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factor优化（暂存版）

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liaozhaorun
2025-10-14 09:44:46 +08:00
parent 44315b2c76
commit 7862b9739a
9 changed files with 804 additions and 4427 deletions
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main/train/Classify/Classify2.ipynb
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main/train/Classify2_load_model.ipynb
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@@ -68,56 +68,28 @@
"name": "stdout",
"output_type": "stream",
"text": [
"daily data\n",
"daily basic\n",
"inner merge on ['ts_code', 'trade_date']\n",
"stk limit\n",
"left merge on ['ts_code', 'trade_date']\n",
"money flow\n",
"left merge on ['ts_code', 'trade_date']\n",
"cyq perf\n",
"left merge on ['ts_code', 'trade_date']\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 9162612 entries, 0 to 9162611\n",
"Data columns (total 33 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object \n",
" 1 trade_date datetime64[ns]\n",
" 2 open float64 \n",
" 3 close float64 \n",
" 4 high float64 \n",
" 5 low float64 \n",
" 6 vol float64 \n",
" 7 amount float64 \n",
" 8 pct_chg float64 \n",
" 9 turnover_rate float64 \n",
" 10 pe_ttm float64 \n",
" 11 circ_mv float64 \n",
" 12 total_mv float64 \n",
" 13 volume_ratio float64 \n",
" 14 is_st bool \n",
" 15 up_limit float64 \n",
" 16 down_limit float64 \n",
" 17 buy_sm_vol float64 \n",
" 18 sell_sm_vol float64 \n",
" 19 buy_lg_vol float64 \n",
" 20 sell_lg_vol float64 \n",
" 21 buy_elg_vol float64 \n",
" 22 sell_elg_vol float64 \n",
" 23 net_mf_vol float64 \n",
" 24 his_low float64 \n",
" 25 his_high float64 \n",
" 26 cost_5pct float64 \n",
" 27 cost_15pct float64 \n",
" 28 cost_50pct float64 \n",
" 29 cost_85pct float64 \n",
" 30 cost_95pct float64 \n",
" 31 weight_avg float64 \n",
" 32 winner_rate float64 \n",
"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
"memory usage: 2.2+ GB\n",
"None\n"
"daily data\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m read_and_merge_h5_data\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mdaily data\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m df = \u001b[43mread_and_merge_h5_data\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/mnt/d/PyProject/NewStock/data/daily_data.h5\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdaily_data\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mts_code\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtrade_date\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mopen\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mclose\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mhigh\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlow\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvol\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mamount\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpct_chg\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mdaily basic\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 9\u001b[39m df = read_and_merge_h5_data(\u001b[33m'\u001b[39m\u001b[33m/mnt/d/PyProject/NewStock/data/daily_basic.h5\u001b[39m\u001b[33m'\u001b[39m, key=\u001b[33m'\u001b[39m\u001b[33mdaily_basic\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 10\u001b[39m columns=[\u001b[33m'\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mturnover_rate\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mpe_ttm\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcirc_mv\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mvolume_ratio\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 11\u001b[39m \u001b[33m'\u001b[39m\u001b[33mis_st\u001b[39m\u001b[33m'\u001b[39m], df=df, join=\u001b[33m'\u001b[39m\u001b[33minner\u001b[39m\u001b[33m'\u001b[39m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m/mnt/d/PyProject/NewStock/main/utils/utils.py:14\u001b[39m, in \u001b[36mread_and_merge_h5_data\u001b[39m\u001b[34m(h5_filename, key, columns, df, join, on, prefix)\u001b[39m\n\u001b[32m 11\u001b[39m processed_columns.append(col)\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# 从 HDF5 文件读取数据，选择需要的列\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m data = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_hdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh5_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprocessed_columns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# 修改列名，如果列名以前有 _，加上 _\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m data.columns:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:452\u001b[39m, in \u001b[36mread_hdf\u001b[39m\u001b[34m(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 448\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mkey must be provided when HDF5 \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 449\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mfile contains multiple datasets.