35, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3681322155779267, variance=0.2347349275743601, skewness=0.2734479351216136, kurtosis=-0.38666271102839156)),\n", " ((25, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.368605800644157, variance=0.2294246885522206, skewness=0.27285275332363895, kurtosis=-0.39286936432663166)),\n", " ((25, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.369952540791647, variance=0.23642155447313948, skewness=0.15339830751247546, kurtosis=-0.6673466083790669)),\n", " ((25, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3704425282816417, variance=0.23182957468994056, skewness=0.22228340584379075, kurtosis=-0.3626070143296296)),\n", " ((25, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.371012587748782, variance=0.2450203543725082, skewness=0.2469304978724812, kurtosis=-0.4951650893264814)),\n", " ((30, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716579318006623, variance=0.2422788765943085, skewness=0.20670554315360368, kurtosis=-0.5289317019408943)),\n", " ((30, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716701561009708, variance=0.24731604266709206, skewness=0.22941321516711177, kurtosis=-0.5283369198285905)),\n", " ((30, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.372573018808825, variance=0.248495576144028, skewness=0.26669420512826, kurtosis=-0.5578124932452173)),\n", " ((45, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.373057696488171, variance=0.227657062156513, skewness=0.3100663573759112, kurtosis=-0.4733080035750321)),\n", " ((25, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.373490069485539, variance=0.24398496452787613, skewness=0.2351830610301328, kurtosis=-0.5279495534648584)),\n", " ((30, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3740534257011907, variance=0.2220012467665858, skewness=0.23492208208246168, kurtosis=-0.40217807803242067)),\n", " ((40, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.374294140016307, variance=0.23088831111757532, skewness=0.28134191057856595, kurtosis=-0.45529187922921555)),\n", " ((45, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3744571686507063, variance=0.2355163304370561, skewness=0.0878789749320027, kurtosis=-0.1818687679043296)),\n", " ((25, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3744699364369355, variance=0.2149962896981116, skewness=0.22963219434874785, kurtosis=-0.39122511587047537)),\n", " ((35, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.3748694433759123, variance=0.22888427449653234, skewness=0.28036566784897027, kurtosis=-0.5267513800129584)),\n", " ((35, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.375368586764985, variance=0.23069279313015867, skewness=0.20332670930357677, kurtosis=-0.4795555488120784)),\n", " ((25, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.375564585578571, variance=0.23205540022105775, skewness=0.191174590767773, kurtosis=-0.39762545239110425)),\n", " ((25, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.3758489971585637, variance=0.24544595203606948, skewness=0.20399683624555676, kurtosis=-0.6183066207894132)),\n", " ((20, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.376325762160446, variance=0.2060657302187169, skewness=0.01250070338034022, kurtosis=-0.34430512545245584)),\n", " ((35, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.376378529726554, variance=0.2327546566086452, skewness=0.27519000763349377, kurtosis=-0.31771745086975933)),\n", " ((45, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3766409284369105, variance=0.2295085075667106, skewness=0.24134106021315538, kurtosis=-0.37408846477628144)),\n", " ((30, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.377108364289762, variance=0.24495071976894514, skewness=0.3024991123083083, kurtosis=-0.5537863211020939)),\n", " ((20, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.377521039722907, variance=0.20392050998976574, skewness=0.04554219008201783, kurtosis=-0.4196129929250443)),\n", " ((35, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3776617261354445, variance=0.23247429066020053, skewness=0.10806676086801285, kurtosis=-0.16554652753591004)),\n", " ((45, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.378327126176745, variance=0.23824530454742882, skewness=0.19566459356086427, kurtosis=-0.1111469729646477)),\n", " ((20, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.378366762261831, variance=0.202805898739558, skewness=0.044863602165060454, kurtosis=-0.40796058479310604)),\n", " ((30, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.637570667436926), mean=2.3786084222442034, variance=0.22573612749333788, skewness=0.288059435082738, kurtosis=-0.40815147247155315)),\n", " ((20, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3788947741669393, variance=0.2264973495722334, skewness=0.09025408327789222, kurtosis=-0.42256332202067437)),\n", " ((25, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.3187205857224815, 3.7195088150342186), mean=2.3789786957469414, variance=0.23032806261527008, skewness=0.20786286650802807, kurtosis=-0.25287251641055786)),\n", " ((30, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3790364690020165, variance=0.23647421694312673, skewness=0.22090866080940696, kurtosis=-0.514964156012657)),\n", " ((45, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3792466167629636, variance=0.2422172698332015, skewness=0.18995496774253304, kurtosis=-0.12741963477250806)),\n", " ((40, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5721712499597116), mean=2.3793735172074504, variance=0.23659306029041108, skewness=0.2553454827884794, kurtosis=-0.4934855282228545)),\n", " ((25, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3795244890339258, variance=0.2267076428562555, skewness=0.18073800339766158, kurtosis=-0.46599314212218124)),\n", " ((25, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.3796113176549283, variance=0.2313326274790801, skewness=0.18020655451793677, kurtosis=-0.3976050568640255)),\n", " ((20, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3800291337752557, variance=0.21008321180837491, skewness=-0.014567494878515918, kurtosis=-0.38431393219096144)),\n", " ((30, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3808467678446803, variance=0.25066438236687766, skewness=0.2895430956226167, kurtosis=-0.47877754061451405)),\n", " ((25, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3813709841735355, variance=0.23045884130101402, skewness=0.23712341428131212, kurtosis=-0.40079076893774035)),\n", " ((20, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3814874348247117, variance=0.21826226065443943, skewness=-0.031130696417601945, kurtosis=-0.3603365136513217)),\n", " ((30, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.381611757332351, variance=0.2506468837033553, skewness=0.26675328203354126, kurtosis=-0.402185686093512)),\n", " ((40, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3819968370307967, variance=0.23273599874155798, skewness=0.14303336257018318, kurtosis=-0.13731717359344797)),\n", " ((40, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5793431626907006), mean=2.38261739715201, variance=0.23783393755543428, skewness=0.1711163972917373, kurtosis=-0.6522259873531335)),\n", " ((35, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.382804047460243, variance=0.22894779192643214, skewness=0.2291410779208234, kurtosis=-0.41091063392043337)),\n", " ((30, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.637570667436926), mean=2.3829987003334803, variance=0.2311443756171956, skewness=0.14685703983398077, kurtosis=-0.5259863508655851)),\n", " ((20, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.383163900730137, variance=0.2199302363476837, skewness=0.13714925299665656, kurtosis=-0.47939364685394814)),\n", " ((30, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.617632303538528), mean=2.383797434796488, variance=0.23380033902387592, skewness=0.18900269218480067, kurtosis=-0.5808358084581036)),\n", " ((40, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3839623101258454, variance=0.23607791753616203, skewness=0.22002487618359687, kurtosis=-0.37320922175372795)),\n", " ((35, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3842603364378356, variance=0.22992856792091504, skewness=0.3132728720903736, kurtosis=-0.28223856418657345)),\n", " ((40, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3843476299533783, variance=0.23531452343703144, skewness=0.20823115450386134, kurtosis=-0.08506840332276866)),\n", " ((30, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3846008662163936, variance=0.2547504201897208, skewness=0.29648509192680983, kurtosis=-0.4623848643623356)),\n", " ((30, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.384897415418424, variance=0.22637196926022032, skewness=0.19173071548573675, kurtosis=-0.43595503689616644)),\n", " ((30, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.576250985624627), mean=2.3850795520255197, variance=0.24493882723561317, skewness=0.2203755344910728, kurtosis=-0.4931265530057929)),\n", " ((45, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.38523972688129, variance=0.23853526447287604, skewness=0.27479949313624624, kurtosis=-0.37936201651186785)),\n", " ((45, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.658925758785262), mean=2.385371374710671, variance=0.23433663582029468, skewness=0.14176451720155842, kurtosis=-0.5927851397233956)),\n", " ((35, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.6212506595263343), mean=2.3854325367926594, variance=0.23457032730509317, skewness=0.2604446162510226, kurtosis=-0.3349483534626043)),\n", " ((30, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.385768838953655, variance=0.25181681694295693, skewness=0.28409827803721455, kurtosis=-0.3134374893329581)),\n", " ((40, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.385897158360537, variance=0.23011231408637273, skewness=0.189040499666452, kurtosis=-0.42683198131482225)),\n", " ((30, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.562558606945364), mean=2.3860009450938464, variance=0.23103302426248906, skewness=0.11204332345204361, kurtosis=-0.5490414886033808)),\n", " ((30, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3861556873839587, variance=0.22842621158531876, skewness=0.1290414047412115, kurtosis=-0.07336495164965884)),\n", " ((40, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.386188689872926, variance=0.23893696990231386, skewness=0.2967800859202916, kurtosis=-0.28282915584685053)),\n", " ((35, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3862119885427275, variance=0.22383472123068804, skewness=0.20644346651108006, kurtosis=-0.48916590957336537)),\n", " ((45, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.6212506595263343), mean=2.38632458240834, variance=0.23813819810123346, skewness=0.29652378295341514, kurtosis=-0.3672361799208925)),\n", " ((40, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3863502776797856, variance=0.22824894724073475, skewness=0.10516468851421981, kurtosis=-0.2594414493181243)),\n", " ((20, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.386627232078584, variance=0.22454729567294118, skewness=-0.07781410951642269, kurtosis=-0.3910997560468208)),\n", " ((35, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.38699418676085, variance=0.2577835077161205, skewness=0.2766783845585309, kurtosis=-0.29476357152580457)),\n", " ((35, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3872945246573107, variance=0.2244550722608443, skewness=0.250064567983947, kurtosis=-0.38361445269250094)),\n", " ((30, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3879080309744856, variance=0.22786302055822993, skewness=0.23902741677955017, kurtosis=-0.4056748763379603)),\n", " ((40, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.3880867134387906, variance=0.23677233140443266, skewness=0.2746348945969141, kurtosis=-0.21305949285139691)),\n", " ((45, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.388144182780888, variance=0.24112210638197287, skewness=0.23756936364283102, kurtosis=-0.2909234719207676)),\n", " ((40, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.388169259178502, variance=0.2272718361825683, skewness=0.14745033955498554, kurtosis=-0.3275151957127749)),\n", " ((35, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3883592992402862, variance=0.23027758760347003, skewness=0.296765878163557, kurtosis=-0.34861854619750154)),\n", " ((40, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5559859764406214), mean=2.3886814716207545, variance=0.23275117890602048, skewness=0.22684340335139833, kurtosis=-0.5936785076680828)),\n", " ((40, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