Merge branch 'MOORA' into 'master'

Untitled.ipynb

+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}