From 63fae374238969c0473c9edbb217b875111406a3 Mon Sep 17 00:00:00 2001 From: Hebry Yanisa Manihuruk <hebrymanihuruk10@gmail.com> Date: Mon, 22 Jun 2020 10:23:23 +0700 Subject: [PATCH] moora --- Untitled.ipynb | 254 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 Untitled.ipynb diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..af66832 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,254 @@ +{ + "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 +} -- libgit2 0.27.1