3888673754435845, variance=0.23588824039933945, skewness=0.2881021402644574, kurtosis=-0.43324356622430704)),\n", " ((35, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.3889835348948454, variance=0.2569148258105363, skewness=0.2728444039629951, kurtosis=-0.22732955648449993)),\n", " ((30, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389402864252692, variance=0.2320812051198164, skewness=0.3130484267618166, kurtosis=-0.32815775713868645)),\n", " ((20, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3895829009651486, variance=0.22363397726813797, skewness=0.20584534226522705, kurtosis=-0.4096143639898431)),\n", " ((40, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3895841432380847, variance=0.24536500899154293, skewness=0.2599897242497131, kurtosis=-0.5167545964253102)),\n", " ((20, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3897760002726582, variance=0.2225776378698793, skewness=0.18575382982618197, kurtosis=-0.40539773457991934)),\n", " ((30, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389938416093227, variance=0.2285287200579085, skewness=0.33902253204824595, kurtosis=-0.32922349087198244)),\n", " ((30, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389975063842067, variance=0.22608397671643873, skewness=0.35686742328669135, kurtosis=-0.2997528177957811)),\n", " ((45, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6046958484870792), mean=2.390131984426462, variance=0.23610747813350952, skewness=0.22710720795114606, kurtosis=-0.5667969121056742)),\n", " ((30, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.3902302733627527, variance=0.2306056632981275, skewness=0.13261356954170908, kurtosis=-0.27069709723457525)),\n", " ((35, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.6212506595263343), mean=2.3904977600986737, variance=0.2507129621263109, skewness=0.21630205116480614, kurtosis=-0.3522875594025541)),\n", " ((35, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3906148388612825, variance=0.23612292896679513, skewness=0.06810877547812552, kurtosis=-0.30725869635176695)),\n", " ((35, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3908782343737354, variance=0.23036013923586704, skewness=0.28462534178743354, kurtosis=-0.38709455161808304)),\n", " ((45, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3914062869519253, variance=0.2393996096447048, skewness=0.3202604593262514, kurtosis=-0.2876938695682867)),\n", " ((20, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.391465728756091, variance=0.22394370233911176, skewness=0.1902320233296623, kurtosis=-0.41359222542326357)),\n", " ((35, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3917090310591567, variance=0.23053829906762635, skewness=0.2568710565884056, kurtosis=-0.36570028673332855)),\n", " ((30, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5455110669933947), mean=2.3917898129538484, variance=0.24078771833868412, skewness=0.19864827152416367, kurtosis=-0.5474088816866427)),\n", " ((40, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.3919928188754604, variance=0.23184626352990195, skewness=0.16634687967225528, kurtosis=-0.4035931059127029)),\n", " ((35, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7689006556014593), mean=2.3920055363342487, variance=0.25763993287424103, skewness=0.30817427852592283, kurtosis=-0.3347297549631598)),\n", " ((20, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.392065057934599, variance=0.23365257607206494, skewness=-0.04666906316083106, kurtosis=-0.474086583223138)),\n", " ((20, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3922155425953786, variance=0.22032420003471148, skewness=0.14340784428616138, kurtosis=-0.5120992116516523)),\n", " ((35, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3922777542401183, variance=0.23879292523118395, skewness=0.32127679699013006, kurtosis=-0.2637818045632927)),\n", " ((20, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.392327612986733, variance=0.22581619892859958, skewness=0.19485470098090532, kurtosis=-0.4507202547441471)),\n", " ((45, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3924854857851394, variance=0.23275307803748638, skewness=0.07500074075038048, kurtosis=-0.2935617253280385)),\n", " ((35, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.39275698546612, variance=0.257313874546351, skewness=0.2951195589126923, kurtosis=-0.32469376243371917)),\n", " ((40, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3930129707845498, variance=0.23215066111872443, skewness=0.2989655069270995, kurtosis=-0.2817878628513344)),\n", " ((35, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.576250985624627), mean=2.3937824872694407, variance=0.2376169204304254, skewness=0.26069901598295314, kurtosis=-0.40435481472120305)),\n", " ((30, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3941387003152848, variance=0.23003758231830418, skewness=0.21058066331754136, kurtosis=-0.379609944610138)),\n", " ((40, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3944423590294193, variance=0.2465065536857468, skewness=0.2721538250896667, kurtosis=-0.4792545836240327)),\n", " ((35, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3945732197549288, variance=0.23483766651871996, skewness=0.16196716664718905, kurtosis=-0.10484682014817315)),\n", " ((35, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7689006556014593), mean=2.394708380809293, variance=0.24331914134325108, skewness=0.3104609785236659, kurtosis=-0.34468360619940963)),\n", " ((40, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3948665465964374, variance=0.23229474512206372, skewness=0.1883658604164852, kurtosis=-0.3117502418808762)),\n", " ((20, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.270472247990643, 