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m )\n\u001b[32m 451\u001b[39m key = candidate_only_group._v_pathname\n\u001b[32m--> \u001b[39m\u001b[32m452\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 453\u001b[39m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 454\u001b[39m \u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 456\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 457\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 458\u001b[39m \u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m=\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 459\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 460\u001b[39m \u001b[43m \u001b[49m\u001b[43mauto_close\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauto_close\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 461\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 462\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mLookupError\u001b[39;00m):\n\u001b[32m 463\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, HDFStore):\n\u001b[32m 464\u001b[39m \u001b[38;5;66;03m# if there is an error, close the store if we opened it.\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:906\u001b[39m, in \u001b[36mHDFStore.select\u001b[39m\u001b[34m(self, key, where, start, stop, columns, iterator, chunksize, auto_close)\u001b[39m\n\u001b[32m 892\u001b[39m \u001b[38;5;66;03m# create the iterator\u001b[39;00m\n\u001b[32m 893\u001b[39m it = TableIterator(\n\u001b[32m 894\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 895\u001b[39m s,\n\u001b[32m (...)\u001b[39m\u001b[32m 903\u001b[39m auto_close=auto_close,\n\u001b[32m 904\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m906\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mit\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:2029\u001b[39m, in \u001b[36mTableIterator.get_result\u001b[39m\u001b[34m(self, coordinates)\u001b[39m\n\u001b[32m 2026\u001b[39m where = \u001b[38;5;28mself\u001b[39m.where\n\u001b[32m 2028\u001b[39m \u001b[38;5;66;03m# directly return the result\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2029\u001b[39m results = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2030\u001b[39m \u001b[38;5;28mself\u001b[39m.close()\n\u001b[32m 2031\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:890\u001b[39m, in \u001b[36mHDFStore.select.<locals>.func\u001b[39m\u001b[34m(_start, _stop, _where)\u001b[39m\n\u001b[32m 889\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfunc\u001b[39m(_start, _stop, _where):\n\u001b[32m--> \u001b[39m\u001b[32m890\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ms\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_start\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_stop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_where\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:4631\u001b[39m, in \u001b[36mAppendableFrameTable.read\u001b[39m\u001b[34m(self, where, columns, start, stop)\u001b[39m\n\u001b[32m 4628\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.infer_axes():\n\u001b[32m 4629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4631\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_axes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4633\u001b[39m info = (\n\u001b[32m 4634\u001b[39m \u001b[38;5;28mself\u001b[39m.info.get(\u001b[38;5;28mself\u001b[39m.non_index_axes[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m], {})\n\u001b[32m 4635\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m.non_index_axes)\n\u001b[32m 4636\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[32m 4637\u001b[39m )\n\u001b[32m 4639\u001b[39m inds = [i \u001b[38;5;28;01mfor\u001b[39;00m i, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m.axes) \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m.index_axes[\u001b[32m0\u001b[39m]]\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:3818\u001b[39m, in \u001b[36mTable._read_axes\u001b[39m\u001b[34m(self, where, start, stop)\u001b[39m\n\u001b[32m 3816\u001b[39m \u001b[38;5;66;03m# create the selection\u001b[39;00m\n\u001b[32m 3817\u001b[39m selection = Selection(\u001b[38;5;28mself\u001b[39m, where=where, start=start, stop=stop)\n\u001b[32m-> \u001b[39m\u001b[32m3818\u001b[39m values = \u001b[43mselection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3820\u001b[39m results = []\n\u001b[32m 3821\u001b[39m \u001b[38;5;66;03m# convert the data\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/io/pytables.py:5397\u001b[39m, in \u001b[36mSelection.select\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 5395\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.coordinates \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 