Anpassung des Pfades
klara
parent
c815233ed5
commit
6a98b0a955
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@ -11,7 +11,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -19,67 +19,130 @@
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"import pickle\n",
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"import sys\n",
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"\n",
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"\n",
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"from scipy.stats import chi2_contingency\n",
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"from data_helper import *\n"
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"sys.path.append('../scripts')\n",
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"import data_helper\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading SB\n",
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"Length of SB: 50\n",
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"Reading AFIB\n",
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"Length of AFIB: 27\n",
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"Reading GSVT\n",
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"Length of GSVT: 0\n",
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"Reading AFIB\n",
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"Reading SR\n",
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"Length of SR: 13\n",
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"Chi-Square Statistic: 38.266574797751275\n",
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"P-value: 0.0004730210823940083\n",
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"Chi-Square Statistic for SB in 60-70 vs others: 1.4858035714285718\n",
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"P-value for SB in 60-70 vs others: 0.22286870264719977\n"
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"Reading SB\n",
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"Number of patients per category:\n",
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"GSVT: 0\n",
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"AFIB: 27\n",
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"SR: 13\n",
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"SB: 50\n"
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]
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}
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],
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"source": [
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"#path = \"C:/Studium/dsa/data\"\n",
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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"path = \"C:/Users/klara/projects/DSA/data\"\n",
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"data = data_helper.load_data(only_demographic=False)\n",
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"\n",
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"categories_dict = {\n",
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"'SB': [426177001],\n",
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"'AFIB': [164889003, 164890007],\n",
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"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"'SR': [426783006, 427393009]\n",
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"}\n",
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"print(\"Number of patients per category:\")\n",
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"for cat_name in data.keys():\n",
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" print(f\"{cat_name}: {len(data[cat_name])}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading GSVT\n",
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"Reading AFIB\n",
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"Reading SR\n",
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"Reading SB\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "All arrays must be of the same length",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[27], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m data_org \u001b[38;5;241m=\u001b[39m data_helper\u001b[38;5;241m.\u001b[39mload_data(only_demographic\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m----> 3\u001b[0m df_dgc \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_org\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\frame.py:767\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 761\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m 762\u001b[0m data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m 763\u001b[0m )\n\u001b[0;32m 765\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 766\u001b[0m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 767\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 768\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m 769\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 500\u001b[0m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m 501\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 116\u001b[0m index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:677\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 675\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 681\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 682\u001b[0m )\n",
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"\u001b[1;31mValueError\u001b[0m: All arrays must be of the same length"
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]
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}
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],
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"source": [
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"data_org = data_helper.load_data(only_demographic=False)\n",
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"\n",
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"data = {}\n",
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"for cat_name in categories_dict.keys():\n",
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" print(f\"Reading {cat_name}\")\n",
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" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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" records = pickle.load(f)\n",
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" data[cat_name] = records\n",
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" print(f\"Length of {cat_name}: {len(records)}\")\n",
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"df_dgc = pd.DataFrame(data_org)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'df_dgc' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[21], line 36\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# #path = \"C:/Studium/dsa/data\"\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# #path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# path = \"C:/Users/klara/projects/DSA/data\"\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 33\u001b[0m \n\u001b[0;32m 34\u001b[0m \u001b[38;5;66;03m# Change from group to category\u001b[39;00m\n\u001b[0;32m 35\u001b[0m age_categories \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m30\u001b[39m, \u001b[38;5;241m40\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m60\u001b[39m, \u001b[38;5;241m70\u001b[39m, \u001b[38;5;241m80\u001b[39m, \u001b[38;5;241m90\u001b[39m]\n\u001b[1;32m---> 36\u001b[0m df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage_group\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mcut(\u001b[43mdf_dgc\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m], bins\u001b[38;5;241m=\u001b[39mage_categories)\n\u001b[0;32m 37\u001b[0m corr_matrix_age_diag\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mcrosstab(df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage_group\u001b[39m\u001b[38;5;124m'\u001b[39m], df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiag\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# Chi-square test\u001b[39;00m\n",
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"\u001b[1;31mNameError\u001b[0m: name 'df_dgc' is not defined"
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]
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}
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],
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"source": [
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"# #path = \"C:/Studium/dsa/data\"\n",
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"# #path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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"# path = \"C:/Users/klara/projects/DSA/data\"\n",
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"\n",
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"data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
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"for cat_name, records in data.items():\n",
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" for record in records:\n",
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" age = record.comments[0].split(' ')[1]\n",
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" sex = record.comments[1].split(' ')[1]\n",
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" if age == 'NaN' or sex == 'NaN':\n",
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" continue\n",
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" # cut Age: from alter string \n",
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" data_demographic['age'].append(int(age))\n",
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" data_demographic['diag'].append(cat_name)\n",
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" data_demographic['gender'].append(sex)\n",
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"# categories_dict = {\n",
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"# 'SB': [426177001],\n",
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"# 'AFIB': [164889003, 164890007],\n",
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"# 'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"# 'SR': [426783006, 427393009]\n",
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"# }\n",
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"\n",
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"df_dgc = pd.DataFrame(data_demographic)\n",
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"# data = {}\n",
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"# for cat_name in categories_dict.keys():\n",
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"# print(f\"Reading {cat_name}\")\n",
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"# with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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"# records = pickle.load(f)\n",
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"# data[cat_name] = records\n",
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"# print(f\"Length of {cat_name}: {len(records)}\")\n",
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"\n",
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"# data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
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"# for cat_name, records in data.items():\n",
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"# for record in records:\n",
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"# age = record.comments[0].split(' ')[1]\n",
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"# sex = record.comments[1].split(' ')[1]\n",
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"# if age == 'NaN' or sex == 'NaN':\n",
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"# continue\n",
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"# # cut Age: from alter string \n",
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"# data_demographic['age'].append(int(age))\n",
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"# data_demographic['diag'].append(cat_name)\n",
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"# data_demographic['gender'].append(sex)\n",
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"\n",
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"# df_dgc = pd.DataFrame(data_demographic)\n",
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"\n",
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"# Change from group to category\n",
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"age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
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Loading…
Reference in New Issue