3.6261780809782476), mean=2.395570462065189, variance=0.22346972511382623, skewness=0.11329897943777814, kurtosis=-0.3234502459445663)),\n", " ((40, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3957542543079233, variance=0.23310121388075183, skewness=0.30095341959348654, kurtosis=-0.2942558792821828)),\n", " ((45, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.396039294508347, variance=0.23706545805278192, skewness=0.2683386111757052, kurtosis=-0.43250740115100994)),\n", " ((25, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.396406456777108, variance=0.21087635212900227, skewness=0.3024016257197271, kurtosis=-0.40876460192220465)),\n", " ((35, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.397308803810493, variance=0.21627613395654613, skewness=0.3096471084250271, kurtosis=-0.41018903098186943)),\n", " ((30, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3975186504066417, variance=0.22094968513618016, skewness=0.07480434328399128, kurtosis=-0.16335538209499267)),\n", " ((35, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3975590366723187, variance=0.24904851847767226, skewness=0.3568965017957376, kurtosis=-0.4302411959545829)),\n", " ((35, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976350502222, variance=0.23944715281813153, skewness=0.17912464319890165, kurtosis=-0.2781964142653237)),\n", " ((40, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976595418096966, variance=0.23818181554736112, skewness=0.2784549893503289, kurtosis=-0.3637520804075818)),\n", " ((40, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3978819990437974, variance=0.23997789669492398, skewness=0.24892265230252733, kurtosis=-0.4017673562738979)),\n", " ((30, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.397930773143809, variance=0.22641718268691954, skewness=0.17481864720402482, kurtosis=-0.4152277535960023)),\n", " ((45, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3985213041603988, variance=0.24132064938614203, skewness=0.32115251612013973, kurtosis=-0.17855020892334617)),\n", " ((25, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.398615207826399, variance=0.20887753685860447, skewness=0.3187548013407416, kurtosis=-0.4229798846403865)),\n", " ((35, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3986405901311167, variance=0.24608385998671142, skewness=0.3549449464418198, kurtosis=-0.4001901870073876)),\n", " ((45, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.3993612139561167, variance=0.24377787252097266, skewness=0.22483344276706962, kurtosis=-0.4970600005238177)),\n", " ((25, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.3997149663071773, variance=0.21211940441425559, skewness=0.3057379221458508, kurtosis=-0.4542345695303873)),\n", " ((35, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.400215914906969, variance=0.2465940458980044, skewness=0.24000447703882694, kurtosis=-0.2886091250801641)),\n", " ((35, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.401718522915244, variance=0.21725510948778534, skewness=0.32112902558791273, kurtosis=-0.397168495428879)),\n", " ((25, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.401775050693766, variance=0.21072810372063794, skewness=0.29902784407963034, kurtosis=-0.4643048528579099)),\n", " ((35, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.402065366133696, variance=0.21630799827125896, skewness=0.3496875784127574, kurtosis=-0.38042501490776415)),\n", " ((40, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.402673983236185, variance=0.2481837073735338, skewness=0.30838627997579465, kurtosis=-0.244559942461954)),\n", " ((40, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403858377352788, variance=0.24749869146926223, skewness=0.3140850115861441, kurtosis=-0.528785556311719)),\n", " ((40, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403932248288499, variance=0.2487742084683387, skewness=0.3381611420772938, kurtosis=-0.5165431694838332)),\n", " ((45, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.405068165571497, variance=0.24646178290038326, skewness=0.2875176572527761, kurtosis=-0.34540566014415486)),\n", " ((45, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.4051959917035837, variance=0.2450394814984553, skewness=0.23972419985337953, kurtosis=-0.4682877238864682)),\n", " ((35, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.405989752988432, variance=0.212938956410703, skewness=0.24447258131202554, kurtosis=-0.5271312715451346)),\n", " ((45, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406087392078327, variance=0.2580389356909127, skewness=0.29136942528153364, kurtosis=-0.29872794085361143)),\n", " ((35, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.406311167879119, variance=0.20781769633334632, skewness=0.2642635361018867, kurtosis=-0.3980318498632758)),\n", " ((35, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.4064772123154166, variance=0.21721735273717763, skewness=0.3199872090051091, kurtosis=-0.41302195380383466)),\n", " ((40, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406839724428981, variance=0.2603261771691146, skewness=0.3032186102576703, kurtosis=-0.3173295824803919)),\n", " ((40, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.407634797501566, variance=0.24371594638017943, skewness=0.2557220507793175, kurtosis=-0.15715980564058407)),\n", " ((45, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.4076371514178407, variance=0.24068682096802357, skewness=0.25787115006032574, kurtosis=-0.23278661069579876)),\n", " ((40, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.4077533351492275, variance=0.25320749575495544, skewness=0.2812107479281849, kurtosis=-0.4215619870544707)),\n", " ((25, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.4086178137902587, variance=0.23777282252768617, skewness=-0.018916891642525895, kurtosis=-0.21225144452113565)),\n", " ((35, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.408989352750845, variance=0.20993256278594338, skewness=0.20771311895378974, kurtosis=-0.5114565720950774)),\n", " ((25, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.409439772520956, variance=0.20459373614192744, skewness=0.2593292649700985, kurtosis=-0.43989262943361895)),\n", " ((40, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7717977793967843), mean=2.4103906271074926, variance=0.2622513620731259, skewness=0.28525878762522766, kurtosis=-0.11029258861434332)),\n", " ((25, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.410518885127268, variance=0.20550593327046196, skewness=0.2499724220330842, kurtosis=-0.4559192902865674)),\n", " ((45, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.410540212549997, variance=0.2396217756873817, skewness=0.26811073141192715, kurtosis=-0.4597579615051637)),\n", " ((25, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4108899944823015, variance=0.21198194312074198, skewness=0.24085917574500354, kurtosis=-0.46285314189413684)),\n", " ((45, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