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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os,django\n",
"import pandas as pd\n",
"from orm.models import Siswa,Kelas,Karakter\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Siswa' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-eabc7ddc4584>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Kelas\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSiswa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mkl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mKelas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mListKelas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'Siswa' is not defined"
]
}
],
"source": [
"# Kelas\n",
"sw=Siswa.objects.all()\n",
"kl=Kelas.objects.all()\n",
"\n",
"def ListKelas(sw):\n",
" if len(sw)>0:\n",
" cols = ['Nilai']\n",
" \n",
" kel ={\n",
" cols[0] : [int(a.kelass.nilai) for a in sw],\n",
" }\n",
" dfkel = pd.DataFrame(data=kel)\n",
" return dfkel\n",
" else:\n",
" return[]\n",
"\n",
"def Hasil_Kelas():\n",
" kl=ListKelas(sw)\n",
" b = 0\n",
" tampung=[]\n",
" for y in range(len(sw)):\n",
" a=(math.pow(kl.Nilai[y],2))\n",
" b = b+a\n",
" for i in range(len(sw)):\n",
" s = kl.Nilai[i]\n",
" ad=s/(math.sqrt(b))\n",
" tampung.append(ad)\n",
" \n",
" swa={'nama':[a.nama for a in sw]}\n",
" \n",
" if len(sw)>0:\n",
" cols = ['Jenjang']\n",
" \n",
" kel ={\n",
" cols[0] : [str(a.kelass.jenjang) for a in sw],\n",
" }\n",
" dfkel = pd.DataFrame(data=kel)\n",
" \n",
" \n",
" dfswa= pd.DataFrame(data=swa)\n",
" Kelas=pd.DataFrame(data=tampung,columns=['Nilai'])\n",
" new = pd.concat([dfswa,dfkel, Kelas], axis=1)\n",
" return new\n",
"\n",
"\n",
"def HasilKelas_Pembobotan():\n",
" b=Hasil_Kelas()\n",
" lst=list(b)\n",
" y=0\n",
" d=[]\n",
" lst\n",
" \n",
" for i in range(len(b)):\n",
" y =0.3*b.Nilai[i]\n",
" d.append(y)\n",
" pb=pd.DataFrame(d,columns=['Nilai'])\n",
" swa={'nama':[a.nama for a in sw]}\n",
" dfswa= pd.DataFrame(data=swa)\n",
" # Kelas=pd.DataFrame(data=tampung,columns=['Nilai'])\n",
" new = pd.concat([dfswa, pb], axis=1)\n",
" return new"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"HasilKelas_Pembobotan()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Hasil_Kelas()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def ListKelasJn(sw):\n",
" if len(sw)>0:\n",
" cols = ['Jenjang']\n",
" \n",
" kel ={\n",
" cols[0] : [str(a.kelass.jenjang) for a in sw],\n",
" }\n",
" dfkel = pd.DataFrame(data=kel)\n",
" return dfkel\n",
" else:\n",
" return[]\n",
"ListKelasJn(sw)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def Bobot_MTK():\n",
" b=Hasil_Kelas()\n",
" lst=list(b)\n",
" y=0\n",
" d=[]\n",
" lst\n",
" for i in range(len(lst)):\n",
" y =0.3*lst[i]\n",
" d.append(y)\n",
" pb=pd.DataFrame(d,columns=['Nilai'])\n",
" swa={'nama':[a.nama for a in sw]}\n",
" dfswa= pd.DataFrame(data=swa)\n",
" new = pd.concat([dfswa, pb], axis=1)\n",
" return new\n",
"\n",
"Bobot_MTK()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"krt=Karakter.objects.all()\n",
"def ListAkademik(krt):\n",
" if len(krt)>0:\n",
" cols = ['matapelajaran','nilai']\n",
" kel ={\n",
" cols[0] : [str(a.matapelajaran) for a in ak],\n",
" cols[1] : [int(a.nilai) for a in ak],\n",
" }\n",
" dfkel = pd.DataFrame(data=kel)\n",
" return dfkel\n",
" else:\n",
" return[]\n",
"\n",
"ListAkademik(ak)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def ListKecerdasan(krywn):\n",
" if len(krywn)>0:\n",
" target = [4, 3, 4, 5, 3]\n",
" cols = ['sistematika_berfikir', 'konsentrasi', 'logika_praktis','imajinasi_kreatif','antisipasi']\n",
"\n",
" krn = {'nama': [a.nama for a in krywn]}\n",
" dfkrn = pd.DataFrame(data=krn)\n",
"\n",
" kec = {\n",
" cols[0] : [int(a.kecerdasans.sistematika_berfikir) for a in krywn],\n",
" cols[1] : [int(a.kecerdasans.konsentrasi) for a in krywn],\n",
" cols[2] : [int(a.kecerdasans.logika_praktis) for a in krywn],\n",
" cols[3] : [int(a.kecerdasans.imajinasi_kreatif) for a in krywn],\n",
" cols[4] : [int(a.kecerdasans.antisipasi) for a in krywn],\n",
" }\n",
" dfkec = pd.DataFrame(data=kec)\n",
"\n",
" gap = get_gap(dfkec, target)\n",
" pb = pembobotan(gap, cols)\n",
" new = pd.concat([dfkrn, pb], axis=1)\n",
" return new\n",
" else:\n",
" return []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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