5396\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.table.table.read_coordinates(\u001b[38;5;28mself\u001b[39m.coordinates)\n\u001b[32m-> \u001b[39m\u001b[32m5397\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/tables/table.py:2083\u001b[39m, in \u001b[36mTable.read\u001b[39m\u001b[34m(self, start, stop, step, field, out)\u001b[39m\n\u001b[32m 2077\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m 2079\u001b[39m start, stop, step = \u001b[38;5;28mself\u001b[39m._process_range(\n\u001b[32m 2080\u001b[39m start, stop, step, warn_negstep=\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 2081\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m2083\u001b[39m arr = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfield\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2084\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m internal_to_flavor(arr, \u001b[38;5;28mself\u001b[39m.flavor)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/tables/table.py:1989\u001b[39m, in \u001b[36mTable._read\u001b[39m\u001b[34m(self, start, stop, step, field, out)\u001b[39m\n\u001b[32m 1985\u001b[39m \u001b[38;5;66;03m# Call the routine to fill-up the resulting array\u001b[39;00m\n\u001b[32m 1986\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m step == \u001b[32m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m field:\n\u001b[32m 1987\u001b[39m \u001b[38;5;66;03m# This optimization works three times faster than\u001b[39;00m\n\u001b[32m 1988\u001b[39m \u001b[38;5;66;03m# the row._fill_col method (up to 170 MB/s on a pentium IV @ 2GHz)\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1989\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_records\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1990\u001b[39m \u001b[38;5;66;03m# Warning!: _read_field_name should not be used until\u001b[39;00m\n\u001b[32m 1991\u001b[39m \u001b[38;5;66;03m# H5TBread_fields_name in tableextension will be finished\u001b[39;00m\n\u001b[32m 1992\u001b[39m \u001b[38;5;66;03m# F. Alted 2005/05/26\u001b[39;00m\n\u001b[32m 1993\u001b[39m \u001b[38;5;66;03m# XYX Ho implementem per a PyTables 2.0??\u001b[39;00m\n\u001b[32m 1994\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m field \u001b[38;5;129;01mand\u001b[39;00m step > \u001b[32m15\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[32m0\u001b[39m:\n\u001b[32m 1995\u001b[39m \u001b[38;5;66;03m# For step>15, this seems to work always faster than row._fill_col.\u001b[39;00m\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
@@ -154,7 +126,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "cac01788dac10678",
"metadata": {
"ExecuteTime": {
@@ -222,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "c4e9e1d31da6dba6",
"metadata": {
"ExecuteTime": {
@@ -322,7 +294,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "a735bc02ceb4d872",
"metadata": {
"ExecuteTime": {
@@ -338,7 +310,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "53f86ddc0677a6d7",
"metadata": {
"ExecuteTime": {
@@ -405,7 +377,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "dbe2fd8021b9417f",
"metadata": {
"ExecuteTime": {
@@ -433,7 +405,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "85c3e3d0235ffffa",
"metadata": {
"ExecuteTime": {
@@ -465,7 +437,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"id": "92d84ce15a562ec6",
"metadata": {
"ExecuteTime": {
@@ -722,7 +694,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "b87b938028afa206",
"metadata": {
"ExecuteTime": {
@@ -760,7 +732,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "f4f16d63ad18d1bc",
"metadata": {
"ExecuteTime": {
@@ -986,7 +958,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "40e6b68a91b30c79",
"metadata": {
"ExecuteTime": {
@@ -1306,7 +1278,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"id": "47c12bb34062ae7a",
"metadata": {
"ExecuteTime": {
@@ -1340,7 +1312,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "29221dde",
"metadata": {},
"outputs": [
@@ -1383,7 +1355,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "03ee5daf",
"metadata": {},
"outputs": [],
@@ -1396,7 +1368,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"id": "b76ea08a",
"metadata": {},
"outputs": [
@@ -1621,7 +1593,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"id": "3ff2d1c5",
"metadata": {},
"outputs": [],
@@ -1762,7 +1734,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"id": "a5bbb8be",
"metadata": {},
"outputs": [
@@ -1787,7 +1759,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": null,
"id": "5d1522a7538db91b",
"metadata": {
"ExecuteTime": {
@@ -1825,7 +1797,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": null,
"id": "09b1799e",
"metadata": {},
"outputs": [
@@ -1847,7 +1819,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": null,
"id": "e53b209a",
"metadata": {},
"outputs": [