4112285800591664, variance=0.24211863480665663, skewness=0.3127233163899829, kurtosis=-0.14450499897441826)),\n", " ((35, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.411574201138517, variance=0.21578778205407273, skewness=0.23369600888365885, kurtosis=-0.2867155946425828)),\n", " ((40, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4118508808603045, variance=0.26022119050841064, skewness=0.3682029744859333, kurtosis=-0.41218842818502033)),\n", " ((45, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4124482991603977, variance=0.248005365526009, skewness=0.35134624885668897, kurtosis=-0.007504347438727521)),\n", " ((40, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4124700839687243, variance=0.25532818691020326, skewness=0.35812542624780425, kurtosis=-0.3749700141329608)),\n", " ((40, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.412501835410017, variance=0.2504678890342812, skewness=0.23613524618695558, kurtosis=-0.2089049468054367)),\n", " ((45, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.412821308581424, variance=0.24551095940694598, skewness=0.2924398069064522, kurtosis=-0.07143088476505399)),\n", " ((45, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.4131517076091358, variance=0.24066940267301618, skewness=0.26131189253213666, kurtosis=-0.49633037705736216)),\n", " ((45, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.414563246470491, variance=0.25243118749415416, skewness=0.32718722307293585, kurtosis=-0.1359966216084869)),\n", " ((25, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.3793123740968751, 3.7195088150342186), mean=2.4150328016456037, variance=0.22965534837872242, skewness=0.11268451204073322, kurtosis=-0.3615049140619653)),\n", " ((40, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.724442042751297), mean=2.415071859981076, variance=0.26238842898246284, skewness=0.2633242071284276, kurtosis=-0.2445055517514012)),\n", " ((45, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.416366874484952, variance=0.25708950801601954, skewness=0.3347859277339084, kurtosis=-0.41942847638872305)),\n", " ((45, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4179154555308684, variance=0.25567498182680215, skewness=0.32706170459005446, kurtosis=-0.4164166171364596)),\n", " ((45, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.86758500469272), mean=2.418061266506169, variance=0.2617243674783607, skewness=0.3253382576002433, kurtosis=-0.06503236957059055)),\n", " ((40, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7725779764160206), mean=2.4188623730204735, variance=0.26939255179747723, skewness=0.2788384614811644, kurtosis=-0.18288967289790214)),\n", " ((25, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.419065699428399, variance=0.21591709226718184, skewness=0.2058330707307416, kurtosis=-0.3095627615564962)),\n", " ((40, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.32972052526856, 3.7195088150342186), mean=2.4194208077125197, variance=0.22735926912395726, skewness=0.08411152670063812, kurtosis=-0.36976518164125993)),\n", " ((20, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.420607944985054, variance=0.22213448574790956, skewness=0.07369247045913387, kurtosis=-0.3204623682911296)),\n", " ((20, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.421994549752394, variance=0.22368579559809232, skewness=0.07766446553557484, kurtosis=-0.37632005523926537)),\n", " ((20, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.4224237705113114, variance=0.22372683062153817, skewness=0.07733561784015744, kurtosis=-0.3741548157951047)),\n", " ((15, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4237830561768567, variance=0.2129392320893983, skewness=-0.08376772978701559, kurtosis=-0.2716619126555728)),\n", " ((20, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.42404563849526, variance=0.2245852469553631, skewness=0.07550445732280753, kurtosis=-0.33011654543771085)),\n", " ((15, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250234741780834, variance=0.20909704825092762, skewness=-0.06777194176027414, kurtosis=-0.27625274525067756)),\n", " ((15, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250708875937916, variance=0.20883053530286166, skewness=-0.07138164793039069, kurtosis=-0.27475050421781333)),\n", " ((40, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.425222538586002, variance=0.2758842502293917, skewness=0.33399224512560255, kurtosis=-0.27223767809946775)),\n", " ((15, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4252546445919934, variance=0.2075275053147006, skewness=-0.07069685472679284, kurtosis=-0.24599343619613867)),\n", " ((15, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4254430153975073, variance=0.2139334478379118, skewness=-0.09371038896209857, kurtosis=-0.2511108814365266)),\n", " ((15, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.425698034696807, variance=0.21914834068542002, skewness=-0.06652177289464448, kurtosis=-0.3290842422376308)),\n", " ((30, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4262628008241856, variance=0.21831525479458277, skewness=0.22263078724693997, kurtosis=-0.48808525476895026)),\n", " ((40, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.4264197520081887, variance=0.27049178724551853, skewness=0.318896684817541, kurtosis=-0.2539636343376732)),\n", " ((45, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.4269174633995525, variance=0.25948011673042015, skewness=0.2999659622583222, kurtosis=-0.06571939509751745)),\n", " ((30, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4271405903951044, variance=0.23033291768137476, skewness=0.1631811023842746, kurtosis=-0.5828797184614842)),\n", " ((20, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.0487238909712968, 3.5605306008619384), mean=2.42725248440732, variance=0.240912705804242, skewness=-0.027445577863958494, kurtosis=-0.2989541627256087)),\n", " ((30, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4275089000561385, variance=0.22327316852087933, skewness=0.17835340203663447, kurtosis=-0.5397748344422348)),\n", " ((30, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.429406136909439, variance=0.21858010355750818, skewness=0.2000876621727689, kurtosis=-0.5077100524895188)),\n", " ((30, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4298489368050427, variance=0.21888683945586074, skewness=0.20063596567110728, kurtosis=-0.5176138357954292)),\n", " ((45, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.9162282140460087), mean=2.432424925734303, variance=0.27393959139490665, skewness=0.373169478138468, kurtosis=0.006635598228321271)),\n", " ((30, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.432507639466392, variance=0.2202826193726728, skewness=0.18051045766742263, kurtosis=-0.510441313183494)),\n", " ((25, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4332410339185766, variance=0.2491228344995086, skewness=-0.01261753485038881, kurtosis=-0.2375642444532593)),\n", " ((30, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.433282778268923, variance=0.22134877479162188, skewness=0.08024460232414445, kurtosis=-0.3947248921866984)),\n", " ((30, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4345756829691187, variance=0.22113762404024856, skewness=0.14077885323381606, kurtosis=-0.5306541114660988)),\n", " ((45, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.4346991775070603, variance=0.2619768174726941, skewness=0.3410719204865325, kurtosis=-0.1540722090125799)),\n", " ((25, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.43477140368765, variance=0.2432307330156745, skewness=0.05414275019076038, kurtosis=-0.36907364977950285)),\n", " ((45, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.434922529220423, variance=0.26497488722201096, skewness=0.3487337194660302, kurtosis=-0.13525730518814028)),\n", " ((20, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4355294363069016, variance=0.22844370985525814, skewness=0.04564180713737524, kurtosis=-0.4708318500785569)),\n", " ((25, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4356151895352216, variance=0.2545402506829705, skewness=-0.014315337290249688, kurtosis=-0.23985265026039926)),\n", " ((45, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9162282140460087), mean=2.436520062983622, variance=0.2731040454544077, skewness=0.3782243329854789, kurtosis=-0.038026118597547676)),\n", " ((25, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.4366509101256306, variance=0.24380395324511875, skewness=0.055522021814290286, kurtosis=-0.3745228956384281)),\n", " ((20, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4385881982087474, variance=0.22429320966832825, skewness=0.04935373950656626, kurtosis=-0.4198054833251561)),\n", " ((30, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.4082365099263328, 3.6261780809782476), mean=2.438737352325921, variance=0.22805965113028293, skewness=0.1429818073521336, kurtosis=-0.4555229006368986)),\n", " ((45, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.9643641477107323), mean=2.439372515277074, variance=0.2801733914247726, skewness=0.4032973011280843, kurtosis=0.004250745916431153)),\n", " ((15, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.441581110876761, variance=0.22090427842471203, skewness=-0.03683980809263881, kurtosis=-0.37562510182765596)),\n", " ((25, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.441599693225722, variance=0.25279254900744635, skewness=-0.049430446846301976, kurtosis=-0.23987523776176367)),\n", " ((20, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.2702796676835728, 3.6261780809782476), mean=2.4429333572379126, variance=0.2317515347098559, skewness=0.030755596810590057, kurtosis=-0.38533107980366)),\n", " ((35, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.3273505012242865, 3.6261780809782476), mean=2.4442174494359254, variance=0.22270434251888466, skewness=0.06292702158159418, kurtosis=-0.41536878070008587)),\n", " ((45, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4478526744775175, variance=0.2808214793691462, skewness=0.45054333863678114, kurtosis=-0.022699653611266246)),\n", " ((45, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4480861377214076, variance=0.28607402615953886, skewness=0.46832740215081625, kurtosis=-0.013192895201957988)),\n", " ((25, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.450736724300251, variance=0.255481258716427, skewness=-0.0916393786706801, kurtosis=-0.1946484500943786)),\n", " ((25, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.6268822743745335), mean=2.4567771801373315, variance=0.26685758977245705, skewness=-0.13106930458537935, kurtosis=-0.23530203208601286)),\n", " ((20, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.461412940346188, variance=0.22261073233179457, skewness=0.013054843197617676, kurtosis=-0.4048394269957174)),\n", " ((15, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.2259766995447088, 3.6261780809782476), mean=2.4641723063091154, variance=0.2159265085681024, skewness=-0.010343951137930279, kurtosis=-0.30470002743739455)),\n", " ((20, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.3251742111209324, 3.6261780809782476), mean=2.4703947628727625, variance=0.23315199031846423, skewness=-0.008292557084480892, kurtosis=-0.3128243051811035)),\n", " ((25, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.454272571633673, 3.6261780809782476), mean=2.473339889745894, variance=0.2233933922107472, skewness=0.017829377188284472, kurtosis=-0.4460671739578528)),\n", " ((30, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.1532672794627605, 3.6261780809782476), mean=2.4826310994850456, variance=0.23833070883681687, skewness=-0.05751429986772294, kurtosis=-0.27576009544920854)),\n", " ((25, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.280605540735518, 3.6261780809782476), mean=2.4906008151320926, variance=0.248685931657691, skewness=-0.15565003519874476, kurtosis=-0.2276167953822883)),\n", " ((15, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.66441849400411), mean=2.4991184440663403, variance=0.24559035270808768, skewness=-0.21622575184806114, kurtosis=-0.16251586619206204)),\n", " ((15, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.499706581469198, variance=0.2470089007313356, skewness=-0.24007391357504945, kurtosis=-0.10916436838477361)),\n", " ((15, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.502045838854079, variance=0.24585856220767538, skewness=-0.24920723946558182, kurtosis=-0.08569310815061959)),\n", " ((20, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.2827429687623266, 3.6506824010059473), mean=2.506670301022558, variance=0.2521143544916601, skewness=-0.09572168770654672, kurtosis=-0.3066998890567092)),\n", " ((15, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.507056878471702, variance=0.2500685023613651, skewness=-0.2455081158344568, kurtosis=-0.06388626860450675)),\n", " ((15, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5104525144988883, variance=0.258237832865022, skewness=-0.2592235877859567, kurtosis=-0.11572394159871147)),\n", " ((15, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.469281283482776, 3.6261780809782476), mean=2.5122558974038944, variance=0.2440282829471233, skewness=-0.12522757705704293, kurtosis=-0.48285495809641654)),\n", " ((20, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.3362673027274208, 3.6261780809782476), mean=2.514185778093504, variance=0.24260229343779774, skewness=-0.13507501269991837, kurtosis=-0.2711732535949607)),\n", " ((15, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.514454351924287, variance=0.2641923391146466, skewness=-0.2689525157400004, kurtosis=-0.16619392726022308)),\n", " ((15, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5172126530759686, variance=0.2807755090016337, skewness=-0.36304760594914764, kurtosis=-0.09794600674215204)),\n", " ((20, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.530455916115353, variance=0.277895703511729, skewness=-0.05581159504734332, kurtosis=-0.14130198306143615)),\n", " ((20, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.53252174939801, variance=0.2771156373425166, skewness=-0.07208813987939296, kurtosis=-0.1032661224771787)),\n", " ((20, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.533041000736945, variance=0.2762960956844567, skewness=-0.0518493542005356, kurtosis=-0.14641200049739478)),\n", " ((20, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5335298487193905, variance=0.277852958678635, skewness=-0.06653145571842661, kurtosis=-0.09408401959396251)),\n", " ((20, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5374620593106982, variance=0.2851672776793523, skewness=-0.13276170000964915, kurtosis=-0.08378526032506972)),\n", " ((15, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.280605540735518, 3.5448537261245736), mean=2.5393145110575412, variance=0.26180386621894675, skewness=-0.33957640223485785, kurtosis=-0.20905919146227614)),\n", " ((20, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5435791505822083, variance=0.28893024616184776, skewness=-0.16093508869298748, kurtosis=-0.08748636816873168)),\n", " ((15, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5464305599413226, variance=0.26795495027539973, skewness=-0.11469074924572617, kurtosis=-0.517523709902524)),\n", " ((15, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5485245903964615, variance=0.26862635121426187, skewness=-0.12194093267641852, kurtosis=-0.5241112363445328)),\n", " ((15, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.549041398461515, variance=0.2728931242961085, skewness=-0.15514934615427897, kurtosis=-0.505909867544049)),\n", " ((15, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5504816395542744, variance=0.2695209952065683, skewness=-0.12792988710767947, kurtosis=-0.5327642254880787)),\n", " ((15, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.554533540655466, variance=0.269587546488235, skewness=-0.10541615752597985, kurtosis=-0.5658111536655612)),\n", " ((25, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.280605540735518, 3.66441849400411), mean=2.56181421211636, variance=0.2601095214113185, skewness=-0.32632240498411913, kurtosis=-0.12005309910500106)),\n", " ((20, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.7118106452900848), mean=2.5648878081388222, variance=0.288071300712369, skewness=-0.1560031922606656, kurtosis=-0.110412069870931)),\n", " ((15, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.5666447985570335, variance=0.26843038578214184, skewness=-0.14633910677551268, kurtosis=-0.560670544130895)),\n", " ((15, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.688916269874316), mean=2.580109759802578, variance=0.2821922595914078, skewness=-0.15430239150351965, kurtosis=-0.5342614515549466)),\n", " ((20, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.2722541239378204, 3.8656333191299255), mean=2.5990973697762323, variance=0.26878379224054205, skewness=-0.08572147612813517, kurtosis=-0.12335277334607442)),\n", " ((15, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.3160117650280163, 3.6053806394700256), mean=2.601205819621667, variance=0.27289267701422726, skewness=-0.4952990792320176, kurtosis=-0.03882736218116145)),\n", " ((15, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.3898060381764048, 3.688916269874316), mean=2.609196850447406, variance=0.2716643462073717, skewness=-0.2642797975682462, kurtosis=-0.2848939064366114)),\n", " ((10, 7, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6441927741135607, variance=0.2594721361604495, skewness=-0.34778411539796744, kurtosis=-0.15659274696374137)),\n", " ((10, 8, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.644217801251108, variance=0.2594927958428506, skewness=-0.3477939113557061, kurtosis=-0.15687301104545748)),\n", " ((10, 9, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6445636740626393, variance=0.25947719317193274, skewness=-0.34986189727592815, kurtosis=-0.15558152824255256)),\n", " ((10, 6, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.646515331167755, variance=0.2625843266390292, skewness=-0.33129530187229717, kurtosis=-0.17352394193686305)),\n", " ((10, 5, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6471995488373823, variance=0.26381906477320244, skewness=-0.320655946681577, kurtosis=-0.169339018678476)),\n", " ((10, 4, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6498871800519215, variance=0.26492318590273994, skewness=-0.32446593956630665, kurtosis=-0.17512206191987056)),\n", " ((10, 3, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.660704229257497, variance=0.26893152564304285, skewness=-0.33487550920479664, kurtosis=-0.16293684332682234)),\n", " ((10, 8, 10),\n", " DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853)),\n", " ((10, 9, 10),\n", " DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853)),\n", " ((10, 7, 10),\n", " DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.667071442043656, variance=0.29269861161342803, skewness=-0.23300886540344137, kurtosis=-0.09841994971348544)),\n", " ((10, 6, 10),\n", " DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669214639548226, variance=0.2935420680896115, skewness=-0.25504270121568223, kurtosis=-0.05367597717982964)),\n", " ((10, 5, 10),\n", " DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669744008221317, variance=0.29841910879887634, skewness=-0.22225993120261048, kurtosis=-0.0472763227484263)),\n", " ((10, 4, 10),\n", " DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.676455843721493, variance=0.2937927870249197, skewness=-0.2234006371411064, kurtosis=-0.09048904303855432)),\n", " ((10, 2, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.681594986261088, variance=0.27842546188147577, skewness=-0.38497515584895964, kurtosis=-0.1392300490851719)),\n", " ((10, 3, 10),\n", " DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.683735530723601, variance=0.2893644793141911, skewness=-0.24196472267608216, kurtosis=-0.01272835493523905)),\n", " ((20, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.3423468584053975, 3.897319842199613), mean=2.684073393901844, variance=0.2807864899381598, skewness=-0.2364426189045562, kurtosis=-0.20265061882354019)),\n", " ((10, 2, 10),\n", " DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.703919186064029, variance=0.2910392479358703, skewness=-0.17569831800238767, kurtosis=-0.026657277400341073)),\n", " ((15, 8, 0),\n", " DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.704509705407346, variance=0.27797811695164676, skewness=-0.3262617881322571, kurtosis=0.09119239683428315)),\n", " ((15, 9, 0),\n", " DescribeResult(nobs=173, minmax=(1.3957580288394014, 3.9147376529767435), mean=2.7075434147500044, variance=0.27093066675680394, skewness=-0.25501352814444467, kurtosis=-0.08732045907999852)),\n", " ((15, 7, 0),\n", " DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.7078749214557574, variance=0.2795285538018334, skewness=-0.2666613713523985, kurtosis=0.0995119864201448)),\n", " ((15, 6, 0),\n", " DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.711894351727476, variance=0.278369156594265, skewness=-0.30086035835336405, kurtosis=0.2023964929072064)),\n", " ((15, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.2588005585814288, 4.191259803191946), mean=2.7124903538315728, variance=0.28517216483889607, skewness=-0.21273665971149405, kurtosis=-0.06564314346805888)),\n", " ((15, 5, 0),\n", " DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.7177283637337104, variance=0.2723959536913965, skewness=-0.33348323143825864, kurtosis=0.24648247402512613)),\n", " ((15, 4, 0),\n", " DescribeResult(nobs=173, minmax=(1.1184268068034193, 4.095658947639928), mean=2.728873529740259, variance=0.2771105516332162, skewness=-0.31565304601887006, kurtosis=0.26495658847041126)),\n", " ((15, 3, 0),\n", " DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7458533421389415, variance=0.2675799369586586, skewness=-0.1809451788868796, kurtosis=0.05540112858880075)),\n", " ((10, 1, 20),\n", " DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.832138275353508), mean=2.7507068339662806, variance=0.25787775042947936, skewness=-0.37508484769566325, kurtosis=-0.03210901516830633)),\n", " ((10, 1, 10),\n", " DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.756003469399469, variance=0.28989047602798246, skewness=-0.2945369702420912, kurtosis=-0.02369081827086239)),\n", " ((15, 2, 0),\n", " DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7671814146840363, variance=0.27029380686229254, skewness=-0.2091631733056473, kurtosis=0.0691644289164639)),\n", " ((15, 1, 0),\n", " DescribeResult(nobs=173, minmax=(1.4871436032569523, 4.281798203709453), mean=2.836863894959103, variance=0.2729460542498244, skewness=-0.20357139230435106, kurtosis=0.09873747800409838)),\n", " ((10, 6, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.886892246290024, variance=0.27395091325862625, skewness=-0.5656360125824968, kurtosis=0.7011024739716984)),\n", " ((10, 7, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n", " ((10, 8, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n", " ((10, 9, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n", " ((10, 5, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873477731939214, variance=0.2728824437420165, skewness=-0.5642008052784352, kurtosis=0.7097326337896264)),\n", " ((10, 4, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8886994646476283, variance=0.27163703068449624, skewness=-0.5564449875702622, kurtosis=0.7075542805925097)),\n", " ((10, 3, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.894245688555616, variance=0.26682389539585616, skewness=-0.5105429044672873, kurtosis=0.6087195136790573)),\n", " ((10, 2, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.9003120200331316, variance=0.261910349388631, skewness=-0.5119892469577535, kurtosis=0.6424781625590157)),\n", " ((10, 1, 0),\n", " DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.2688324308036), mean=2.918631217038793, variance=0.2655104551963414, skewness=-0.5301066567004962, kurtosis=0.6648724709751717)),\n", " ((10, 6, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n", " ((10, 7, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n", " ((10, 8, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n", " ((10, 9, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n", " ((10, 5, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9227759884362543, variance=0.24341675218182648, skewness=-0.1876547463116731, kurtosis=-0.1468739255370366)),\n", " ((10, 4, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9231854043619783, variance=0.24314027663665091, skewness=-0.1893019934136203, kurtosis=-0.14083355822754395)),\n", " ((10, 3, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.92518079836633, variance=0.24887539377691065, skewness=-0.14225847098489716, kurtosis=-0.08017538671679647)),\n", " ((10, 2, 30),\n", " DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.931984625553082, variance=0.2494577589000683, skewness=-0.11959221264031848, kurtosis=-0.13815791364404895)),\n", " ((10, 1, 30),\n", " DescribeResult(nobs=173, minmax=(1.7501416446074862, 4.191259803191946), mean=2.961261234054409, variance=0.2398959796022379, skewness=-0.1092311580556235, kurtosis=-0.26051346232955686))]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 7 } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }