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taruma/taruma_li05_analisis_frekuensi_uji_distribusi.ipynb

Last active April 6, 2022 13:32
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taruma_li05_analisis_frekuensi_uji_distribusi.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_LI05_analisis_frekuensi_uji_distribusi.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyOdmza2BBLgv8Qb4vIc/sZg",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/taruma/560eecd2e55b5fdc3a357b6d3ac4f20c/taruma_li05_analisis_frekuensi_uji_distribusi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Analisis Frekuensi dan Uji Kecocokan Distribusi\n",
"\n",
"Buku (_notebook_) ini akan membandingkan hasil perhitungan analisis frekuensi empat distribusi yaitu distribusi normal, log normal, log pearson tipe III, dan gumbel dan menerapkan uji kecocokan distribusi (kolmogorov-smirnov dan chisquare). Buku ini lebih mengutamakan implementasi penggunaan, dan mempresentasikan hasil dari masing-masing analisis. **Buku ini tidak mengikuti teori atau pekerjaan aktual, buku ini hanya mendemonstrasikan fitur yang tersedia di hidrokit 0.4.0**.\n",
"\n",
"Untuk informasi mengenai hidrokit 0.4.0 dapat mengunjungi [repository di github](https://github.com/hidrokit/hidrokit) atau [situs resminya](https://hidrokit.github.io/hidrokit/)."
],
"metadata": {
"id": "Gy-4HQrdqljH"
}
},
{
"cell_type": "markdown",
"source": [
"# PENGATURAN BUKU\n",
"\n",
"Buku ini dikembangkan menggunakan Google Colab dengan fleksibilitas dalam penggunaan _runtime_ lokal ataupun _google colab_. "
],
"metadata": {
"id": "ZXJIGqvlshkH"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bpGB_hsZpP0c",
"outputId": "762b8bf5-7744-4fde-a313-05848e9a9afc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"RUNTIME: LOKAL\n"
]
}
],
"source": [
"#@title PENGGUNAAN RUNTIME (GOOGLE COLAB/LOKAL) {display-mode:\"form\", run:\"auto\"}\n",
"# Using Google Colab or Local System\n",
"_IS_LOCAL = True #@param {type:\"boolean\"}\n",
"\n",
"(print('RUNTIME: LOKAL') if _IS_LOCAL else print('RUNTIME: GOOGLE COLAB'))"
]
},
{
"cell_type": "markdown",
"source": [
"# INISIASI"
],
"metadata": {
"id": "YePtwWmis8mY"
}
},
{
"cell_type": "code",
"source": [
"from pathlib import Path\n",
"if _IS_LOCAL:\n",
" _LOCAL_DIRECTORY = './laporan_implementasi/li5' #@param {type:\"string\"}\n",
" _directory = Path(_LOCAL_DIRECTORY) \n",
"else:\n",
" from google.colab import drive\n",
" drive.mount('/content/gdrive', force_remount=True)\n",
" _COLAB_DIRECTORY = Path('/content/gdrive/My Drive/Colab Notebooks/')\n",
" _CLOUD_DIRECTORY = './laporan_implementasi/li5' #@param {type:\"string\"}\n",
" _directory = _COLAB_DIRECTORY / _CLOUD_DIRECTORY"
],
"metadata": {
"id": "OhF9AXlms9fL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"try:\n",
" import hidrokit\n",
"except ModuleNotFoundError:\n",
" !pip install hidrokit==0.4.0 -q"
],
"metadata": {
"id": "UuCX8A1Du_OW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def _check_system(PACKAGE_LIST='numpy pandas matplotlib'):\n",
" from pkg_resources import get_distribution\n",
" from sys import version_info\n",
"\n",
" print(':: INFORMASI VERSI SISTEM')\n",
" print(':: {:>12s} version: {:<10s}'.format(\n",
" 'python', \n",
" '{}.{}.{}'.format(*version_info[:3]))\n",
" )\n",
" for package in PACKAGE_LIST.split():\n",
" print(':: {:>12s} version: {:<10s}'.format(\n",
" package, \n",
" get_distribution(package).version)\n",
" )\n",
"\n",
"_check_system('numpy pandas matplotlib scipy hidrokit')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "22KhzQ-X0bsI",
"outputId": "f4b2e42d-06d8-4cbf-924f-853293dac764"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
":: INFORMASI VERSI SISTEM\n",
":: python version: 3.7.11 \n",
":: numpy version: 1.21.5 \n",
":: pandas version: 1.3.4 \n",
":: matplotlib version: 3.5.1 \n",
":: scipy version: 1.7.3 \n",
":: hidrokit version: 0.4.0 \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "ko7SmSXI0hhK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.style.use('seaborn-whitegrid')"
],
"metadata": {
"id": "pS__7DyW17m6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## IMPOR DATASET\n",
"\n",
"Untuk pengolahan dataset dari berkas mentah terlampir pada dokumen LI05-A: transfer data excel."
],
"metadata": {
"id": "yboNRRIETp-c"
}
},
{
"cell_type": "code",
"source": [
"from pathlib import Path\n",
"EXCEL_PATH = 'dataset/hk_daily_bandar_lampung_filled.xlsx' #@param {type: \"string\"}\n",
"excel_path = _directory / EXCEL_PATH"
],
"metadata": {
"id": "6KcPjfi-F1Hl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from hidrokit.contrib.taruma import hk88\n",
"\n",
"dataset_raw = hk88.read_workbook(excel_path)\n",
"dataset_raw"
],
"metadata": {
"id": "GZB8p_mZ0onL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "d5ef349f-8c0f-489e-b923-7a382403bb93"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" PH001 PH003 PH004 PH005\n",
"2009-01-01 NaN NaN 4.5 11\n",
"2009-01-02 NaN NaN NaN 9\n",
"2009-01-03 19.5 NaN 1 NaN\n",
"2009-01-04 2 0.6 32 70\n",
"2009-01-05 NaN 0.2 26.5 35\n",
"... ... ... ... ...\n",
"2018-12-27 0 0 0 0\n",
"2018-12-28 0 10 0 0\n",
"2018-12-29 0 0 0 0\n",
"2018-12-30 0 0 3 0\n",
"2018-12-31 0 0 0 0\n",
"\n",
"[3652 rows x 4 columns]"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PH001</th>\n",
" <th>PH003</th>\n",
" <th>PH004</th>\n",
" <th>PH005</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.5</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-03</th>\n",
" <td>19.5</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-04</th>\n",
" <td>2</td>\n",
" <td>0.6</td>\n",
" <td>32</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-05</th>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>26.5</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-27</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-28</th>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-29</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-30</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-31</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3652 rows × 4 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"# periksa isian yang invalid (bukan angka)\n",
"\n",
"from hidrokit.contrib.taruma import hk43\n",
"\n",
"for station in dataset_raw.columns:\n",
" print(f':: PERIKSA DATA YANG INVALID DI STASIUN {station}', end=': ')\n",
" print(list(hk43._check_invalid(dataset_raw[station], check=float).keys()))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ev-_MbnoZvOw",
"outputId": "ecff3f91-fd78-44f6-b384-77448038e728"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
":: PERIKSA DATA YANG INVALID DI STASIUN PH001: ['NaN', 'tad']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH003: ['NaN', '-', 'tad']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH004: ['NaN', '-', 'tad']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH005: ['NaN', '-', 'tad']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# mengubah 'tad' dan '-' menjadi NA (np.nan)\n",
"\n",
"dataset_clean = dataset_raw.copy()\n",
"dataset_clean[dataset_clean == '-'] = np.nan\n",
"dataset_clean[dataset_clean == 'tad'] = np.nan\n",
"dataset_clean"
],
"metadata": {
"id": "bIZl9igPZw1o",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "7ad69075-d022-4d0b-956b-e9acf90978ed"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" PH001 PH003 PH004 PH005\n",
"2009-01-01 NaN NaN 4.5 11\n",
"2009-01-02 NaN NaN NaN 9\n",
"2009-01-03 19.5 NaN 1 NaN\n",
"2009-01-04 2 0.6 32 70\n",
"2009-01-05 NaN 0.2 26.5 35\n",
"... ... ... ... ...\n",
"2018-12-27 0 0 0 0\n",
"2018-12-28 0 10 0 0\n",
"2018-12-29 0 0 0 0\n",
"2018-12-30 0 0 3 0\n",
"2018-12-31 0 0 0 0\n",
"\n",
"[3652 rows x 4 columns]"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PH001</th>\n",
" <th>PH003</th>\n",
" <th>PH004</th>\n",
" <th>PH005</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.5</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-03</th>\n",
" <td>19.5</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-04</th>\n",
" <td>2</td>\n",
" <td>0.6</td>\n",
" <td>32</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-05</th>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>26.5</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-27</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-28</th>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-29</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-30</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-31</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3652 rows × 4 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"# periksa kembali datasetnya\n",
"\n",
"for station in dataset_clean.columns:\n",
" print(f':: PERIKSA DATA YANG INVALID DI STASIUN {station}', end=': ')\n",
" print(list(hk43._check_invalid(dataset_clean[station], check=float).keys()))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1jmwAKXdSIzX",
"outputId": "43398016-9ac7-4764-aff7-2bbb2c0965d7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
":: PERIKSA DATA YANG INVALID DI STASIUN PH001: ['NaN']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH003: ['NaN']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH004: ['NaN']\n",
":: PERIKSA DATA YANG INVALID DI STASIUN PH005: ['NaN']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# periksa datatype dataframe\n",
"\n",
"dataset_clean.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qv0PTL9nHynM",
"outputId": "1ad90f13-dd4b-41fc-fd92-977496b19f89"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 3652 entries, 2009-01-01 to 2018-12-31\n",
"Freq: D\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PH001 3277 non-null object\n",
" 1 PH003 2809 non-null object\n",
" 2 PH004 3001 non-null object\n",
" 3 PH005 3290 non-null object\n",
"dtypes: object(4)\n",
"memory usage: 142.7+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ubah ke tipe data yang sesuai \n",
"\n",
"dataset_clean = dataset_clean.infer_objects()\n",
"dataset_clean.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A_aqy_YVSskQ",
"outputId": "65e3ae55-914c-4886-ea72-7d1267369303"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 3652 entries, 2009-01-01 to 2018-12-31\n",
"Freq: D\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PH001 3277 non-null float64\n",
" 1 PH003 2809 non-null float64\n",
" 2 PH004 3001 non-null float64\n",
" 3 PH005 3290 non-null float64\n",
"dtypes: float64(4)\n",
"memory usage: 142.7 KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# check apakah jumlah datanya sesuai atau tidak\n",
"\n",
"pd.date_range('2009-01-01', '2018-12-31', freq='D')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3hp7w1X0HzzM",
"outputId": "d2d5dd68-4d62-4b8b-cc30-04ac4ee3a6fc"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03', '2009-01-04',\n",
" '2009-01-05', '2009-01-06', '2009-01-07', '2009-01-08',\n",
" '2009-01-09', '2009-01-10',\n",
" ...\n",
" '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',\n",
" '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',\n",
" '2018-12-30', '2018-12-31'],\n",
" dtype='datetime64[ns]', length=3652, freq='D')"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"source": [
"# PRAPEMROSESAN DATA"
],
"metadata": {
"id": "AV8RQE0HnJ5f"
}
},
{
"cell_type": "code",
"source": [
"# buat hujan wilayah dengan dirata-ratakan dan mengabaikan nilai NAN\n",
"\n",
"dataset_clean_average = pd.DataFrame(dataset_clean.mean(axis=1, skipna=True), columns=['hujan'])\n",
"dataset_clean_average"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "Csk2QsNAb8Ld",
"outputId": "4cc56b53-6581-45fa-8039-3a3f52d8d705"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" hujan\n",
"2009-01-01 7.750000\n",
"2009-01-02 9.000000\n",
"2009-01-03 10.250000\n",
"2009-01-04 26.150000\n",
"2009-01-05 20.566667\n",
"... ...\n",
"2018-12-27 0.000000\n",
"2018-12-28 2.500000\n",
"2018-12-29 0.000000\n",
"2018-12-30 0.750000\n",
"2018-12-31 0.000000\n",
"\n",
"[3652 rows x 1 columns]"
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"</table>\n",
"<p>3652 rows × 1 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"# eksplorasi dataset\n",
"# pd.pivot_table(\n",
"# dataset_clean.resample('MS').mean(), index=dataset_monthly.index.year,\n",
"# columns=dataset_monthly.index.month\n",
"# )"
],
"metadata": {
"id": "WZs5EAsmNw6E"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# cek kelengkapan data hujan wilayah\n",
"\n",
"dataset_clean_average.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-ID9t0QZfe0L",
"outputId": "9b133e4b-5558-48cb-a096-5fc8f2796ecd"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 3652 entries, 2009-01-01 to 2018-12-31\n",
"Freq: D\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 hujan 3465 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 57.1 KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# dataset_clean_average[dataset_clean_average.hujan.isnull()]"
],
"metadata": {
"id": "Qv-1HNTaSTVz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# data di rekap setiap bulannya dengan nilai maksimum\n",
"\n",
"dataset_monthly = dataset_clean_average.resample('MS').max()\n",
"dataset_monthly"
],
"metadata": {
"id": "n_pk1VGY1BJl",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "df078e95-9f82-42f1-b388-3adf34bac419"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" hujan\n",
"2009-01-01 35.25\n",
"2009-02-01 43.00\n",
"2009-03-01 82.50\n",
"2009-04-01 30.50\n",
"2009-05-01 125.00\n",
"... ...\n",
"2018-08-01 10.00\n",
"2018-09-01 29.75\n",
"2018-10-01 18.50\n",
"2018-11-01 38.50\n",
"2018-12-01 24.75\n",
"\n",
"[120 rows x 1 columns]"
],
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>35.25</td>\n",
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" <th>2009-02-01</th>\n",
" <td>43.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-03-01</th>\n",
" <td>82.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-04-01</th>\n",
" <td>30.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-05-01</th>\n",
" <td>125.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-08-01</th>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-01</th>\n",
" <td>29.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-01</th>\n",
" <td>18.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01</th>\n",
" <td>38.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-01</th>\n",
" <td>24.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 1 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"# cek kelengkapan data\n",
"\n",
"dataset_monthly.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nO5kH0IHhVFh",
"outputId": "59da7039-cd8d-440a-9ca0-2a4c358fb5d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 120 entries, 2009-01-01 to 2018-12-01\n",
"Freq: MS\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 hujan 120 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 1.9 KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# plot grafik data bulanan maksimum\n",
"\n",
"dataset_monthly.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 261
},
"id": "OOzPU9OZhZGj",
"outputId": "4025f6d7-4f5c-4962-949c-02fbde52a5db"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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2qAzUarXicDhwOp3Y7fao9zmdzu6NvAdRFjYtBh25VgMQXSvu8Aawm2VBj8zE4/VOgdZ2tJ6IocoCgUDQGZJm4gCVlZXceuutLFmyhG984xs8/vjj6nMul4vMzExsNhsulyvq8UhRb0tZWVkXw+4aHo+nQzEcrazDoNXw1b69uOrlz/n57n048uS68BPV9Rg0IcrKypACPmobmygrK6PF58PpaIo5V1Od7Kk3uToWR3fR0e9jIMfRF2IQcfTNOPpCDPFIKuK1tbVcf/313H///ZxxxhkATJ48mS1btjB37lw2bNjA6aefzrRp0/jtb3+L1+vF5/NRUVFBSUlJwuOWlpam91N0kLKysg7FkLFvFzazm9LSUlwZ9fB+FVmFRZSWFACg2+Ik166ltLSU7A8b0Gvl/w9xhML83Jhz7fMeh821SDpDr38X0PHvYyDH0RdiEHH0zTh6M4Zt27YlfC6piP/+97+nubmZ5557jueeew6Ae++9l4ceeognn3ySsWPHsmjRInQ6HUuXLmXJkiVIksTy5csxmUzp/RS9iMvb6n/nhGvBI2vFnd4AVpNip+jUbfiJe6codorwxAUCQddIKuIrV65k5cqVMY+vXr065rHFixezePHi9EXWh2jxB9TZmLkWWcQjPXGXL0Ch3QyASa+lzpm8TtyqDoYQnrhAIOgaYrNPCsiZuCy8mRkGtJroToZOTwBbOBM3GXR4AkFCIYlgKPFmHxCZuEAg6DpCxFOgxddqp+i0GrLb7Np0eoOqnWLSa/H6Q/hDcpbddigEoL5WVKcIBIKuIkQ8BVy+VjsFIMdiiMrEXd4ANpP8vNkg14krNeCJ+omDsFMEAkHXGdQi7vQGONnkafd1bl9Qre0G1F2bAIGgvPHHZpLrx816HR5/CH84y47fxVAsbAoEgvQwqEX8ibf38p3/2dzu69y+gLoYCZBjMdLgktvRKj1QrOFM3GTQhjPxZCKueOIiExcIBF1jUIv44To3R+vdBEPJM2K3N6guRkI4Ew/bKUo5obpjUy9vqVd2bcZrgGXSa9FqwOMXmbhAIOgag1rEaxxeQlLsuLVIJEnC7Q9iNbbaKYWZZuqcXpzeQETzK6U6Rf5Kmz1yph6vn7hGo8Fq1NMiMnGBQNBFBr2IR/43Ht5AiGBIisrE54zOJSTBpwfr1Uxc3ewTFm3l8Xh2CshlhsITFwgEXWXQingoJFHrDIu4M7GIt071aRXx2aNzMOq1bNxfi9Oj9BJv3bEJrSPbEom41aRXLReBQCDoLINWxBtb/ATCXnhtkkxcbUMbYaeYDTpmFeewsaIuoZ3i9Mp2SjxPXD6ejhaRiQsEgi4yaEU80kJJJRO3mHRRj88fn0dZZTNH6t0A6o5Nsz46E49XJw6yiHuFJy4QCLqIEHGSe+KqiBujRXze+HwA3tlTBRCx7V7+SlU7Jc7Cpnw8PS2iOkUgEHSRwSviTnmTj06rUb3xeLi9sXYKwLSiLOwmPduONACRC5upeeKynSIycYFA0DUGr4iHs+/xBbaUMnFrGxHX67TMHZuHJMllhEopoUld2EzuidtMerHtXiAQdJlBLeJmg5bR+ZakIu4KL2xmtLFTQPbFodVKAXkjD7SWGCbyxG1mPW4h4gKBoIsMahEvsJsosJuS2yltttVHMj/si0c+l2qJod0ke+KhdnaLCgQCQTIGr4g7vRTYTBTYzDS4/Wqvk7a0DkmOnZ8xodBGgd2kNr+C1kxctVMSLGzazHokwO0PduVjCASCQc7gFfFwJp5vlyf11Dnjb71XFjbj2SkajYYbzxrD104Zqj4Wm4kn8sRl4Vc2C3U3B2qcrP3saI+cSyAQ9BztTrsfqFQ7vMwZk0uBTZ4FWuPwMjTLHPM6tz+IQaeJ2wMF4KYF46L+bW5TYpjIE1caZjk8/rjnTTe/+s9e3tpzkstnFKFPEJNAIOh/DMq/Zl9QotHtp8BmpsAeFnFn/L7ibm8gprwwGSZ9dHVKIsG0KSLu7f5M3OHx897eaiQJGtz+bj+fQCDoOQaliDd5ZB+6wG4iP5yJ1zri2ykuXzBmo08yDDoNWk1kA6z4dorSa6Un7JS3d1fhC9ekR04kEggE/Z9BKeL1LbJwKtUpkHjrfUsHRVyj0WDS61CKTgza5Jm4swcy8dd3nlD/P1nbXYFA0P8YlCLe0NKaiZsNOuxmfcJacZcvoO7GTBXFF9drNWi1iTf7QPdn4vUuHx9/VcvCkgIAGoSICwQDikEv4gAFNlPCTNztC5JhSD0Th9YKlUQ14gD2cHVKd3vi/9l1kkBI4rp5owHUiUQCgWBgMKhFPN8mlxfm200JM3F3JzJxpVY8kR8OEQubnu5daHxtx3HGFljVjUn1CUopBQJB/yQlEd+xYwdLly4FYPfu3Zx11lksXbqUpUuX8u9//xuAtWvXcvnll7N48WLef//97os4DTS0BMm2GNRKkgK7KWFPcbcvGLdGPBlKJp6oLBHkxltmvaZb7ZSqZg9bDtbzzVOHY9RrsZv0IhMXCAYY7aaYf/jDH3jttdfIyMgAYM+ePXz/+9/n+uuvV19TU1PDqlWrWLduHV6vlyVLljB//nyMRmP3Rd4FGloCan04yHbKhgg7Ze2nR5k5KpvxhXbc3mDUVJ9UaM3Ek18jLQZtty5sfnaoAUmC8yYNASDHahSeuEAwwGg3Ey8uLubpp59W/71r1y4++OADrrnmGlasWIHT6WTnzp3MmDEDo9GI3W6nuLiY8vLybg28KzS0BFU/HORM3OEJ4PEHOVTr4r/W7eTOv+9EkiRcvo7ViUNrJ8NURLw7PfGW8Jb+rAzZf8+xGqkXdeICwYCiXXVatGgRx44dU/89bdo0rrzySqZOncrzzz/Ps88+y6RJk7Db7eprrFYrTqcz4THLysq6GHbXqHMHKLR51Tj8jmYAPtm+m7f3OwDYcbSRP729Dbc3QIujsUMxB70tAEhBf9L3mfVQVdexY3eEw8fkz3XkUAWuaj3GkJfKOnfM+TweT6//TPpKHH0hBhFH34yjL8QQjw5vu7/gggvIzMxU///BBx9k9uzZuFwu9TUulytK1NtSWlraiVDTgyRJNHoPMr6oQI2jUlMFm2rJHDKSj97/gtPH5lLd7OXlXU6CEhQPH0Jp6fiUz5H7qQtOtGDNMCf9rLa3K5H0yV/TFT6pPwjUMmXSRHKsRoq/9HHiQF3M+crKynr1Z9KX4ugLMYg4+mYcvRnDtm3bEj7X4eqUG264gZ07dwKwefNmpkyZwrRp09i2bRterxeHw0FFRQUlJSWdj7gbcfmCeANStJ1ik3uX/GfXSY7Wt7B49kjuuLCEAzXyhamzJYbJFjYBrAZNt1anKDM8lZFxuVaD2OwjEAwwOpyJ//znP+fBBx/EYDCQn5/Pgw8+iM1mY+nSpSxZsgRJkli+fDkmk6n9g/UCSilhW08c4K9bjmAx6lg0ZSgZBh2Th1Wwp7I5bi/xZHRoYbOp+zxxZau90oQrx2qkxR+kpRMVNwKBoG+SkoiPGDGCtWvXAjBlyhTWrFkT85rFixezePHi9EbXDcQT8bxwvbjTG+DyGUVqXfhdF03k+3/+lFxrxy5Iyo7NZHXiABZj9y5segNBdFqN2oQr1yJ/zga3jwxjRredVyAQ9ByDbrNPPBE36LTkWOQKjstnjlAfP2diIf++7SzOmVjQoXMow5JTLTGUpO6Z7uMLhNS7ApAzcRD9UwSCgcSg6yde55JFPNcaXcNeYDdh1Gs5Y1xe1OOTh2d2+ByKB52ol7iCxaBFkuQNRR3dFZoK3kAoypfPEyIuEAw4Bp2Iu7xy7bStjWguP78Ek0GLLkHDqo7QkUwcZBunW0TcHz8TF+1oBYKBw6ATcWUDjCK0ChefMixt51Ay8UTzNRUsxtYpQEM6nvC3iy8YUlsLQKsnLjJxgWDgMOg88RZfAJMucYvYdNDaxbCdhU11lFv3lBl6A8EoOyUzw4BWI9rRCgQDiUEn4m5fEJO++wQcWrP8VDxx6L7BEG0XNnVaDdkWo2iCJRAMIAadiLf4g5jbsTm6imqntCPiVoN8MemuTobeNiIO8oKusFMEgoHD4BPxHsjETakubCqeeDdl4l5/KGbXaK5FiLhAMJAYfCLek5l4OxcL1U7prky8zcImQI7VQINLdDIUCAYKg07EB5Mn7vUHYzNxq/DEBYKBRJ8V8Qff2MM7e6rSftwWXxBzd4t4ip64Tqshw6DrUHXKW7tP8uAbe1J6bduFTYAcizwYort2iQoEgp6lz4r437Ye4T+7Tqb9uD1ip6ToiYM8azNRJv7FkYYYsX13TxUvbTqEPxhq99jywma0nZJrNRIISTR341g4gUDQc/RJEQ+FJNy+oLpFPp30xMJmqg2wAOwmPY44grr9aCOXPbeJzRV1UY+7fUGCIYkTjS3tHrvttntobTcgasUFgoFBnxRxT0DeVdkdVRRuX6AHFjZT6ycOiTPx8kp5Kk9tm+/A5ZNfe6jO3e6xfYFgrJ2i9E8RvrhAMCDokyKu9Depc6ZfaGQ7pXsz8aGZZq46bSTzxuW3+1q7WR+3OqWiRh5v524j8O7wd3O4zhXznrbErRO3iExcIBhI9MneKe5wtlnn8iJJEhpNekQ3FJLw+EPdbqfotBoe/fa0lF5rM+mpc8Zm1cpUobZZupqJ1ybPxCVJSrjZB0T/FIFgoNCnM3GPP4TbF0zbcVubX/Wdj20zGeJ64gdqZRFv+/ldYVE/Up88E/cH5QVRkyF2YROEiAsEA4W+o2YRKJk4pNdSaRXx7s3EO4LdrI8pMfQFQhyplzNtV0wmLn+G9jxxb3hdoW2tusWow6jXCk9cIBgg9EkRd0Vkn+msUGkJH9eUQulfT2Ez6WOm+xypdxEMyf92+dp64uFMvM6tviYevjZDkhU0Gg254VpxgUDQ/+k7ahZBS3dn4oa+k4nbzHpCUmtsAPurW60SxVqCcOmlP0i+zYQvGOJksyfhcdVJ93GsoxyrkXqx9V4gGBD0SRGPFK50ereKv9yXPHG7WV5bjqxQOVArV6YUZWdE2SmeQBBJah0Zl6xCRRHxeGWOuVYD9d1Qgy8QCHqevqNmEUR64rVpFBvluKYUNuH0FMqYuMhOhhXVLoZkmhiSaYqyU5SL2+Rhiogn9sVVO6XNjk2ATHP8xVSBQND/6JMlhoonrtNqqE+jneLpowubEJuJj823oddposRWuQiNK7Bi1Gk5lDQTj7+wCa0+vEAg6P/0zUzcG0CjkTfN1A1wO8VmMgCoYi1JEgdqXIwrtGI16qPuShThtZsNjMjN4Egqmbghjogn2GAkEAj6Hymp2Y4dO1i6dCkAhw8f5uqrr2bJkiU88MADhEKyWKxdu5bLL7+cxYsX8/7773cpKJcvSIZBR77d1C0i3t2bfTqCYqc4vfJCY53LR1OLn7H5NiwmXdT6gBK/1aRjdJ41aZmhN4mdYjfpcfoCopOhQDAAaFfE//CHP7By5Uq8XtmbfuSRR7j99tv561//iiRJrF+/npqaGlatWsWaNWt48cUXefLJJ/H5Oi++bl8Qi1FPntVInTN9nrinD272UewUJRNXdmqOK7RhM+nbeOLy/1uMekblWThc50ooxKqdEuez2sx6JCl2I5FAIOh/tKtmxcXFPP300+q/d+/ezZw5cwBYsGABmzZtYufOncyYMQOj0Yjdbqe4uJjy8vJOB+X2BbCadOSleR5kX8zEVU88LNBKz5Sx+VYsRn1UdYoSv82kZ1SuBbcvSE2Ci5wvSYmh1RR9ToFA0H9pd2Fz0aJFHDt2TP13ZC8Tq9WKw+HA6XRit9vV11itVpxOZ8JjlpWVJT1nVV0julAAyeugxuFhz549aemfcvREvfwZAt52Y+gJPB4PRw/uB+Dg0UrKyjx8Wl6HUaeh+eQh3M2N+IMSO3ftwaDTsP+QA4DKowfRumX7ZcPnZUwpNMcc+8Bh+fs/dvggNBqjnmsOlzDu2LOXkVlGPB5Pn/k+ejuOvhCDiKNvxtEXYohHh6tTtNrWzM7lcpGZmYnNZsPlckU9HinqbSktLU1+jo+byM0MMbF4KIFdTYwcOwG72dDRUGOwHizDbGjGkpHRbgw9QVlZGaWlpZgNh8nIzKG0tJTGLZ8ytsDGlMmTGV1/EL5oYOSY8eRYjWxtOATUMG3yJEa1+GH9SbAVUFo6IubYXzqPAtWUTpzAiBxL1HOVmir4qJrColGUjsxW4+ht+kIcfSEGEUffjKM3Y9i2bVvC5zpsDk+ePJktW7YAsGHDBmbPns20adPYtm0bXq8Xh8NBRUUFJSUlnQ7Y5QtiMenVZk3p2rXp9gXIMMQu9PU2NpOBWqePBpePihon4wps4cfla6ziiyv/tRh1FGVnoNNqEm748QYTL2wqFTGiQkUg6P90OBO/++67ue+++3jyyScZO3YsixYtQqfTsXTpUpYsWYIkSSxfvhyTydTpoNzeAMMyzeTZwiLu8jE639rp4ym0+EJYjH2vND7bYmDd58dY97lsW106vQgAi0kWYKVCxeUNoNNqMOm1aDQahmebE1aoeP1JFjaFJy4QDBhSUrQRI0awdu1aAMaMGcPq1atjXrN48WIWL16clqDcviAWk448q3whSFeFSos/QIax72Xiv7piGjuPNgLyBqevTRsOgNXYJhP3BrEYder6wLDMDKoT9E/xBRMvbLZdTB0sPPv+fvKsRq6aU9zboQgEaaPvpaWEq1OMejUTT1eFijtcf97XmFmcw8zinJjHlSoSpUJF+V5an9dRm8Bq8vrDvVPi7NhUq1M8g6sJ1rrPj1GUnSFEXDCg6DsF0xG4wpm46omnScRbfME+mYknwmJsY6f4glhNrfFbTfqYfuMK3kAIo06LVhtb1aMcY7Bl4k5PYNB9ZsHAp8+JuD8YwhcIYTXqMRt04fFlaRJxf1AVxv6ArW0m7g2oWTTIdkvbfuMKvjij2RRMenkwhGOQCZrTGxCNvwQDjj4n4sqGFkVsc63GtA2G6Kt2SiKUhU23Wp0SfRGSM/H4uy69gWDcRU0Fe5IsfiASDEm4fUFRkSMYcPRBEW/dWg6QZzOmLxPvZ3ZKaxWJLNRtPXGbSYcrQQ+UZJk4DL4mWIqNIuwUwUCj10W8qtnD5oo69d+RTZ4AuX9KujzxfmanZBh0aDQRmbhXrp9XsJjkHiiRU4EUvIFQzJDkSAZbO9pIEQ8lGWsnEPQ3el3En/+ggutf+lTNJt1exU4JZ+JWU/pKDPuZnaLRaGTfO6JO3NrGToH42aU3EIxbmRL53sHkD0fedSRaRxD0bepdPm5f8wXNg6yqqj16XcSPN7bQ4g+qQqT8gSlilWuTm2B1tW1qKCTR4g+S0Qc3+yTDYtRFlBgGozYr2dpsBorEFwjF7SWuYB90mbg/4v8Hz+ceSHx6qJ5/bT/B54cbejuUPkWvi/jJJnmzilILrnriJiUTNxIISTS3dO0PzxNuzdqfMnFAbUcrSRIuX0AVbmi9W4m3QKmUGCY8rnmwiXjrhW4wrQUMJJpa5AtxtUPMh42k10W8sqkFQN20omSVSibeuvW+az+4ljZVL/0FeTBEAI8/hCQR5Ym3LUGMpL1M3DbIqlMihbtZiHi/pCncubNGiHgUvSri3kBQFe+2mbhSRaJuve/i4qayYNqfqlNAqQUPxthMELGjM47H6w2E4ja/UrCZB5knLuyUfk9ji6wBiVpNDFZ6VcSrm1uvqMripVqdErYK0tXJUKng6G92irIrs+2CL7R64s44nnh7C5s2ox5vIKQOj+hNfIEQd6zdzonm7luwirxgCTulf6LYKYkGoQxWelXETzS2qP9fp2biYbEKC1RhppyJK7ZLZ+mvdorVpMcdmYnH8cTdnbFTzImtmJ6mosbJPz8/zqYj8dvqpoPI7DsyKxf0HxrDdkpk8ifoZRE/GXFbVKd64gH0Wo2aRRbYTORajZRVNnfpXP3XTtHh9Aai5muqzyUtMWxnYbMPtaNVPM7j3ZiJOz0BlOFQg8lGGkiIhc349KqIV4YrU/JtRupdrXZKZLtVjUbDlOGZ7OmiiHv6sZ3i9gZwqZugInunJC4x9LZXYthmQHNvUuvsARH3BsgLW3N94cIl6DhqJu7wdLnkeCDRuyLe2ILdrGdkrkW1U1xtmjwBTB6eyb6TTvzBzvu3rT1Z+leduNWowxXR8yPSTtHrtJj0WnUxOBJfewub4ek+fWHjS0+IuMMbIDPDIN/Z9IELl6DjKJm4xx8SF+IIej0TH5ZllrfWO1s98baWx+RhmfiCIfZXJx6+3B7uiNFm/QnlgqaUWFrbXIQSbZ9vrwGW4on3BUFTK5Ragt32x+n0BLCb9IOuKmcg0ej2kZUhJx/CUmml1z3xoVkZ8tZ61U4JxAjVlOFZAOw+0XlLRbFTzP3MTlHqwpXFnLYXoXg9xUMhCX9QSt4AK5zR94V2tJF1v4dqu2dx0xm+wxtsPWMGCsGQRLMnQMkQef6sWNxspZerUzwMzzJHba1v224VYEy+lQyDjt0nmjp9rrYtbvsLiu+tCF1bq8kStlsi8SUZkqzQl4Yl1zq92MOf60A3ibjLG8Bm0mMzG/rEhUvQMdzhSVXjC+2A7IsLZHpNxH2BELVOL0PDdoo/KF9p3b5YT1yn1TBpmJ09XcjE1eqUfpaJK99FtcODVhM7MzPezkt1NFsqdkofKLercXiZXpyNBjhY0z0i7vAEsJn1ZJr1g24s3UDA4ZV/pycUypm42LXZSq+JeFW4vHB4VkbULE23N367WKVCpbOr0h5/EJM+/riyvoxiLVU7vFiNerVqR30+nogH5QtWMjvFEm5z2zcycR8jcjIosOo5WNv5dY9kOL1hT1zYKf0SR7gCa1SeBaNeK0Q8gl4TcaW8UM7EW6fau+J44gCTh2Xh8AQ4Wt+5TT/uODZNf0CpRql2eGPuUJTn29opqWTiWq0Gm1Efd7dnTxIMSdS7vOTbTBRlGjjYDXaKJEk4vXImbjMNrmEYAwWHT/6dzrYYKbCZxMJmBL0o4rIYD8syRw1EdnuD6m7NSKYMzwRgT2XnfPG2bVz7C2p1itMb93uR+41Hi1KrJ578x2s16XvdTql3+QhJUGA3MSLTwIFaV6fvthK1EPD4QwRDEjaTQa5OEZl4v8MZtlOyMgwUZpqEJx5Br4m40oJ2WHYG+TYlE/fh8gXiZswTh9rRaTWdrlDx+IOYk2x+6asoIh6SYssLlefb2gNKJp5sYRP6RjtapUY832aiKMuAwxPoVLOzfVUOpjzwH76qcsQ85whfqGxmvdpHXUz36V84wneb2RYDhXaTsFMi6HRq+q1vfQu7XV4pHjFiBLfccgv33HMPGo2GCRMm8MADD6DVJhbNyiaP6lEadJrwYy2EpPgbcswGHeMKrJ1e3HT7Av0zEzdG9kqJFWVbuLeKJEmqX+4NtO+JK+/t7ZrpKBHPlCtmDta61At7qpSfdOAPSuyrcjJhiD3qOcU+sZv0tJjlkXZuf1BtPSDo+zgiMvECu4ktB+t7OaK+Q6d+i71e+Q9v1apV6mO33HILt99+O3PnzuX+++9n/fr1XHDBBQmPUdnUwtAsMyBnjHazniP1biBauCKZMjwrah5nR2jx96/RbArxeqVEPW/SEQxJeAMhtQZesRXaE3F7H8jElYwq32bEqYh4jYvTRud26DhV4Tu7eLfZymeUL3itpZVCxPsPTl8Iq1GHQael0G6m0e3HGwi2e7c5GOiUv1BeXk5LSwvXX3891157Ldu3b2f37t3MmTMHgAULFrBp06akxzjZ5GFYdob67zyrkaNhEbck+OOaPCyTk82eTs3c7G+T7hWMeq3ayCpRJg7R/UC8gfYXNpX39nYXQzUTt5sotMp3ZZ2pFVeqneLdZiuZuM2sV3vG9PZagKBjOLxBsi3y2lmhXb5LE5aKTKdSEbPZzA033MCVV17JoUOHuPHGG6Nu561WKw5HrDepUFZWxtE6J0MzLJSVlQFg0QY5WC2/p7HmJGVlsaVm2SF5MXTdRzs5a7RNffyfuxupdgW4ZU5+wnM2ONzYdAHKysrweDzqeXuTVOMw68EXBH+LM+b1TXXyd7Zzz16G2eUss+KoLIInjh2hrKUq4XEDHicNzpZe/T72Hq7DqNNw7MBX+H1ehtr07DxYSVlZxzzrr45Vy/89WhXz3rJwi9uaE0dpaJGtpi/L9+OvM8ccp7/9bgyWOJpa/Ji0EmVlZXib5GTv0y/34igwU17jodYd4MxRtnaO0jVS/S58wRAvbK3j65OyGJNj7NaYoJMiPmbMGEaNGoVGo2HMmDFkZ2eze/du9XmXy0VmZmbC94+bMJEGzwEmFQ+ltLQEgKJPXeypkQVnwthRlE4sjHnfhJIQD2+opazZwE2lpQAEgiFeXbcety/Ib5aembAOPPRaJUNysyktLaWsrIzS8Pt7k1TjsGdU0uxtoagwP+b1h4OV8HENQ0eMpjRcwXPAXwlUMWnCOEra+MORFH0V4pNjxzCbzb32fUg7t1Ng9zF58mTKysqYVJTL4ToX4yZM5Im39/LW7pOML7QxeXgWF04ewtSirLjHadnQCLjw6TJiPktZyzGgimmlE+Tsbf1J8oeNoHRCQcxx+tvvxmCJw/XmcYZk2yktLSVgb4L1J7HkDaO0dCg/fecjvqp2csVZp5Jj7T7RTPW7ePytcv69z8GEkUO5ZF5JWs69bdu2hM91yk75xz/+waOPPgpAVVUVTqeT+fPns2XLFgA2bNjA7NmzE75fbiUJw7NbM6F8W+uXb0ngXet1Ws4rHcL6siq1o+HWg/XUOn24fUHVU49Hf7VToNUyscYrMYwzok1Z2EzWTxxaPfHebOtZ4/SSb29dxBybb+VQrZvFL2zmhQ0HGJFj4VCdm2fe+4of/fXzhMdJaqdEeOJ9qfGXIHWc3hDZFvlOUxkUU+3w8lWVg90nmvEFQqz7/FhvhgjA7hNN/P7DAwBJ9SiddErEr7jiChwOB1dffTXLly/n4Ycf5t577+Xpp5/mO9/5Dn6/n0WLFiV8/0l1o0+rJ54bcQWNt4CncOHkITR7AmwNr06/8WWl+lz5ycSVK/11sw+0TjmKV12jiniEt60ubLZTUmkzyZUankAvirjDS0HEBXxMvhVfMERFtZPnrpnJ6h/M5d07FvKzi0s5VOeOO19RkiRVxONtAnFEeOLKBbG3q3IEHcPhaxXxPKsRjQZqmj38a/txtBqYOMTOy1uO9GpCEgiGuHvdTnIsRiYPy+wxEe+UnWI0GnniiSdiHl+9enVK799XJfvdw7NaM3Fl1yYkb1J11oQCzAYtb+8+ydwxufxn10kumCxn52WVDi6aOizmPZIk9dvqFGgnEzcqIt6689IbSL1OHFqbC/UGtU4fM4qz1X9fOGUo5ScdfH/+aEblWdXHTxsjV6tsPVTP16cNjzpGsyeAxx/CZtJT7/ISDEnoImw1pzeAUaeVq6DCjb8iN/xErucI+h6SJOHwBskMt6HV67TkWU1UNXvZWFHL/PH5XD6ziOWv7GBzRR3zxideG0sHoZBEg9tHXpsy2D9+fJBdx5t5/pqZvL+3mvfKa7o1DoVe2f3y7Pv7mTwsk3EFrQsRebbUMvEMo44FEwp4e08Vmw/UUe/yccWsEYzOtyYc4eYJi1RGP6wTh9aLWvxMXJnuE8dOSaE6BaCll0Q8csu9Qq7VyM+/OSVKwEHesZth0PFpnPpgJTufPDyTkNTae13B6Qmo31NbO+Vkk4eZD77Dy1sOp++DCdKKxx8iEILsjFaNKLSbWF9ezbGGFi6bUcTFU4eRbTHw8pYj3R7PL9/Yw7xH32PH0Ub1sZ3HGnny7X1cNGUoF58yjFF5Vmqd3rgDW9JNr4j48cYWVn69NGoRMtVMHORsrbLJw+Nv7cVq1LGwpIDSoZmUn4xfEaNMuu+vdopyUYtXPx+vxLAjdeIArl4S8Qa3vOU+lY09Bp2WmaOy2XqoIeY5ZVbr1HDf+ba9ppW+KSB3xLQYdWqJ4Y5jjTS4/dz76i7+2gMCIOg4jS3yDl7FTgG5TUOt04vZoOXCKUMxG3RcOWsEb+0+mfKWfKc3oJY1p8rRejcvbzmMNxBi2ept1Dq9NLn9/PDlzymwm3jk8lMAGJlrCb++8wPeW3yp9TXqFRG/YPIQ5o2LvuWJ9MTb21l53qRCtBrYeayJCyYPwWzQUTrMzpF6d9zNK8rVsL/aKYplEq9+Xp1474uuE9doQN9Ox0bluG5/7/iIrRt9UtudedroXMpPNtPcppVsVVi0TxkhV+fUtNlH4PAE1P7pED0NqaJGtvbOHJ/Pile/5M19XZvlKkg/ymzN7IzWn6FSK37B5KFqInP1nGICIYm1nx5N6bj//f/KuOy5jR3y0Z9a/xUajYY/XjubOpePH/31c+78+3aqmj08s2SGWh1THBbxw3Wda+h2sNbFtF+8xcdf1bb72l4R8RWXxJbpKNUpJr02ys+MR47VyJywR/q1sD86aaj8B7w3zuJmSz+ddK+gZOK2OJ64shkoshuhPF9T267Pq3rivt7JxFu33KdWFnba6FwkCbYdjs7Gq9pk4m0rVJxevzp0Aoga0VZR7WJIpok/fm82Z08s4OnNtRxv7Hz2lA52HG3kjfKmLs2UHUgoIp4VKeLhCpXLZrSuj4wtsDFtRBabUtjVLUkS68uqqHX6ONaQ2s/7WJOPf35xnO/OHcX5k4fw8GWn8MmBet4tq+ZnF5cyozhHfe2osIh3dnFzc0Ud/qDEq18cb/e1vSLiY/KtMY8pV7BkfngkV88pZuIQO2dNkDP6ScPkeuiyylhLRcm6+q2dksQTh3A72jY7NlPZjqws8vXWwqYi4gX21DLxGcXZ6LWaGF+8utlDZnjgNsQT8VY7BVCbYAEcqHUyrsCG2aDjpxdORAI+Pxxr2fQUwZDE7a9s59ktdVz6zMZ2p1l9fqSBxS9sxtFDgy4a3T5+8fpuGjrRpKyzKAOSsyLslHMmFvK1U4ZxVpta/wmFdvXuKhnlJx1qJdOeBGtpINsnyu/pyzsaMeq0LDt7HADfnjWCuxZN5Pr5Y/j+/NFR78u2GLCb9B22axS+OCL/Dr4bUU6diD7T1s+g05KVYUhZaC+dXsRbyxeo/UKKsjOwm/UxZYbBkMQTb+/DqNcm3fjSl7Gonnh8EbcY9TF14u0takLvV6fUOmQhyE9RxC1GPVOKsvj0ULSIn2z2MCTTjNkg9+CJEfE2fVKUTFySJCqqnYwtkJOKkiF29Fr48njnxwB2lTd2nuBgrYtLSzOpdni59JmN/G1rfK8+FJK4//92sfVgPTuO9kzMv3xjD3/eeCilDDFdNKmeeOsd2+zRuTx7zUwMbfZCjCu0UtXsbfei9sFeuXJEoyFhU72/bD7EWb96n9kPvcusB9/hw4NOvjdvdFTSces547n/G5Nj7no1Gg3FeRYOd1bEjzZiN+lpavGr5dSJ6DMiDnL9ZyKhag+NRkPp0MyYTPy37+7j4/21PHjpFDVT62/MGpXDaaNz1FvItrTtgeIN2yntoVRs9FZ1So3Ti1GvjbI62mPO6Bx2HG1SB1+D7IkrzdQK4rQpbZuJK4Mhap0+mj0BtUrKqNcyLtfEzmONUe9/ecthblm1jcffKufVL46pmWG6CYYknlr/FROH2LnptDzevWMBM4tz+NV/yqM+r8JrO06w67gsQMn2SKSLDftq+Ofnsni/V16d9uP7AiG1siqSeHZKIpSf5YF2xvx9uK+a0mGZjElQ1bZm6xHu/7/dnDupkPu+PpnzS4dwRrGFWxaOTeWjALIv3hk7panFz/5qJ9fOG4XZoOWt3SeTvr5PiXi+3RT1x9ZRJg2zs/ekQ+0V/X55NU+/t58rZ43gO6cVpyvMHmf6yGz+fss89a6jLbKdEl0nnkombtLrMOq0vZiJeymwmTpUo33a6Fx8wRA7j7VmntXNHgrtsogX2mMHBiij2RTsZgNOb0C97Y4sdZ2QZ2LX8Wb1d0iSJH777ld89FUNv//wAMtf2cGv39rb8Q+bAv/+spKKGhc/Pm88Wo2GbIuR286bQIPbz392Rf8he/xBHn9rL1OLMsmzGtmboDIrXbi8AVa8+iVjC6xcN280Ww7WpdXCcfsCfPv5TVz3p09jnmtq8aPTJO5uGonys0xmqTi9AT471MDCkgImD8uMsVNe/eIYP3v1SxaWFPD8d2dyw5ljeOyKadx3ztCou4H2KM6zcKy+hWA7veu3HqyPss2U0sV54/JZWFLQv0R8xSWlrLhkUqffXzosE6c3wPHGFnYcbeQna76gdFgmD35rahqj7HtYTdF2ii9FTxxka6HR0zsj2tpuuU+F2eEWtYqlEgpJVDu8DAnfpRTYzVGZuD8YUjcCKch91P2tIl4YLeJOb0DtpLi3ykGNw8vPvzmFsl9exBlj82IWVtNBKCTx9HtfMaHQxiURG9bmjctjVJ4lpvzxfzcd4nhjCysuLmXiUDv74gzDSCdPvrOPYw0tPHr5NC45ZRj+oMRHKVROpEIoJHHn2h18ebyJrYfqYyrMGlv82E26lC72o/Is6LWapJn4pv21BEKSLOLDMznW0KLeXbm8AX72zy+ZMzqXF5bO6lKr2+JcC75gSF14T8RP1nzB7Wu2q1UyXxxpRKOBaSOyWDRlqFp9lYg+JeLTR2Yza1TH+khHMmmo7Hn/ZfMhrvnjFrIsBv5n6ayEGexAoe2ItlTtFID54/PZcNDVKxUZtU5f1Jb7VMi1GikZYmNThSwgdS4fgZDUaqfYou0U5XuJWtgM94zZX+0kw6BjWGbrzuGSfPli8OXxRgA+2ief56wJBRj1WmaPzmFvlSOlGt5NFbV89FVqu/beKatiX5WTH507Pmr/hFar4eo5xWw9VK9OLTrR2MIz7+/n3EmFzBufHxZxZ7dNK3r1i2P8aeNBvnt6MXPG5DKzOJusDAPry9Jjqfz23X28ueskF04eQjAk8VmbNY8mtx+bMbXfZ4NOS3GeJWkm/sG+GqxGHbNG5VA6TK5qKw9n4x99VYPHH+In50/osm4Up1ChUtnUQmWTh6+qnXwRzsC/ONpASaEdu9nAeZOGtFsq3KdEvKtMHGpHo4E/fHSQoVlm/n7zvH7rg3cEeeJ9hJ3iT21hE+Dui+SKjEf+nbjFZqLZlV1BkiRqHN4OT/ABWFhSwNaD9bi8ATXLUe2UTBMuX1AVb7VvSptMPCTB7uPNjC2wRonmyCwDGQadatds+KqGCYU29SJx6ohsgiGJXUmqRiRJ4o8fHeCaP27hv/6xM6XP9Nr2E+TbTDEtBQCumDUCg07DX7ceod7lY+mLW0CCe78ml+pOGmqnxZ+8AVyqbD1Yzx8/OkCjW15M3HTExU//vpPTx+Sx8muTAXnb+9kTC/hgb3W7VkEiJElix9FGfv7abp56bz+LZ4/gt1dNR6/VxEztaWzxYTelLlXjCmwJRVySJD7cW8O88fkY9VqmhEVc8cXf2VNNplnf4aEk8RiVKy+YH6lL/HP5/HAjIC+wvrL1KJIk8cWRRrUVRZbFwBnj8pKeZ0CJuMWoZ/rIbE4pyuKVm05X//AGOjaTLtpOCaaeiY/IsXDl1Cze2FnJlgOx9bW/fXcfZzyyPu0N+L883kSt08uUBK1lk3HOxEL8QYmN+2tV/1u1U2zRAwOUW3O7Obo6RYlhbIQfDvKOzinDM9l5TF483XqwPqqMbXr4j2v7kca4sQWCIR54bTcP/b8yci1GKps87XrHHn+Q9/dWs2jKkLh7JPJtJhZNGcq6bce47s9bOdbQwovXnab6vxPDeyQS7VjuCL94XY593qPvcefaHTzyYRWnFGXxh+/NjspMz51USJ3Lx/Zw9hgIhlIeMHKw1sW5T3zIpc9u5OUth7lsRhEPfesULEY9p47M5pM2v4dNYTslVcYV2DhU6yYQpzSvoka+6zx7ovwzLbCbyLMa2VPZTCAY4r3yKs6dVBhT9dIZhmWb0Wk1SS+uXxxpwKTX8q3pRby+8wS7TzTT1OKP6if0zVNjL+yRDCgRB/jbjafz2o/mxzSnGchY2lan+FP3xAGumJrN8CwzP399T1Rmtbmijt+t/4o6l4/Vn6S3t8jfth7BbNBy6fTkv6DxmD06F6tRxwf7alS/cEhma3UKtO7abG1DG71jE+R2DOMKYvcsTBuRze4TTXxyoA5vIKTuRQBZUEfkZKjipRAKSbz5ZSWXPPURf9l8mJsWjOW/L5O3YFe0WylRg9sX5OI4zdsUlswtptkTYPeJZp67Zqa62Q2gZIgs5l31xY81uNl9opnvnl7MRVOH8n/bjzMyy8hL3z8tZpTd2SWF6LQa3iuvYtfxJi556iPOfOy9duvaAV78+AAnGlv41RXT+OzeC/jNd6ard46nj81l57GmqN/nxg7YKQDjCuROmPE28bwZ7nq6sEQWcY1Gw+Th8uLm50fkFgwXTB6a8rmSYdBpKcrOSCrinx9p4JSiLL57ejFuX5AH39gDwPSRrRuHrpg1Iul5BpyImw2pLYAMJGwmPf6gpJZndSQTBzDrtaz4Willlc389O87aHL7aXT7uGPtdkbnWZk/Po/VnxyOW+bWGZzeAK9tP8E3pg0n09x+2VhbjHot88bn8+HeGk42edBoWsVb7TUdFvfI0WwKkecc1yYTB3lByeMP8eeNhzDoNMwdG31rferI7CgRr2r28PWnP2bZy58TCEk8d81MVlxSqorrV+2I6392nSQrwxBznkjOGJvH984YxbNLZnBe6ZCo5yxGPcW5lqgKlbv+voOfrPki6Xnb8s4eeSjLDWeO5cnF09my4jyevGR43IqMLIuB2aNyeHnLEb717EYa3X7MBh3X/HFL0mHmvkCIN3ZWcuGUoSyePTJqAw/A6WPzZF88YvG4ye3vmJ1SGL9CJRAM8betRzhzfD4jclpt1snDMtlX5eTNXZUYdBoWlKSvC2JxbuJacW8gyK7jzcwclcPM4hzGF9rYcrAem0nP+IjF9vb0bMCJ+GBEKb1yh31xeYBsx360XztlGLedN4HXdpzggt98yE2rtlHj8PK7q6bzw7PHU+fy8dr2E2mJ9/UdJ3D5glw1p/Nln+dMLOR4Ywsb99eSZzWpt7+tdopsszi8cTzxCEGPJ+KnjJAtng/31TB7VG7MTtkZI7M53tiiWjYvfHiAfVUOnlx8Ku8sX8glp8gZdXGuBaNOy/7qxItsvkCId8uquGDykKS38BqNhl9cOjVuq2WQ14OUWvGqZg///OI4b355skMzVN/ZU8WEQpu6ozrPZsKc5Pdo0ZShNLr9XHzKMN5evoBXbjoDi0HHkj9+wq4EG6be31tNo9vP5TOK4j4/a1QOeq1GtVQCwRAObwB7B3Zbj8uPL+Lry6s50eThu6ePinq8dFgmvkCItZ8e5Yxx+dg7kVgkYmSuJeGuzT0nmvEFQ8wszkaj0fCd2SMBOHVkVrutRyIRIj4AsLTpZOj1p1YnHolGo+GOC0r4v1vnk2s1svVgPXdcWMK0EdnMG5fHpKF2/vjxgbQ03f/b1iNMHGJnZoTv11EUT/Ozww2qHw6QYzGi12pa7RRPHE88/H1pNPFbQIzJs6p15WdOiM3KTh0px73jaCMub4C/f3aUS04ZxuUzR0T98el1WsYWWJOK+MaKWhyeABdP7dot/KShdg7VufH4g/xj2zGCIQlfMMTmFPqIgLydfsvBei6cMqT9F4f53rzR/Of2s3j66hlkW4wU51lYExbyK36/idWfHI75ffnn58fItxmjLKpI2vriSjlnpjn13+csi4F8m4mK6mgba/UnhxmWZeb80ujRj5PDYw1dviAXtHmuq4zKs1Dv8sVdF/k8vK6i9Fy5fGYRZoOWuWOSL2S2RYj4AEARJWVxs6N2SiRTi7J47UdnsvbmM7hlgdwjQqPRcMOZY9hX5eTj/V2rDd51vImdx5q4es7ILtlew7MzVLtiSESJoFarId9marVTwi1nraZYER+elRG3KZpWq1FneS6IM4dz6nA5U9p+tJF/fn4MhzfA9+aNjhvn+EIbXyUR8bd2ncRm0se9WHSEiUPtBEMS+6udrP3sKDOKs7EYdXy4L7USx/fK5UqTCzvgB+u0GrXxnEJxnoV/3Tqf00bnsvJfu7hp1Tbqw31WGt0+3iuv5punFqFPcteh+OK7jjdxy+ptjM6zsHB07B1TMsYWWKMy8QM1Tj76qpYlc4pjzj0236omPedPTv0ilgpKmaGyzT+Sz480UJSdof7+5tlMvHvHQm7uwK5QECI+IGgd0Ra2U/whTF2ocTXqtcwZkxtVevfN6cPJt5l49v39nS4rC4YkXthwAJNey2Uzki/WpMI54WHakSIO4a336sJmEI0mem6rkpVHbvJpy1kl+YzKszBleOzA7wyjjolD7HxxtIH/3XyYaSOyEt5VTCi0c7TBHVVXfqTOzRdHGth5rJG398jVEF3ZVAKteyRWbT7M4To3S08fxbxxeXywrzqlu6e3d1cxNNPMKZ2oFmpLYaaZ//3+HFZ+rZQP99bwjac/ZtfxJt7YWYk/KHH5zPhWioLii3/nhc1oNBpe+v4cMs0d+37alhmu/uQIBp2G78wZGfNavU7LlOGZTBuRxbCIkZHpYP64fMYX2vjx377g/v/bFdUyentEKaHCiBxLh38XhIgPABRPXPE/fcFQu0OSO4pJr+P28yfwyYF67vu/XR22VSqbWljyh094fccJrj9zTMyCVmdYGLZUhrTpKRPZP8XpCWAz6qMuSMpFL15lisKyheN4786zo94XyfTibDZV1LG/2sn3zhid8K5ifKENSWr1Z2udXs5/8kMue24T33xmI/Uun+qhd4VReVaMOi1rtx3FbtJz8dRhLCwp4Gh9C4eS1CmDXOL44b4aLpg8JOHn7SharYYfnDWWfyw7g5Ak8e3nN/H8BxVMKLTFvTBGovjigZDEH66dzeg4lld7jCuw0uD2U+/yUev08vdtR7lo6jB1P0FbnrpqBs9/d1aHz9MeWRYDb/z4TG44cwyrPjnM156SL2hVzR6ON7ZEta/tLP1zXpkgishhyYFgiGBI6rSdkozvnj6K440tPP9BBXlWI3deODGl9312qJ4b//IZ3kCIJ648lW+3UzKVKqeNzuWyGUWcNyn6FrjAZuLL401s2FfDu2VVZFujLxgGnZZfX3kqc5Js6NBoNOiS6Nn0kdn8dcsR8qxGvn5qYhGeMKR1kW1qURbv7KnCFwzx2LdPIc9qwqjXcmYaZkIadFrGFdooq2zmm9OHk2HUsbCkENjNh3urGZM/JuF7P9hbTYs/2CE/PFWmjcjmtR+dya1//ZytB+v5r4smtmujWYx6HvjGZEbnW5k1qnMip9xlvfjxAV759Chef4gfnJn4O+jOTYFmg05torX8le1c/twmLgjbNl1ZF1IQIj4AiBzRpgxJ7ujCZqr816KJNLh8PP3efoZmmblm7qh23/Pom+VkGHT8Y9m8uNUgncWg0/Kb70yPebwwU87Er/3TVkbmZvDf3zol5jXt1d62h/LHd/Wc4qS3v6PzrOi0Gr4KDwd/a/dJRuZmsHh219YE4jFpqJ2yymauCjd7K86zMCbfyof7arhufnwB++xQPXf9YycjczM6vKCWKgV2Ey//YC7v7qni3BQXDpeeMbpL5xwf/j179v0KSodlsvoHp8b49z3NGePy+PdPzuLOtdv5f19WyjtGh3fdvhIiPgBQMnG3L5jyfM3OotFo+O/LTuFog5tf/Wcv3zx1eNKSrP3VTj473MA9F09Kq4AnY/boXIpzLXxv3mi+e3pyke0s4wvtvPT90zh9bHLhM+q1jM6z8FW1A4fHz6b9dVx7xqhu2ctw1WkjGZJpZmpRq1gtLClgzadH8PiDMb1APvqqhpv+so2hWWZW/2But134Qb7gXpwG2yhVirIzOL90CJOG2rntvAnd+tk6Qq7VyIvfO43VWw7jS7HbaHsIER8AKIM0IjPxrixstodOq+G/Fk3i0mc3suqTw/zw7PEJX/v3z46i02raXcxKJwtLCtjwX+d0+3nOnphaVjmh0M6+agfv763BFwyxqIvlhImYOzaPuW0uKgsnFvDSpkNsPVjPgpLWSpu/f3aUe1/dxdgCK6tumJvydKX+glar4Y/fm93bYcRFq9VwbRfvNKKOl7YjCXoNk16LXqvh9x9W8K1nNwKkfWGzLaeOzGZhSQF//Ohg1Ip7JP5giHWfH+PcSYUJF5QGAxOG2Dhc5+b1HSfItxmZmYbFrFQ5fUweJr2WX76xhzd2nsDjD3Lvq19y1z92MmtUDmtuOn3ACfhgI62ZeCgU4uc//zl79+7FaDTy0EMPMWpU+56poGtoNBruuXiSOtXIZNAm3EyRTm47bzzffn4zf91yhB+cFVvb+l55NbVOn7oTbbAyvtBGMCTxblkVV502skO78bpKhlHHU1fP4LE3y/nRX78gw6CjxR/k5oVjuevCiUnrtQX9g7SK+LvvvovP5+OVV15h+/btPProozz//PPpPIUgAfFEtLuZNSqX+ePz+P2HB/ju6aNiPNe1nx6l0G5Sd1cOVpQ+GJIEF07pHislGYumDOX80iG8vfskaz87yuLZI3vUnxZ0L2kV8W3btnHWWWcBMH36dHbt2pXOwwv6ID8+dwJX/c8nXPrMRuaMyeWUEVlYjXq8Abm96s0Lxw36bG9cgQ2NRh7eMa+d3tDdhU6r4eJThgnxHoCkVcSdTic2W2sFgk6nIxAIoNdHn6asLPEAgp7A4/H0egwDJY4sYNmcPDYdcfGPbUdY9UnrJiCdBmbn+FI+dl/4ProrhrE5Rsblmjjw1b5ejaOjiDj6VgzxSKuI22w2XK7WpjOhUChGwAFKS0vTedoOU1ZW1usxDKQ4lLcGQxJH6t34w834M82GDg3m6AvfR3fF8Pq4Cei12pRLyvrCdyHi6DsxbNu2LeFzab3PnTlzJhs2bABg+/btlJSUpPPwgj6OTqthTL6VkiF2SobYB81kpVSwGPV9plZZMLBIayZ+wQUXsHHjRq666iokSeLhhx9O5+EFAoFA0Ia0irhWq+WXv/xlOg8pEAgEgiSI+zuBQCDoxwgRFwgEgn6MEHGBQCDoxwgRFwgEgn6MEHGBQCDox2ikdIwv7wDJitYFAoFAEJ9Zs+KPj+txERcIBAJB+hB2ikAgEPRjhIgLBAJBPyYtOzb9fj8rVqzg+PHj+Hw+li1bxvjx47nnnnvQaDRMmDCBBx54AK1Wy9q1a1mzZg16vZ5ly5Zxzjnn0NjYyF133YXT6SQ7O5uHHnqIvLyOtezsSAwA9fX1XHXVVbz++uuYTCY8Hg933XUXdXV1WK1WHnvsMXJzE09D785YAA4fPsytt97KG2+80SsxOBwO9Wfi9/u55557mDFjRo/H4Xa7ufPOO2lqaiIjI4PHH3+8wz+XdPw8ACoqKli8eDGbNm2Keryn4pAkiQULFjB69GhAbvd855139ngcwWCQRx55hF27duHz+fjxj3/MOed0fBxeV+P4n//5Hz766CMAmpubqa2tZePGjT0ag8PhYPny5bS0tGAwGHj88ccpKOjh/vlSGvjHP/4hPfTQQ5IkSVJ9fb20cOFC6eabb5Y++eQTSZIk6b777pPefvttqbq6Wvr6178ueb1eqbm5Wf3/Rx99VHr++eclSZKkjRs3SitWrOi2GCRJkjZs2CBdeuml0owZMySPxyNJkiT96U9/kp566ilJkiTpjTfekB588MFu/z4SxfLqq69Kl112mTRv3rxei+F3v/ud9Oc//1mSJEmqqKiQvvWtb/VKHH/+85+lp59+WpIkSVq3bl2nfi5djUGSJMnhcEg33nijdPrpp0c93pNxHDp0SLr55ps7de50xrFu3TrpgQcekCRJkk6ePKn+nvR0HJHcdNNN0oYNG3o8hpdeekl67LHHJEmSpFdeeUV65JFHOhxDV0mLnXLRRRfxk5/8RP23Tqdj9+7dzJkzB4AFCxawadMmdu7cyYwZMzAajdjtdoqLiykvL2f//v0sWLAAkDshdqaCJdUYQO7x8uc//5ns7Gz19ZEDLRYsWMDmzZs7HEO6YsnKymL16tWdPn86Yrjuuuu46qqrAAgGg53KPNMVx7JlywA4ceIE+fkdHzvX1RgkSeK+++7jjjvuICMjo8PnT1ccu3fvpqqqiqVLl3LjjTdy4MCBXonj448/ZujQodx0002sXLmSc889t1fiUHj77bfJzMxU/357MoaSkhK1/bbT6Yzberu7SYuIW61WbDYbTqeT2267jdtvvx1JktBoNOrzDocDp9OJ3W6Pep/T6aS0tJT33nsPgPfeew+Px9NtMQDMnz+fnJzoYbWRsUW+tjN0NZZzzjkHi8XS6fOnI4bMzEzMZjM1NTXcdddd3HHHHb0SB8h/WNdeey2rV69m4cKFPR7DM888w8KFC5k0aVKHz53OOAoKCrjppptYtWoVN998M3fddVevxNHQ0MDhw4d54YUXuPHGG/nZz37WK3EovPDCC/zoRz/qlRhycnLYuHEjl1xyCS+++CJXXHFFp+LoCmlb2KysrOTaa6/l0ksv5Rvf+IbqIQG4XC4yMzNjhka4XC7sdjs33XQTx48f57rrrqOyspKhQzs3hzCVGBIRGVt7r+3uWNJFV2PYu3cv1113HcuXL1czk96IA+Avf/kLL7/8Mj/+8Y97PIbXXnuNdevWsXTpUmpqarj++us7FUNX45g6dSrnnXceALNnz6aqqgqpkxXCXYkjOzubs88+G41Gw5w5czh06FCnYuhqHAD79+8nMzOzSwPZuxLDM888ww9+8AP+/e9/8+KLL3b697MrpEXEa2truf7667nrrrvUK9HkyZPZsmULABs2bGD27NlMmzaNbdu24fV6cTgcVFRUUFJSwmeffcall17KSy+9xIgRI5g5c2a3xZCImTNn8uGHH6qvTVRY3xOxpIOuxrB//35+8pOf8MQTT3Qq+01XHC+88AL/+te/ALBYLOh0uoSv7a4Y3nnnHVatWsWqVasoKCjgT3/6U4djSEcczzzzDP/7v/8LQHl5OcOHD1czxp6MY9asWerfSnl5OcOGdW5uZzr+TjZt2qRasb0RQ2ZmpnoHn5eXF5Wk9hRp2ezz0EMP8eabbzJ2bOvE9XvvvZeHHnoIv9/P2LFjeeihh9DpdKxdu5ZXXnkFSZK4+eabWbRoEYcPH+buu+8GoLCwkIcffjhqVme6Y1A499xzefPNNzGZTLS0tHD33XdTU1ODwWDgiSee6PQqc1djUZg/f36HV9vTFcOyZcvYu3cvRUVFgHyn8vzzz/d4HLW1tdx99934fD6CwSB33nlnhy+w6fp5JHu8J+Joamrirrvuwu12o9PpuP/++xk3blyPx+Hz+XjggQeoqKhAkiR+/vOfM2XKlB6PA+AXv/gF8+fP5/zzz+/w+dMRQ1VVFStXrsTtdhMIBLjtttuYP39+p2LpLGLHpkAgEPRjxGYfgUAg6McIERcIBIJ+jBBxgUAg6McIERcIBIJ+jBBxgUAg6McIERcIBIJ+jBBxgUAg6McIERcIBIJ+zP8HfZZb3ChNXxMAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# kemudian direkap kembali setiap tahunnya dengan nilai maksimum\n",
"\n",
"dataset_r24 = dataset_monthly.resample('YS').max()\n",
"dataset_r24"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "cg8k1As3UL_l",
"outputId": "1016bdd2-ddd9-499d-8bda-18a2599caac1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" hujan\n",
"2009-01-01 125.000000\n",
"2010-01-01 307.000000\n",
"2011-01-01 173.333333\n",
"2012-01-01 28.750000\n",
"2013-01-01 92.400000\n",
"2014-01-01 37.050000\n",
"2015-01-01 36.100000\n",
"2016-01-01 34.675000\n",
"2017-01-01 81.425000\n",
"2018-01-01 48.750000"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hujan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>307.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>28.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"source": [
"# UJI OUTLIER (BULAN DAN TAHUN)"
],
"metadata": {
"id": "TmEW0ag_OQgK"
}
},
{
"cell_type": "code",
"source": [
"# periksa data dengan uji outlier untuk data bulanan\n",
"\n",
"from hidrokit.contrib.taruma import hk151\n",
"\n",
"outlier_boundary_monthly = hk151.calc_boundary(dataset_monthly[dataset_monthly.hujan != 0])\n",
"outlier_boundary_monthly"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ya5OkrBZO9lM",
"outputId": "5bedccf1-ee35-490e-826b-3b42e1f3a21a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1.5436683423343482, 691.7076981433444)"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"# periksa data dengan uji outlier untuk data bulanan\n",
"\n",
"from hidrokit.contrib.taruma import hk151\n",
"\n",
"outlier_monthly = hk151.find_outlier(dataset_monthly[dataset_monthly.hujan != 0])\n",
"\n",
"if outlier_monthly is not None:\n",
" print(outlier_monthly[outlier_monthly.outlier.notnull()])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "co8ljh52hvxC",
"outputId": "68ea8f2e-2b85-4739-cf18-fcbfad9bdf08"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" hujan outlier\n",
"2012-07-01 0.95 lower\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def plot_outlier(dataset, outlier_boundary, title='uji outlier'):\n",
" fig, ax = plt.subplots(figsize=(20,4))\n",
"\n",
" ax.scatter(dataset.index, dataset.hujan, label='Hujan', marker='o')\n",
" ax.set_ylabel('Hujan (mm)')\n",
" ax.set_xlabel('Tanggal')\n",
" ax.set_ylim([0, max([outlier_boundary[1], dataset.hujan.max()]) + 50])\n",
" ax.axhline(y=outlier_boundary[0], ls='--', c='red')\n",
" ax.axhline(y=outlier_boundary[1], ls='--', c='red')\n",
" ax.legend();\n",
" \n",
" fig.suptitle(title.title(), size='x-large', fontweight='bold')\n",
" fig.tight_layout(rect=[0, 0.03, 1, 1])\n",
"\n",
" return fig, ax"
],
"metadata": {
"id": "KDpeUNIKTfz8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plot_outlier(dataset_monthly, outlier_boundary_monthly, title='uji outlier data bulanan');"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 294
},
"id": "MjKfcYxiOU5t",
"outputId": "b2d9dbc6-eafc-4abc-b684-009904adef0d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1440x288 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# uji outlier untuk data tahunan\n",
"\n",
"outlier_boundary_r24 = hk151.calc_boundary(dataset_r24)\n",
"outlier_boundary_r24"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uwHp11KNiryB",
"outputId": "24013250-ea2c-4716-e6a6-2b4646bfb42c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(14.043086515260704, 358.9172589309031)"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"plot_outlier(dataset_r24, outlier_boundary_r24, title='uji outlier data r24');"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 294
},
"id": "LPGxTA67Tmz-",
"outputId": "b80251f1-d6de-4e0e-f9ff-6ec1004a1789"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1440x288 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"if hk151.find_outlier(dataset_r24) is not None:\n",
" print('Ada Outlier')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9mEXvm6CT3Vn",
"outputId": "eab88aae-ef4f-483e-e5e6-bb9c76f6f9ec"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tidak ada Outlier\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# PARAMETER STATISTIK"
],
"metadata": {
"id": "McA9MBhqnGpF"
}
},
{
"cell_type": "code",
"source": [
"# uji dispersi atau parameter statistik untuk lihat jenis sebaran\n",
"\n",
"from hidrokit.contrib.taruma import hk158\n",
"\n",
"hk158.check_distribution(dataset_r24.hujan, show_stat=True, show_detail=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3eZhbvRxm-Ll",
"outputId": "ab859dad-671c-4162-929b-b4e5e40e9d30"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cv = 0.90879\n",
"Cs = 1.79945\n",
"Ck = 6.94973\n",
"Normal : Tidak Memenuhi\n",
"Log Normal : Tidak Memenuhi\n",
"Gumbel Tipe I : Tidak Memenuhi\n",
"Log Pearson Tipe III: Memenuhi\n",
"-----------------------------------------------\n",
"> Distribusi Normal [syarat](nilai)\n",
"[Cs ~ 0](Cs = 1.79945)\n",
"[Ck ~ 3](Ck = 6.94973)\n",
"> Log Normal\n",
"[Cs ~ 3](Cs = 1.79945)\n",
"[Cs > 0](Cs = 1.79945)\n",
"[Cs ~ 3Cv](Cs = 1.79945 ~ 3Cv = 2.72637)\n",
"> Gumbel Tipe I\n",
"[Cs ~ 1.1396](Cs = 1.79945)\n",
"[Ck ~ 5.4002](Ck = 6.94973)\n",
"> Log Pearson Tipe III\n",
"Tidak memiliki ciri khas (Cs/Ck/Cv = Bebas)\n",
"-----------------------------------------------\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# ANALISIS FREKUENSI"
],
"metadata": {
"id": "sU3_6c_enxFk"
}
},
{
"cell_type": "code",
"source": [
"from hidrokit.contrib.taruma import anfrek"
],
"metadata": {
"id": "ELAWPVyQnahC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## NORMAL (hk172)"
],
"metadata": {
"id": "zJThs9i5n-kG"
}
},
{
"cell_type": "code",
"source": [
"# pada hk172 terdapat 1 sumber tabel (yaitu soewarno)\n",
"# defaultnya menggunakan scipy\n",
"\n",
"normal_source = ['soewarno', 'scipy']\n",
"\n",
"table_normal = []\n",
"\n",
"for _source in normal_source:\n",
" table_normal.append(anfrek.freq_normal(dataset_r24, 'hujan', source=_source, col_name=f'Normal ({_source.title()})'))\n",
"\n",
"table_normal = pd.concat(table_normal, axis=1)\n",
"table_normal"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "x5ahJ_Apn94B",
"outputId": "2967b714-5611-4a30-db82-f39ed53aa8ef"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Normal (Soewarno) Normal (Scipy)\n",
"Kala Ulang \n",
"2 96.448333 96.448333\n",
"5 170.075460 170.217563\n",
"10 208.642050 208.778047\n",
"20 240.196533 240.621959\n",
"25 246.186041 249.898315\n",
"50 276.133582 276.462179\n",
"100 300.675958 300.355844"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Normal (Soewarno)</th>\n",
" <th>Normal (Scipy)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>96.448333</td>\n",
" <td>96.448333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>170.075460</td>\n",
" <td>170.217563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>208.642050</td>\n",
" <td>208.778047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>240.196533</td>\n",
" <td>240.621959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>246.186041</td>\n",
" <td>249.898315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>276.133582</td>\n",
" <td>276.462179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>300.675958</td>\n",
" <td>300.355844</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbandingan hasil\n",
"\n",
"table_normal.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 274
},
"id": "64qPD_cTrAIl",
"outputId": "6b29bb15-e0c9-4448-caa1-735e293bfc95"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# menghitung delta/selisih terhadap nilai scipy (default)\n",
"\n",
"table_normal['delta'] = table_normal['Normal (Scipy)'] - table_normal['Normal (Soewarno)']\n",
"table_normal "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "5hjBiqEcq3PR",
"outputId": "e150cd07-63b1-47b6-cace-5cbc9ca934a8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Normal (Soewarno) Normal (Scipy) delta\n",
"Kala Ulang \n",
"2 96.448333 96.448333 0.000000\n",
"5 170.075460 170.217563 0.142103\n",
"10 208.642050 208.778047 0.135997\n",
"20 240.196533 240.621959 0.425427\n",
"25 246.186041 249.898315 3.712274\n",
"50 276.133582 276.462179 0.328597\n",
"100 300.675958 300.355844 -0.320114"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Normal (Soewarno)</th>\n",
" <th>Normal (Scipy)</th>\n",
" <th>delta</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>96.448333</td>\n",
" <td>96.448333</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>170.075460</td>\n",
" <td>170.217563</td>\n",
" <td>0.142103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>208.642050</td>\n",
" <td>208.778047</td>\n",
" <td>0.135997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>240.196533</td>\n",
" <td>240.621959</td>\n",
" <td>0.425427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>246.186041</td>\n",
" <td>249.898315</td>\n",
" <td>3.712274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>276.133582</td>\n",
" <td>276.462179</td>\n",
" <td>0.328597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>300.675958</td>\n",
" <td>300.355844</td>\n",
" <td>-0.320114</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"source": [
"# plot delta\n",
"# (table_normal.delta / table_normal['Normal (Scipy)'] * 100).plot.bar(); # dalam persentase\n",
"\n",
"table_normal.delta.plot.bar();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "mFAfZh-vrLEs",
"outputId": "869ea56b-52d1-4ade-db83-124eb209bed4"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# anfrek.normal.t_normal_sw\n",
"# nilai k untuk 25 tahun hasil interpolasi"
],
"metadata": {
"id": "3e5HQQXdWF27"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## LOG NORMAL (hk124)"
],
"metadata": {
"id": "EmmsuQDHrX6-"
}
},
{
"cell_type": "code",
"source": [
"# sama halnya dengan distribusi normal, terdapat 1 tabel yaitu soewarno\n",
"# sumber default='scipy'\n",
"\n",
"lognormal_source = ['soewarno', 'scipy']\n",
"\n",
"table_lognormal = []\n",
"\n",
"for _source in lognormal_source:\n",
" table_lognormal.append(anfrek.freq_lognormal(dataset_r24, 'hujan', source=_source, col_name=f'Log Normal ({_source.title()})'))\n",
"\n",
"table_lognormal = pd.concat(table_lognormal, axis=1)\n",
"table_lognormal"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "0PjHfmlkqZ8U",
"outputId": "e72a7832-13c4-424c-e990-b3b460dc9040"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Log Normal (Soewarno) Log Normal (Scipy)\n",
"Kala Ulang \n",
"2 70.995113 70.995113\n",
"5 138.542728 138.721613\n",
"10 196.641235 196.884220\n",
"20 261.886173 262.899814\n",
"25 276.523943 286.004214\n",
"50 362.938965 364.023524\n",
"100 453.543231 452.226795"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Log Normal (Soewarno)</th>\n",
" <th>Log Normal (Scipy)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>70.995113</td>\n",
" <td>70.995113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>138.542728</td>\n",
" <td>138.721613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>196.641235</td>\n",
" <td>196.884220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>261.886173</td>\n",
" <td>262.899814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>276.523943</td>\n",
" <td>286.004214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>362.938965</td>\n",
" <td>364.023524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>453.543231</td>\n",
" <td>452.226795</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 37
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbandingannya\n",
"\n",
"table_lognormal.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 274
},
"id": "ApL0vj_JrsPV",
"outputId": "65914fb7-3474-4608-bad9-27eedcbe4745"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# menghitung delta/selisih\n",
"\n",
"table_lognormal['delta'] = table_lognormal['Log Normal (Scipy)'] - table_lognormal['Log Normal (Soewarno)']\n",
"table_lognormal"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "9oDe0CTEryge",
"outputId": "b367f5d4-4ffd-4767-f398-611ddafd6629"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Log Normal (Soewarno) Log Normal (Scipy) delta\n",
"Kala Ulang \n",
"2 70.995113 70.995113 0.000000\n",
"5 138.542728 138.721613 0.178886\n",
"10 196.641235 196.884220 0.242985\n",
"20 261.886173 262.899814 1.013641\n",
"25 276.523943 286.004214 9.480271\n",
"50 362.938965 364.023524 1.084558\n",
"100 453.543231 452.226795 -1.316435"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Log Normal (Soewarno)</th>\n",
" <th>Log Normal (Scipy)</th>\n",
" <th>delta</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>70.995113</td>\n",
" <td>70.995113</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>138.542728</td>\n",
" <td>138.721613</td>\n",
" <td>0.178886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>196.641235</td>\n",
" <td>196.884220</td>\n",
" <td>0.242985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>261.886173</td>\n",
" <td>262.899814</td>\n",
" <td>1.013641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>276.523943</td>\n",
" <td>286.004214</td>\n",
" <td>9.480271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>362.938965</td>\n",
" <td>364.023524</td>\n",
" <td>1.084558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>453.543231</td>\n",
" <td>452.226795</td>\n",
" <td>-1.316435</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"source": [
"# plot delta\n",
"\n",
"table_lognormal.delta.plot.bar();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "p8EHnawGsAFl",
"outputId": "3e5eed3e-c043-4889-bcaa-4315436b424f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEMCAYAAAACt5eaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAR0ElEQVR4nO3deWwUBf/H8U9beqRbSwPihXJGcDFelJgYwQt4CooCBio0Vjz/IKi0BUVLrQdQahSRQyiHwo+CIEQ0GpUYRG0QUnUjqLASIkFTIDVQlB6uXdj9/fFoHyrFtsMus198v/5yp7PT747pO8Ps7GxcOBwOCwAQ0+LdHgAA0DpiDQAGEGsAMIBYA4ABxBoADCDWAGBAm2K9c+dO5ebmSpJ++uknjR8/Xjk5OXr22WcVCoWiOiAAoA2xXrZsmYqKivTHH39IkmbPnq28vDy9+eabCofD+uSTT6I+JAD827Ua627dumnBggVNj3ft2qXrr79eknTTTTdp27Zt0ZsOACBJ6tDaCllZWaqqqmp6HA6HFRcXJ0nyeDyqra1t8Xk+ny9CIwLAv0tmZuYpy1qN9d/Fx//vYLy+vl7p6ent+oWR4vf75fV6o7b9aGN+91ieXWJ+t0V7/tMd6Lb7apB+/fqpsrJSklRRUaEBAwac2WQAgFa1O9bTpk3TggULdM899ygYDCorKysacwEATtKm0yCXXnqp1q9fL0nq2bOnVq9eHdWhAADN8aEYADCAWAOAAcQaAAwg1gBgALEGAAPa/aEYAJHV46kPHDxrX5vX3F96h4PtI9ZwZA0ABhBrADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYACxBgADiDUAGNDByZOCwaCeeuopHThwQPHx8ZoxY4Z69+4d6dkAAH9ydGT9+eef6/jx41q3bp0mTZqkV199NcJjAQBO5ijWPXv21IkTJxQKhVRXV6cOHRwdoAMA2shRZVNTU3XgwAENHz5cR48eVVlZWYvr+f3+MxrunwQCgahuP9qY3z2WZ3ci1l6r9f3v1vyOYr1y5UoNHDhQU6ZM0aFDhzRhwgS9//77Sk5Obrae1+uNyJAt8fv9Ud1+tDG/e2Jv9n1R3XpsvdZY3P/tE+35fT5fi8sdxTo9PV2JiYmSpI4dO+r48eM6ceKE8+kAAP/IUazvv/9+FRYWKicnR8FgUPn5+UpNTY30bACAPzmKtcfj0bx58yI9CwDgNPhQDAAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYACxBgADiDUAGECsAcAAYg0ABhBrADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMKCD0ycuWbJEW7ZsUTAY1Pjx4zV27NhIzgUAOImjWFdWVuqbb77R2rVr9fvvv+uNN96I9FwAgJM4ivXWrVvVp08fTZo0SXV1dXryyScjPRcA4CSOYn306FEdPHhQZWVlqqqq0sSJE7Vp0ybFxcVFej4AgBzGOiMjQ7169VJSUpJ69eql5ORk1dTUqHPnzs3W8/v9ERmyJYFAIKrbjzbmd4/l2Z2Itddqff+7Nb+jWGdmZmrVqlV64IEH9Msvv+j3339XRkbGKet5vd4zne+0/H5/VLcfbczvntibfV9Utx5brzUW93/7RHt+n8/X4nJHsb711lv11VdfacyYMQqHwyouLlZCQsIZDQgAOD3Hl+7xpiIAnD18KAYADCDWAGAAsQYAA4g1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYACxBgADiDUAGECsAcAAYg0ABhBrADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAgDOK9ZEjR3TzzTfrxx9/jNQ8AIAWOI51MBhUcXGxUlJSIjkPAKAFjmP94osvaty4cbrgggsiOQ8AoAUdnDxp48aN6tSpkwYNGqSlS5eedj2/3+94sNYEAoGobj/amN89lmd3ItZeq/X979b8jmL99ttvKy4uTtu3b5ff79e0adO0ePFidenSpdl6Xq83IkO2xO/3R3X70cb87om92fdFdeux9Vpjcf+3T7Tn9/l8LS53FOs1a9Y0/Xdubq6ee+65U0INAIgcLt0DAAMcHVmfrLy8PBJzAAD+AUfWAGAAsQYAA4g1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYACxBgADiDUAGECsAcAAYg0ABhBrADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAAGINAAZ0cPKkYDCowsJCHThwQI2NjZo4caIGDx4c6dkAAH9yFOv33ntPGRkZeumll3T06FGNHj2aWANAFDmK9bBhw5SVldX0OCEhIWIDAQBO5SjWHo9HklRXV6fHH39ceXl5La7n9/sdD9aaQCAQ1e1HG/O7x/LsTsTaa7W+/92a31GsJenQoUOaNGmScnJydOedd7a4jtfrdTxYa/x+f1S3H23M757Ym31fVLceW681Fvd/+0R7fp/P1+JyR7E+fPiwHnzwQRUXF+uGG244o8EAAK1zdOleWVmZjh07pkWLFik3N1e5ubkKBAKRng0A8CdHR9ZFRUUqKiqK9CwAgNPgQzEAYACxBgADiDUAGECsAcAAYg0ABhBrADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwADH97MGAEnq8dQHDp7V9nt47y+9w8H2zz3EGuYRC/wbcBoEAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAAO66B0nRvXMdd60DzpyjWIdCIT333HPas2ePkpKSNHPmTHXv3j3Ss5nCbToBRJOj0yCbN29WY2Oj3nrrLU2ZMkWlpaWRngsAcBJHsfb5fBo0aJAk6dprr9X3338f0aEAAM3FhcPhcHufNH36dP3nP//RzTffLEm65ZZbtHnzZnXo8L+zKj6fT6mpqZGb9G8CgYBSUlKitv1oY373WJ5dYv5IG/5/bT8d6cRHE3q1a/2GhgZlZmaestzROeu0tDTV19c3PQ6FQs1C/Rev1+tk823i9/ujuv1oY373WJ5dYv7Ii26s2/tafT5fi8sdnQbp37+/KioqJEk7duxQnz59nGwGANBGjo6shw4dqi+++ELjxo1TOBxWSUlJpOcCAJzEUazj4+P1wgsvRHoWAMBp8AlGADCAWAOAAcQaAAwg1gBgALEGAAOINQAYQKwBwABiDQAGEGsAMIBYA4ABxBoADCDWAGAAsQYAA4g1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYICjL8wFgHPF/tI72rW+3++X1+uN0jSnx5E1ABhArAHAAGINAAYQawAwgFgDgAHEGgAMINYAYACxBgAD4sLhcDgaG/b5fNHYLACc8zIzM09ZFrVYAwAih9MgAGAAsQYAA4g1ABhgOtaNjY1uj9BuoVBI1dXVCoVCbo8CwBATsd6yZYtuvfVWDR06VB9++GHT8ocfftjFqdqusLBQkrRz505lZWXp0Ucf1YgRI7Rjxw53BwNghon7WZeVlemdd95ROBzW5MmT9ccff2j06NGyciFLVVWVJGnu3LlatmyZevTooerqak2ZMkWrV692ebpz3+bNm7V9+3bV1tYqPT1dmZmZGjZsmOLi4twerVUfffSRhg8froaGBi1YsEA//PCDrrzySk2cOFEej8ft8drE8v6XpGAwqD179jTNf/nllyspKemsz2Ei1omJicrIyJAkLVq0SBMmTNDFF19s5n/2XxISEtSjRw9J0oUXXmjqVIjVP7jnn39eoVBIN910kzwej+rr61VRUaGtW7dq1qxZbo/XqrVr12r48OGaNWuWLrvsMhUVFWn79u0qLi7WnDlz3B6vVdb3/2effaY5c+aoR48eSk1NVX19vfbt26eCggINGTLkrM5iItZdu3bV7NmzNXnyZKWlpWnhwoV66KGHdOzYMbdHa5Pa2lrdfffdamho0IYNG3TXXXeptLRUl1xyidujtYnlP7i9e/ee8q+XwYMHa9y4cS5N5MxPP/3UtK979+6tjz/+2OWJ2sb6/i8rK9PatWuVlpbWtKy2tlb3338/sW5JSUmJ3nvvvaajuIsvvlirVq3SkiVLXJ6sbd555x01Njbqhx9+UEpKiuLi4tSnTx+NGTPG7dHaxPIfXCgU0tdff60BAwY0Lfvqq6+UmJjo4lRtt3//fq1cuVIJCQnavXu3+vXrp++++87Mm+st7f8vv/zSzP4PBoNKSUlptiw5OdmVf1HyCUa0KicnRwUFBacEb/78+SovL3dxstb9/PPPmj17tnbv3q1wOKz4+Hh5vV7l5eWpb9++bo/Xqt27d2v37t3atWuXrr76ag0ZMkQPPfSQnn76aV133XVuj9eqv+//I0eO6MYbb9T06dPVvXt3t8dr1fr161VeXq7MzEydd955qqurk8/nU25ursaOHXtWZyHWaNVff3C7du1qCl6/fv00bdq0pnPwsWrLli2aMWOGEhISlJ+frzvu+O+Xo953331atWqVy9O17q/5O3TooLy8PHPzFxYWqqSkRDt37tTUqVOVkZGhuro6lZaW6pprrnF7vDY5fPiwvv32W9XX1ystLU1XXXWVzj///LM+h4nTIHBXt27dtHjxYrfHcKSsrEzvvvuuQqGQJk+erMbGRlNXEv39Sihr858LV0Lt2LFD27ZtU11dndLT0xUIBFx5c51Yo1W5ubkKBoMt/mzdunVneZr2SUxMVMeOHSXZvJKIK6HcFUtvrhNrtGrq1KkqKirSa6+9poSEBLfHaRfrVxJZn9/6lVCx9OY6sUarrrnmGo0cOVJ79uzR0KFD3R6nXaxfSWR9futXQsXS1US8wQgApxFLb64TawAwgNMgAHAasfTmOkfWAHAaO3fuPO2b6127dj2rsxBrAPgHy5cvV/fu3V1/c51YA4ABJr58AAD+7Yg1ABhArBFzKisrlZ+f3/R406ZNGjFihA4ePNji+lVVVcrOzm7Ttjdu3KiXX3652bL8/HxVVla2+DMgVnDpHmLaBx98oNdff10rV6505U5nQKwg1ohZ7777rlavXq0VK1Y03Yzpyy+/1MKFCyVJgUBAL774YrOP/m7atElr1qxpejxv3jx16tSp3b97zpw5+v7771VfX6/evXtr9uzZWrBggaqqqnTkyBEdPHhQTz/9tAYNGqRPP/1U8+fPV1pamjp27Ki+ffvqscceO8NXDzTHaRDEpK+//lrr16/Xb7/9phMnTjQt37t3r1566SWtWrVKt912mzZt2tTsefv379fSpUtVXl6unj17auvWrW36fSffxe6vW2GuWLFC69at044dO1RdXS1JSkpK0vLlyzV9+nStXLlSJ06c0MyZM7Vs2TKVl5crOTk5Aq8eOBVH1ohJXbp00YoVK7RhwwY98cQTWrZsmeLj43XhhRdq1qxZSk1NVXV1tfr379/seZ07d9a0adPk8Xi0b98+XXvttc1+npKScspXYjU0NDT76qbk5GTV1NSooKBAqampamhoaPoUm9frlSRddNFFamxsVE1NjdLS0ppO0QwYMECHDx+O9O4AOLJGbOrevbuSk5N17733KjExsenLD4qKilRSUqLS0lJdcMEFzW7CX1tbq/nz52vu3LmaOXOmkpOTT7lJ/xVXXKFt27apvr5ekvTrr79q79696t27d9M6FRUVOnTokF555RUVFBQoEAg0befv95Hu3Lmz6uvrVVNTI+m/n3gDooEja8S8kpISjRo1SpmZmRo5cqSys7OVnp6u888/X7/88kvTemlpaerfv79Gjx6t1NRUpaenN/u5JPXq1Us5OTnKycmRx+PR8ePHNX36dHk8nqZ1rr76ai1atEjZ2dlKSkrSZZdddsp2/hIfH69nnnlGjzzyiM477zyFQiET3y0Ie/gEI3CGlixZogceeEBJSUmaOnWqBg4cqFGjRrk9Fs4xHFkDZ8jj8Sg7O1spKSnq2rWrbr/9drdHwjmII2sAMIA3GAHAAGINAAYQawAwgFgDgAHEGgAMINYAYMD/A0MEOW7MsInkAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## LOG PEARSON III (hk126)"
],
"metadata": {
"id": "NjrbyswisNnk"
}
},
{
"cell_type": "code",
"source": [
"# pada logpearson tersedia 3 tabel sumber yaitu soewarno, soetopo, dan limantara\n",
"# default=scipy\n",
"\n",
"logpearson3_source = ['soewarno', 'soetopo', 'limantara', 'scipy']\n",
"\n",
"table_logpearson3 = []\n",
"\n",
"for _source in logpearson3_source:\n",
" table_logpearson3.append(anfrek.freq_logpearson3(dataset_r24, 'hujan', source=_source, col_name=f'Log Pearson III ({_source.title()})'))\n",
"\n",
"table_logpearson3 = pd.concat(table_logpearson3, axis=1)\n",
"table_logpearson3"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "nGGbhEqesECZ",
"outputId": "ab215ffd-3400-4265-8438-db3ab59643a5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Log Pearson III (Soewarno) Log Pearson III (Soetopo) \\\n",
"Kala Ulang \n",
"2 72.249193 65.297940 \n",
"5 133.817500 133.817500 \n",
"10 204.591937 204.689662 \n",
"20 308.496053 308.520608 \n",
"25 334.905154 334.905154 \n",
"50 470.530987 470.530987 \n",
"100 648.727728 648.727728 \n",
"\n",
" Log Pearson III (Limantara) Log Pearson III (Scipy) \n",
"Kala Ulang \n",
"2 65.297940 65.287547 \n",
"5 133.817500 133.796200 \n",
"10 204.591937 204.657082 \n",
"20 308.496053 298.615313 \n",
"25 334.905154 334.905154 \n",
"50 470.530987 470.605894 \n",
"100 648.727728 648.779363 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Log Pearson III (Soewarno)</th>\n",
" <th>Log Pearson III (Soetopo)</th>\n",
" <th>Log Pearson III (Limantara)</th>\n",
" <th>Log Pearson III (Scipy)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>72.249193</td>\n",
" <td>65.297940</td>\n",
" <td>65.297940</td>\n",
" <td>65.287547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>133.817500</td>\n",
" <td>133.817500</td>\n",
" <td>133.817500</td>\n",
" <td>133.796200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>204.591937</td>\n",
" <td>204.689662</td>\n",
" <td>204.591937</td>\n",
" <td>204.657082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>308.496053</td>\n",
" <td>308.520608</td>\n",
" <td>308.496053</td>\n",
" <td>298.615313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>470.530987</td>\n",
" <td>470.530987</td>\n",
" <td>470.530987</td>\n",
" <td>470.605894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>648.727728</td>\n",
" <td>648.727728</td>\n",
" <td>648.727728</td>\n",
" <td>648.779363</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbedaan\n",
"\n",
"table_logpearson3.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 274
},
"id": "MaAIGYolsoQa",
"outputId": "7f6149e1-d10a-4ae2-d230-fcfb29427954"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# hitung delta terhadap scipy\n",
"\n",
"for _source in logpearson3_source[:-1]:\n",
" table_logpearson3[f'delta_{_source}'] = table_logpearson3['Log Pearson III (Scipy)'] - table_logpearson3[f'Log Pearson III ({_source.title()})']\n",
"\n",
"table_logpearson3"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "YknVo46BtQh0",
"outputId": "494e0352-c62a-4c73-be1c-27ff7b0b5a94"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Log Pearson III (Soewarno) Log Pearson III (Soetopo) \\\n",
"Kala Ulang \n",
"2 72.249193 65.297940 \n",
"5 133.817500 133.817500 \n",
"10 204.591937 204.689662 \n",
"20 308.496053 308.520608 \n",
"25 334.905154 334.905154 \n",
"50 470.530987 470.530987 \n",
"100 648.727728 648.727728 \n",
"\n",
" Log Pearson III (Limantara) Log Pearson III (Scipy) \\\n",
"Kala Ulang \n",
"2 65.297940 65.287547 \n",
"5 133.817500 133.796200 \n",
"10 204.591937 204.657082 \n",
"20 308.496053 298.615313 \n",
"25 334.905154 334.905154 \n",
"50 470.530987 470.605894 \n",
"100 648.727728 648.779363 \n",
"\n",
" delta_soewarno delta_soetopo delta_limantara \n",
"Kala Ulang \n",
"2 -6.961646 -0.010393 -0.010393 \n",
"5 -0.021300 -0.021300 -0.021300 \n",
"10 0.065145 -0.032580 0.065145 \n",
"20 -9.880740 -9.905294 -9.880740 \n",
"25 0.000000 0.000000 0.000000 \n",
"50 0.074906 0.074906 0.074906 \n",
"100 0.051635 0.051635 0.051635 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Log Pearson III (Soewarno)</th>\n",
" <th>Log Pearson III (Soetopo)</th>\n",
" <th>Log Pearson III (Limantara)</th>\n",
" <th>Log Pearson III (Scipy)</th>\n",
" <th>delta_soewarno</th>\n",
" <th>delta_soetopo</th>\n",
" <th>delta_limantara</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>72.249193</td>\n",
" <td>65.297940</td>\n",
" <td>65.297940</td>\n",
" <td>65.287547</td>\n",
" <td>-6.961646</td>\n",
" <td>-0.010393</td>\n",
" <td>-0.010393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>133.817500</td>\n",
" <td>133.817500</td>\n",
" <td>133.817500</td>\n",
" <td>133.796200</td>\n",
" <td>-0.021300</td>\n",
" <td>-0.021300</td>\n",
" <td>-0.021300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>204.591937</td>\n",
" <td>204.689662</td>\n",
" <td>204.591937</td>\n",
" <td>204.657082</td>\n",
" <td>0.065145</td>\n",
" <td>-0.032580</td>\n",
" <td>0.065145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>308.496053</td>\n",
" <td>308.520608</td>\n",
" <td>308.496053</td>\n",
" <td>298.615313</td>\n",
" <td>-9.880740</td>\n",
" <td>-9.905294</td>\n",
" <td>-9.880740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" <td>334.905154</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>470.530987</td>\n",
" <td>470.530987</td>\n",
" <td>470.530987</td>\n",
" <td>470.605894</td>\n",
" <td>0.074906</td>\n",
" <td>0.074906</td>\n",
" <td>0.074906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>648.727728</td>\n",
" <td>648.727728</td>\n",
" <td>648.727728</td>\n",
" <td>648.779363</td>\n",
" <td>0.051635</td>\n",
" <td>0.051635</td>\n",
" <td>0.051635</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"source": [
"# plot bar\n",
"\n",
"col_delta_logpearson3 = [f'delta_{_source}' for _source in logpearson3_source[:-1]]\n",
"table_logpearson3[col_delta_logpearson3].plot.bar();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "9Kg7dJWBtqw5",
"outputId": "5e622817-776d-45d5-9de0-309f1033fdf3"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# print(1/20) # 0.05\n",
"# nilai 0.05 pada tabel diperoleh hasil interpolasi antara 0.1 dan 0.04\n",
"# anfrek.logpearson3.t_pearson3_st"
],
"metadata": {
"id": "oWtQ91uIWy5B"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## GUMBEL (hk127)"
],
"metadata": {
"id": "82a1QcGCzvQu"
}
},
{
"cell_type": "code",
"source": [
"# pada gumbel terdapat 3 sumber tabel: soewarno, soetopo, gumbel\n",
"# 1 sumber formula: powell, 1 sumber fungsi: scipy\n",
"# default yang digunakan adalah gumbel \n",
"\n",
"gumbel_source = ['soewarno', 'soetopo', 'powell', 'scipy', 'gumbel']\n",
"\n",
"table_gumbel = []\n",
"\n",
"for _source in gumbel_source:\n",
" table_gumbel.append(anfrek.freq_gumbel(dataset_r24, 'hujan', source=_source, col_name=f'Gumbel ({_source.title()})'))\n",
"\n",
"table_gumbel = pd.concat(table_gumbel, axis=1)\n",
"table_gumbel"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "xV1WLaPFudGl",
"outputId": "746a2ffc-4503-4807-bc29-f3d157a78341"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Gumbel (Soewarno) Gumbel (Soetopo) Gumbel (Powell) \\\n",
"Kala Ulang \n",
"2 87.892998 84.571325 82.048598 \n",
"5 192.512208 189.179519 159.508670 \n",
"10 261.779229 258.439246 210.793978 \n",
"20 328.221818 324.874839 259.988076 \n",
"25 349.298293 345.949095 275.593100 \n",
"50 414.224934 410.868900 323.664791 \n",
"100 478.672167 475.309347 371.381527 \n",
"\n",
" Gumbel (Scipy) Gumbel (Gumbel) \n",
"Kala Ulang \n",
"2 128.573682 84.571325 \n",
"5 227.920085 189.179519 \n",
"10 293.696047 258.439246 \n",
"20 356.789930 324.874839 \n",
"25 376.804150 345.949095 \n",
"50 438.458489 410.868900 \n",
"100 499.657582 475.309347 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gumbel (Soewarno)</th>\n",
" <th>Gumbel (Soetopo)</th>\n",
" <th>Gumbel (Powell)</th>\n",
" <th>Gumbel (Scipy)</th>\n",
" <th>Gumbel (Gumbel)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>87.892998</td>\n",
" <td>84.571325</td>\n",
" <td>82.048598</td>\n",
" <td>128.573682</td>\n",
" <td>84.571325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>192.512208</td>\n",
" <td>189.179519</td>\n",
" <td>159.508670</td>\n",
" <td>227.920085</td>\n",
" <td>189.179519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>261.779229</td>\n",
" <td>258.439246</td>\n",
" <td>210.793978</td>\n",
" <td>293.696047</td>\n",
" <td>258.439246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>328.221818</td>\n",
" <td>324.874839</td>\n",
" <td>259.988076</td>\n",
" <td>356.789930</td>\n",
" <td>324.874839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>349.298293</td>\n",
" <td>345.949095</td>\n",
" <td>275.593100</td>\n",
" <td>376.804150</td>\n",
" <td>345.949095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>414.224934</td>\n",
" <td>410.868900</td>\n",
" <td>323.664791</td>\n",
" <td>438.458489</td>\n",
" <td>410.868900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>478.672167</td>\n",
" <td>475.309347</td>\n",
" <td>371.381527</td>\n",
" <td>499.657582</td>\n",
" <td>475.309347</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbandingan\n",
"\n",
"table_gumbel.plot(figsize=(15,7));"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 437
},
"id": "41Z463fz0NlX",
"outputId": "d2b9ff6e-080d-4220-a388-4bc7a7ef1184"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1080x504 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# hitung selisih/delta\n",
"\n",
"for _source in gumbel_source[:-1]:\n",
" table_gumbel[f'delta_{_source}'] = table_gumbel['Gumbel (Gumbel)'] - table_gumbel[f'Gumbel ({_source.title()})']\n",
"\n",
"table_gumbel"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "baExZa1N0QLh",
"outputId": "0f3d0e5e-d411-4bbd-cb58-ecfc13bcf923"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Gumbel (Soewarno) Gumbel (Soetopo) Gumbel (Powell) \\\n",
"Kala Ulang \n",
"2 87.892998 84.571325 82.048598 \n",
"5 192.512208 189.179519 159.508670 \n",
"10 261.779229 258.439246 210.793978 \n",
"20 328.221818 324.874839 259.988076 \n",
"25 349.298293 345.949095 275.593100 \n",
"50 414.224934 410.868900 323.664791 \n",
"100 478.672167 475.309347 371.381527 \n",
"\n",
" Gumbel (Scipy) Gumbel (Gumbel) delta_soewarno delta_soetopo \\\n",
"Kala Ulang \n",
"2 128.573682 84.571325 -3.321673 0.0 \n",
"5 227.920085 189.179519 -3.332689 0.0 \n",
"10 293.696047 258.439246 -3.339982 0.0 \n",
"20 356.789930 324.874839 -3.346979 0.0 \n",
"25 376.804150 345.949095 -3.349198 0.0 \n",
"50 438.458489 410.868900 -3.356034 0.0 \n",
"100 499.657582 475.309347 -3.362821 0.0 \n",
"\n",
" delta_powell delta_scipy \n",
"Kala Ulang \n",
"2 2.522727 -44.002358 \n",
"5 29.670849 -38.740565 \n",
"10 47.645268 -35.256801 \n",
"20 64.886763 -31.915091 \n",
"25 70.355995 -30.855056 \n",
"50 87.204109 -27.589590 \n",
"100 103.927819 -24.348235 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gumbel (Soewarno)</th>\n",
" <th>Gumbel (Soetopo)</th>\n",
" <th>Gumbel (Powell)</th>\n",
" <th>Gumbel (Scipy)</th>\n",
" <th>Gumbel (Gumbel)</th>\n",
" <th>delta_soewarno</th>\n",
" <th>delta_soetopo</th>\n",
" <th>delta_powell</th>\n",
" <th>delta_scipy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kala Ulang</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>87.892998</td>\n",
" <td>84.571325</td>\n",
" <td>82.048598</td>\n",
" <td>128.573682</td>\n",
" <td>84.571325</td>\n",
" <td>-3.321673</td>\n",
" <td>0.0</td>\n",
" <td>2.522727</td>\n",
" <td>-44.002358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>192.512208</td>\n",
" <td>189.179519</td>\n",
" <td>159.508670</td>\n",
" <td>227.920085</td>\n",
" <td>189.179519</td>\n",
" <td>-3.332689</td>\n",
" <td>0.0</td>\n",
" <td>29.670849</td>\n",
" <td>-38.740565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>261.779229</td>\n",
" <td>258.439246</td>\n",
" <td>210.793978</td>\n",
" <td>293.696047</td>\n",
" <td>258.439246</td>\n",
" <td>-3.339982</td>\n",
" <td>0.0</td>\n",
" <td>47.645268</td>\n",
" <td>-35.256801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>328.221818</td>\n",
" <td>324.874839</td>\n",
" <td>259.988076</td>\n",
" <td>356.789930</td>\n",
" <td>324.874839</td>\n",
" <td>-3.346979</td>\n",
" <td>0.0</td>\n",
" <td>64.886763</td>\n",
" <td>-31.915091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>349.298293</td>\n",
" <td>345.949095</td>\n",
" <td>275.593100</td>\n",
" <td>376.804150</td>\n",
" <td>345.949095</td>\n",
" <td>-3.349198</td>\n",
" <td>0.0</td>\n",
" <td>70.355995</td>\n",
" <td>-30.855056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>414.224934</td>\n",
" <td>410.868900</td>\n",
" <td>323.664791</td>\n",
" <td>438.458489</td>\n",
" <td>410.868900</td>\n",
" <td>-3.356034</td>\n",
" <td>0.0</td>\n",
" <td>87.204109</td>\n",
" <td>-27.589590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>478.672167</td>\n",
" <td>475.309347</td>\n",
" <td>371.381527</td>\n",
" <td>499.657582</td>\n",
" <td>475.309347</td>\n",
" <td>-3.362821</td>\n",
" <td>0.0</td>\n",
" <td>103.927819</td>\n",
" <td>-24.348235</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"# plot delta\n",
"\n",
"col_delta_gumbel = [f'delta_{_source}' for _source in gumbel_source[:-1]]\n",
"table_gumbel[col_delta_gumbel].plot.bar(figsize=(12, 5));"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336
},
"id": "HwK8aPI20nwE",
"outputId": "b466560b-6cdc-45c1-8228-be343c52defd"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# UJI KECOCOKAN DISTRIBUSI"
],
"metadata": {
"id": "lPskSX7f1flL"
}
},
{
"cell_type": "markdown",
"source": [
"## UJI KOLMOGOROV-SMIRNOV TEST (hk140)"
],
"metadata": {
"id": "MKNGZX9u2i0f"
}
},
{
"cell_type": "code",
"source": [
"from hidrokit.contrib.taruma import hk140"
],
"metadata": {
"id": "LkXUfN6M-tnD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# perbandingan nilai delta kritis\n",
"# terdapat 2 sumber tabel: soetopo dan soewarno\n",
"# default: scipy\n",
"\n",
"dcr_source = ['soetopo', 'soewarno', 'scipy']\n",
"table_dcr = {}\n",
"n_test = np.arange(10, 31, 2)\n",
"\n",
"for _source in dcr_source:\n",
" table_dcr[f'dcr ({_source.title()})'] = hk140.calc_dcr(alpha=0.05, n=n_test, source=_source)\n",
"\n",
"table_dcr = pd.DataFrame(table_dcr)\n",
"table_dcr.insert(0, 'n', n_test)\n",
"table_dcr = table_dcr.set_index('n')\n",
"table_dcr"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "9Nq9KMeo4bti",
"outputId": "01182836-3899-4fb8-fe63-4e208e39bf21"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" dcr (Soetopo) dcr (Soewarno) dcr (Scipy)\n",
"n \n",
"10 0.409 0.41 0.409246\n",
"12 0.375 0.38 0.375430\n",
"14 0.349 0.35 0.348901\n",
"16 0.328 0.33 0.327334\n",
"18 0.309 0.31 0.309360\n",
"20 0.294 0.29 0.294076\n",
"22 0.294 0.28 0.280869\n",
"24 0.294 0.27 0.269308\n",
"26 0.294 0.26 0.259075\n",
"28 0.294 0.25 0.249934\n",
"30 0.294 0.24 0.241704"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dcr (Soetopo)</th>\n",
" <th>dcr (Soewarno)</th>\n",
" <th>dcr (Scipy)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>n</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.409</td>\n",
" <td>0.41</td>\n",
" <td>0.409246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.375</td>\n",
" <td>0.38</td>\n",
" <td>0.375430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.349</td>\n",
" <td>0.35</td>\n",
" <td>0.348901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.328</td>\n",
" <td>0.33</td>\n",
" <td>0.327334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.309</td>\n",
" <td>0.31</td>\n",
" <td>0.309360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.294</td>\n",
" <td>0.29</td>\n",
" <td>0.294076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.294</td>\n",
" <td>0.28</td>\n",
" <td>0.280869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.294</td>\n",
" <td>0.27</td>\n",
" <td>0.269308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.294</td>\n",
" <td>0.26</td>\n",
" <td>0.259075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.294</td>\n",
" <td>0.25</td>\n",
" <td>0.249934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.294</td>\n",
" <td>0.24</td>\n",
" <td>0.241704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbandingan nilai delta kritis\n",
"\n",
"table_dcr.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 274
},
"id": "rfxmBdrl8OJ5",
"outputId": "54b08ea7-7d4b-4fb0-e12a-34b5c4ac3d5e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# hk140.t_dcr_st # tabel soetopo hanya sampai n=20"
],
"metadata": {
"id": "X7lqjwJHYQpO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# hitung delta\n",
"\n",
"for _source in dcr_source[:-1]:\n",
" table_dcr[f'delta_{_source}'] = table_dcr['dcr (Scipy)'] - table_dcr[f'dcr ({_source.title()})']\n",
"\n",
"table_dcr"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "psAmCgZK9edj",
"outputId": "db45c693-ad79-4de3-a0b0-c8104c39d370"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" dcr (Soetopo) dcr (Soewarno) dcr (Scipy) delta_soetopo delta_soewarno\n",
"n \n",
"10 0.409 0.41 0.409246 0.000246 -0.000754\n",
"12 0.375 0.38 0.375430 0.000430 -0.004570\n",
"14 0.349 0.35 0.348901 -0.000099 -0.001099\n",
"16 0.328 0.33 0.327334 -0.000666 -0.002666\n",
"18 0.309 0.31 0.309360 0.000360 -0.000640\n",
"20 0.294 0.29 0.294076 0.000076 0.004076\n",
"22 0.294 0.28 0.280869 -0.013131 0.000869\n",
"24 0.294 0.27 0.269308 -0.024692 -0.000692\n",
"26 0.294 0.26 0.259075 -0.034925 -0.000925\n",
"28 0.294 0.25 0.249934 -0.044066 -0.000066\n",
"30 0.294 0.24 0.241704 -0.052296 0.001704"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dcr (Soetopo)</th>\n",
" <th>dcr (Soewarno)</th>\n",
" <th>dcr (Scipy)</th>\n",
" <th>delta_soetopo</th>\n",
" <th>delta_soewarno</th>\n",
" </tr>\n",
" <tr>\n",
" <th>n</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.409</td>\n",
" <td>0.41</td>\n",
" <td>0.409246</td>\n",
" <td>0.000246</td>\n",
" <td>-0.000754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.375</td>\n",
" <td>0.38</td>\n",
" <td>0.375430</td>\n",
" <td>0.000430</td>\n",
" <td>-0.004570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.349</td>\n",
" <td>0.35</td>\n",
" <td>0.348901</td>\n",
" <td>-0.000099</td>\n",
" <td>-0.001099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.328</td>\n",
" <td>0.33</td>\n",
" <td>0.327334</td>\n",
" <td>-0.000666</td>\n",
" <td>-0.002666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.309</td>\n",
" <td>0.31</td>\n",
" <td>0.309360</td>\n",
" <td>0.000360</td>\n",
" <td>-0.000640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.294</td>\n",
" <td>0.29</td>\n",
" <td>0.294076</td>\n",
" <td>0.000076</td>\n",
" <td>0.004076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.294</td>\n",
" <td>0.28</td>\n",
" <td>0.280869</td>\n",
" <td>-0.013131</td>\n",
" <td>0.000869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.294</td>\n",
" <td>0.27</td>\n",
" <td>0.269308</td>\n",
" <td>-0.024692</td>\n",
" <td>-0.000692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.294</td>\n",
" <td>0.26</td>\n",
" <td>0.259075</td>\n",
" <td>-0.034925</td>\n",
" <td>-0.000925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.294</td>\n",
" <td>0.25</td>\n",
" <td>0.249934</td>\n",
" <td>-0.044066</td>\n",
" <td>-0.000066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.294</td>\n",
" <td>0.24</td>\n",
" <td>0.241704</td>\n",
" <td>-0.052296</td>\n",
" <td>0.001704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"source": [
"# plot bar\n",
"\n",
"col_delta_dcr = [f'delta_{_source}' for _source in dcr_source[:-1]]\n",
"table_dcr[col_delta_dcr].plot.bar();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 276
},
"id": "YzGTNa3T-MQG",
"outputId": "c1c9612b-c561-45fb-fefd-443b876a50e1"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### NORMAL"
],
"metadata": {
"id": "D_jztD0w-ipQ"
}
},
{
"cell_type": "code",
"source": [
"from hidrokit.contrib.taruma import ujidist\n",
"\n",
"ujidist.uji_kstest(dataset_r24, dist='normal', report='full')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 432
},
"id": "33TC-PQL2cnU",
"outputId": "3714a72f-4813-489a-a694-995a7be22b3f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Normal\n",
"Delta Kritikal = 0.40925\n",
"Delta Max = 0.16139\n",
"Result (Dmax < Dcr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" x no p_w k p_d d\n",
"2012-01-01 28.750000 1 0.090909 -0.772359 0.219951 0.129042\n",
"2016-01-01 34.675000 2 0.181818 -0.704762 0.240479 0.058661\n",
"2015-01-01 36.100000 3 0.272727 -0.688504 0.245568 0.027160\n",
"2014-01-01 37.050000 4 0.363636 -0.677666 0.248992 0.114645\n",
"2018-01-01 48.750000 5 0.454545 -0.544183 0.293158 0.161388\n",
"2017-01-01 81.425000 6 0.545455 -0.171399 0.431955 0.113499\n",
"2013-01-01 92.400000 7 0.636364 -0.046187 0.481581 0.154783\n",
"2009-01-01 125.000000 8 0.727273 0.325741 0.627690 0.099583\n",
"2011-01-01 173.333333 9 0.818182 0.877169 0.809802 0.008379\n",
"2010-01-01 307.000000 10 0.909091 2.402150 0.991850 0.082760"
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" <th>no</th>\n",
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" <th>k</th>\n",
" <th>p_d</th>\n",
" <th>d</th>\n",
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" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>28.750000</td>\n",
" <td>1</td>\n",
" <td>0.090909</td>\n",
" <td>-0.772359</td>\n",
" <td>0.219951</td>\n",
" <td>0.129042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" <td>2</td>\n",
" <td>0.181818</td>\n",
" <td>-0.704762</td>\n",
" <td>0.240479</td>\n",
" <td>0.058661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" <td>3</td>\n",
" <td>0.272727</td>\n",
" <td>-0.688504</td>\n",
" <td>0.245568</td>\n",
" <td>0.027160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" <td>4</td>\n",
" <td>0.363636</td>\n",
" <td>-0.677666</td>\n",
" <td>0.248992</td>\n",
" <td>0.114645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" <td>5</td>\n",
" <td>0.454545</td>\n",
" <td>-0.544183</td>\n",
" <td>0.293158</td>\n",
" <td>0.161388</td>\n",
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" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" <td>6</td>\n",
" <td>0.545455</td>\n",
" <td>-0.171399</td>\n",
" <td>0.431955</td>\n",
" <td>0.113499</td>\n",
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" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" <td>7</td>\n",
" <td>0.636364</td>\n",
" <td>-0.046187</td>\n",
" <td>0.481581</td>\n",
" <td>0.154783</td>\n",
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" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" <td>8</td>\n",
" <td>0.727273</td>\n",
" <td>0.325741</td>\n",
" <td>0.627690</td>\n",
" <td>0.099583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" <td>9</td>\n",
" <td>0.818182</td>\n",
" <td>0.877169</td>\n",
" <td>0.809802</td>\n",
" <td>0.008379</td>\n",
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" <th>2010-01-01</th>\n",
" <td>307.000000</td>\n",
" <td>10</td>\n",
" <td>0.909091</td>\n",
" <td>2.402150</td>\n",
" <td>0.991850</td>\n",
" <td>0.082760</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "markdown",
"source": [
"### LOG NORMAL"
],
"metadata": {
"id": "7sRBPIzV-oI_"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_kstest(dataset_r24, dist='lognormal', report='full')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 432
},
"id": "CncWNbav3JwM",
"outputId": "41035fba-b675-4436-ccc2-7212050e09fb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Lognormal\n",
"Delta Kritikal = 0.40925\n",
"Delta Max = 0.15670\n",
"Result (Dmax < Dcr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" x no p_w log_x k p_d d\n",
"2012-01-01 28.750000 1 0.090909 1.458638 -1.135768 0.128027 0.037118\n",
"2016-01-01 34.675000 2 0.181818 1.540016 -0.900339 0.183970 0.002152\n",
"2015-01-01 36.100000 3 0.272727 1.557507 -0.849738 0.197735 0.074992\n",
"2014-01-01 37.050000 4 0.363636 1.568788 -0.817102 0.206935 0.156701\n",
"2018-01-01 48.750000 5 0.454545 1.687975 -0.472295 0.318358 0.136187\n",
"2017-01-01 81.425000 6 0.545455 1.910758 0.172219 0.568367 0.022913\n",
"2013-01-01 92.400000 7 0.636364 1.965672 0.331086 0.629710 0.006653\n",
"2009-01-01 125.000000 8 0.727273 2.096910 0.710759 0.761383 0.034110\n",
"2011-01-01 173.333333 9 0.818182 2.238882 1.121485 0.868959 0.050777\n",
"2010-01-01 307.000000 10 0.909091 2.487138 1.839692 0.967093 0.058002"
],
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" <th>2012-01-01</th>\n",
" <td>28.750000</td>\n",
" <td>1</td>\n",
" <td>0.090909</td>\n",
" <td>1.458638</td>\n",
" <td>-1.135768</td>\n",
" <td>0.128027</td>\n",
" <td>0.037118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" <td>2</td>\n",
" <td>0.181818</td>\n",
" <td>1.540016</td>\n",
" <td>-0.900339</td>\n",
" <td>0.183970</td>\n",
" <td>0.002152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" <td>3</td>\n",
" <td>0.272727</td>\n",
" <td>1.557507</td>\n",
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" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" <td>4</td>\n",
" <td>0.363636</td>\n",
" <td>1.568788</td>\n",
" <td>-0.817102</td>\n",
" <td>0.206935</td>\n",
" <td>0.156701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" <td>5</td>\n",
" <td>0.454545</td>\n",
" <td>1.687975</td>\n",
" <td>-0.472295</td>\n",
" <td>0.318358</td>\n",
" <td>0.136187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" <td>6</td>\n",
" <td>0.545455</td>\n",
" <td>1.910758</td>\n",
" <td>0.172219</td>\n",
" <td>0.568367</td>\n",
" <td>0.022913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" <td>7</td>\n",
" <td>0.636364</td>\n",
" <td>1.965672</td>\n",
" <td>0.331086</td>\n",
" <td>0.629710</td>\n",
" <td>0.006653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" <td>8</td>\n",
" <td>0.727273</td>\n",
" <td>2.096910</td>\n",
" <td>0.710759</td>\n",
" <td>0.761383</td>\n",
" <td>0.034110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" <td>9</td>\n",
" <td>0.818182</td>\n",
" <td>2.238882</td>\n",
" <td>1.121485</td>\n",
" <td>0.868959</td>\n",
" <td>0.050777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>307.000000</td>\n",
" <td>10</td>\n",
" <td>0.909091</td>\n",
" <td>2.487138</td>\n",
" <td>1.839692</td>\n",
" <td>0.967093</td>\n",
" <td>0.058002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 57
}
]
},
{
"cell_type": "markdown",
"source": [
"### LOG PEARSON III"
],
"metadata": {
"id": "fFU7gleg_BNl"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_kstest(dataset_r24, dist='logpearson3', report='full')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 432
},
"id": "PHrKrS7S4TwK",
"outputId": "4437d22e-43d8-456d-d6b3-ca6a73c0e69b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Logpearson3\n",
"Delta Kritikal = 0.40925\n",
"Delta Max = 0.14955\n",
"Result (Dmax < Dcr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" x no p_w log_x k p_d d\n",
"2012-01-01 28.750000 1 0.090909 1.458638 -1.135768 0.114777 0.023868\n",
"2016-01-01 34.675000 2 0.181818 1.540016 -0.900339 0.185319 0.003501\n",
"2015-01-01 36.100000 3 0.272727 1.557507 -0.849738 0.202598 0.070129\n",
"2014-01-01 37.050000 4 0.363636 1.568788 -0.817102 0.214091 0.149546\n",
"2018-01-01 48.750000 5 0.454545 1.687975 -0.472295 0.348026 0.106519\n",
"2017-01-01 81.425000 6 0.545455 1.910758 0.172219 0.608311 0.062856\n",
"2013-01-01 92.400000 7 0.636364 1.965672 0.331086 0.664750 0.028386\n",
"2009-01-01 125.000000 8 0.727273 2.096910 0.710759 0.778668 0.051395\n",
"2011-01-01 173.333333 9 0.818182 2.238882 1.121485 0.867339 0.049157\n",
"2010-01-01 307.000000 10 0.909091 2.487138 1.839692 0.952599 0.043508"
],
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" <td>1</td>\n",
" <td>0.090909</td>\n",
" <td>1.458638</td>\n",
" <td>-1.135768</td>\n",
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" <td>0.023868</td>\n",
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" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" <td>2</td>\n",
" <td>0.181818</td>\n",
" <td>1.540016</td>\n",
" <td>-0.900339</td>\n",
" <td>0.185319</td>\n",
" <td>0.003501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" <td>3</td>\n",
" <td>0.272727</td>\n",
" <td>1.557507</td>\n",
" <td>-0.849738</td>\n",
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" <td>0.070129</td>\n",
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" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" <td>4</td>\n",
" <td>0.363636</td>\n",
" <td>1.568788</td>\n",
" <td>-0.817102</td>\n",
" <td>0.214091</td>\n",
" <td>0.149546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" <td>5</td>\n",
" <td>0.454545</td>\n",
" <td>1.687975</td>\n",
" <td>-0.472295</td>\n",
" <td>0.348026</td>\n",
" <td>0.106519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" <td>6</td>\n",
" <td>0.545455</td>\n",
" <td>1.910758</td>\n",
" <td>0.172219</td>\n",
" <td>0.608311</td>\n",
" <td>0.062856</td>\n",
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" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" <td>7</td>\n",
" <td>0.636364</td>\n",
" <td>1.965672</td>\n",
" <td>0.331086</td>\n",
" <td>0.664750</td>\n",
" <td>0.028386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" <td>8</td>\n",
" <td>0.727273</td>\n",
" <td>2.096910</td>\n",
" <td>0.710759</td>\n",
" <td>0.778668</td>\n",
" <td>0.051395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" <td>9</td>\n",
" <td>0.818182</td>\n",
" <td>2.238882</td>\n",
" <td>1.121485</td>\n",
" <td>0.867339</td>\n",
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" <th>2010-01-01</th>\n",
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" <td>2.487138</td>\n",
" <td>1.839692</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"source": [
"### GUMBEL"
],
"metadata": {
"id": "intWxL-d_GDm"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_kstest(dataset_r24, dist='gumbel', report='full')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 432
},
"id": "QNLyquuz_Duc",
"outputId": "a8270441-76df-4927-8989-6df0ba2443e9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Gumbel\n",
"Delta Kritikal = 0.40925\n",
"Delta Max = 0.19009\n",
"Result (Dmax < Dcr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" x no p_w k p_d d\n",
"2012-01-01 28.750000 1 0.090909 -0.772359 0.281 0.190091\n",
"2016-01-01 34.675000 2 0.181818 -0.704762 0.304 0.122182\n",
"2015-01-01 36.100000 3 0.272727 -0.688504 0.310 0.037273\n",
"2014-01-01 37.050000 4 0.363636 -0.677666 0.314 0.049636\n",
"2018-01-01 48.750000 5 0.454545 -0.544183 0.360 0.094545\n",
"2017-01-01 81.425000 6 0.545455 -0.171399 0.488 0.057455\n",
"2013-01-01 92.400000 7 0.636364 -0.046187 0.529 0.107364\n",
"2009-01-01 125.000000 8 0.727273 0.325741 0.639 0.088273\n",
"2011-01-01 173.333333 9 0.818182 0.877169 0.767 0.051182\n",
"2010-01-01 307.000000 10 0.909091 2.402150 0.940 0.030909"
],
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" <td>1</td>\n",
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" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" <td>2</td>\n",
" <td>0.181818</td>\n",
" <td>-0.704762</td>\n",
" <td>0.304</td>\n",
" <td>0.122182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" <td>3</td>\n",
" <td>0.272727</td>\n",
" <td>-0.688504</td>\n",
" <td>0.310</td>\n",
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" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" <td>4</td>\n",
" <td>0.363636</td>\n",
" <td>-0.677666</td>\n",
" <td>0.314</td>\n",
" <td>0.049636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" <td>5</td>\n",
" <td>0.454545</td>\n",
" <td>-0.544183</td>\n",
" <td>0.360</td>\n",
" <td>0.094545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" <td>6</td>\n",
" <td>0.545455</td>\n",
" <td>-0.171399</td>\n",
" <td>0.488</td>\n",
" <td>0.057455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" <td>7</td>\n",
" <td>0.636364</td>\n",
" <td>-0.046187</td>\n",
" <td>0.529</td>\n",
" <td>0.107364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" <td>8</td>\n",
" <td>0.727273</td>\n",
" <td>0.325741</td>\n",
" <td>0.639</td>\n",
" <td>0.088273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" <td>9</td>\n",
" <td>0.818182</td>\n",
" <td>0.877169</td>\n",
" <td>0.767</td>\n",
" <td>0.051182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>307.000000</td>\n",
" <td>10</td>\n",
" <td>0.909091</td>\n",
" <td>2.402150</td>\n",
" <td>0.940</td>\n",
" <td>0.030909</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "markdown",
"source": [
"## UJI CHI SQUARE (hk141)"
],
"metadata": {
"id": "HJB85Y87EjhN"
}
},
{
"cell_type": "code",
"source": [
"from hidrokit.contrib.taruma import hk141"
],
"metadata": {
"id": "sMhiWetI_Iu9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# nilai x kritis terdapat 1 sumber tabel: limantara\n",
"# default: scipy\n",
"\n",
"xcr_source = ['limantara', 'scipy']\n",
"\n",
"table_xcr = {}\n",
"dk_test = np.arange(1, 21, 2)\n",
"\n",
"for _source in xcr_source:\n",
" table_xcr[f'xcr ({_source.title()})'] = hk141.calc_xcr(alpha=0.05, dk=dk_test, source=_source) # .round(3)\n",
"\n",
"table_xcr = pd.DataFrame(table_xcr)\n",
"table_xcr.insert(0, 'dk', dk_test)\n",
"table_xcr = table_xcr.set_index('dk')\n",
"table_xcr"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "TZuXovMJE09d",
"outputId": "2232b7a8-0392-4ae4-a6ba-eaa3da80f8c3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" xcr (Limantara) xcr (Scipy)\n",
"dk \n",
"1 3.841 3.841459\n",
"3 7.815 7.814728\n",
"5 11.070 11.070498\n",
"7 14.067 14.067140\n",
"9 16.919 16.918978\n",
"11 19.675 19.675138\n",
"13 22.362 22.362032\n",
"15 24.996 24.995790\n",
"17 27.587 27.587112\n",
"19 30.144 30.143527"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>xcr (Limantara)</th>\n",
" <th>xcr (Scipy)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>dk</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.841</td>\n",
" <td>3.841459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.815</td>\n",
" <td>7.814728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11.070</td>\n",
" <td>11.070498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>14.067</td>\n",
" <td>14.067140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>16.919</td>\n",
" <td>16.918978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>19.675</td>\n",
" <td>19.675138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>22.362</td>\n",
" <td>22.362032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>24.996</td>\n",
" <td>24.995790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>27.587</td>\n",
" <td>27.587112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>30.144</td>\n",
" <td>30.143527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"source": [
"# plot perbandingan\n",
"\n",
"table_xcr.plot();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 274
},
"id": "oNRqMnIjFTta",
"outputId": "6a52832b-1403-413a-a07d-fd7dc306363c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# hitung selisih/delta\n",
"\n",
"for _source in xcr_source[:-1]:\n",
" table_xcr[f'delta_{_source}'] = table_xcr['xcr (Scipy)'] - table_xcr[f'xcr ({_source.title()})']\n",
"\n",
"table_xcr # .round(3)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "kfs47YwZFdZo",
"outputId": "6283819f-fd61-4948-8280-5b873dbc6e1f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" xcr (Limantara) xcr (Scipy) delta_limantara\n",
"dk \n",
"1 3.841 3.841459 0.000459\n",
"3 7.815 7.814728 -0.000272\n",
"5 11.070 11.070498 0.000498\n",
"7 14.067 14.067140 0.000140\n",
"9 16.919 16.918978 -0.000022\n",
"11 19.675 19.675138 0.000138\n",
"13 22.362 22.362032 0.000032\n",
"15 24.996 24.995790 -0.000210\n",
"17 27.587 27.587112 0.000112\n",
"19 30.144 30.143527 -0.000473"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>xcr (Limantara)</th>\n",
" <th>xcr (Scipy)</th>\n",
" <th>delta_limantara</th>\n",
" </tr>\n",
" <tr>\n",
" <th>dk</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.841</td>\n",
" <td>3.841459</td>\n",
" <td>0.000459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.815</td>\n",
" <td>7.814728</td>\n",
" <td>-0.000272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11.070</td>\n",
" <td>11.070498</td>\n",
" <td>0.000498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>14.067</td>\n",
" <td>14.067140</td>\n",
" <td>0.000140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>16.919</td>\n",
" <td>16.918978</td>\n",
" <td>-0.000022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>19.675</td>\n",
" <td>19.675138</td>\n",
" <td>0.000138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>22.362</td>\n",
" <td>22.362032</td>\n",
" <td>0.000032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>24.996</td>\n",
" <td>24.995790</td>\n",
" <td>-0.000210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>27.587</td>\n",
" <td>27.587112</td>\n",
" <td>0.000112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>30.144</td>\n",
" <td>30.143527</td>\n",
" <td>-0.000473</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"source": [
"# plot delta\n",
"\n",
"col_delta_xcr = ['delta_limantara']\n",
"table_xcr[col_delta_xcr].plot.bar();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 276
},
"id": "4Y9y4KIdFpub",
"outputId": "37658705-c640-4625-bc17-30c59973a7ca"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### NORMAL"
],
"metadata": {
"id": "_ayEYcdMF6SZ"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_chisquare(dataset_r24, dist='normal')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 310
},
"id": "WXWGpOCVFw-b",
"outputId": "d26b65ec-3aa8-4f4e-c7db-bddd2573086a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Normal\n",
"Jumlah Kelas = 4\n",
"Dk = 1\n",
"X^2_hitungan = 2.121\n",
"X^2_kritis = 3.841\n",
"Result (X2_calc < X2_cr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" batas_kelas fe ft\n",
"no \n",
"1 X <= 37.3284 4 2.5\n",
"2 37.3284 < X <= 96.4483 3 2.5\n",
"3 96.4483 < X <= 155.5683 1 2.5\n",
"4 X > 155.5683 2 2.5"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>batas_kelas</th>\n",
" <th>fe</th>\n",
" <th>ft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>no</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>X &lt;= 37.3284</td>\n",
" <td>4</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37.3284 &lt; X &lt;= 96.4483</td>\n",
" <td>3</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>96.4483 &lt; X &lt;= 155.5683</td>\n",
" <td>1</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>X &gt; 155.5683</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 65
}
]
},
{
"cell_type": "markdown",
"source": [
"### LOG NORMAL"
],
"metadata": {
"id": "buN2yXjDGF0c"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_chisquare(dataset_r24, dist='lognormal')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 310
},
"id": "0H98A8PtGCR5",
"outputId": "e82e7197-4d68-4fe6-d058-377e1d97165f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Lognormal\n",
"Jumlah Kelas = 4\n",
"Dk = 1\n",
"X^2_hitungan = 2.121\n",
"X^2_kritis = 3.841\n",
"Result (X2_calc < X2_cr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" batas_kelas fe ft\n",
"no \n",
"1 X <= 41.5034 4 2.5\n",
"2 41.5034 < X <= 70.9951 1 2.5\n",
"3 70.9951 < X <= 121.4432 2 2.5\n",
"4 X > 121.4432 3 2.5"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>batas_kelas</th>\n",
" <th>fe</th>\n",
" <th>ft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>no</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>X &lt;= 41.5034</td>\n",
" <td>4</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>41.5034 &lt; X &lt;= 70.9951</td>\n",
" <td>1</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70.9951 &lt; X &lt;= 121.4432</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>X &gt; 121.4432</td>\n",
" <td>3</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 66
}
]
},
{
"cell_type": "markdown",
"source": [
"### LOG PEARSON III"
],
"metadata": {
"id": "wsOv3S7JGIPf"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_chisquare(dataset_r24, dist='logpearson3')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 310
},
"id": "o9WdZduQGHcE",
"outputId": "d23409d2-43e3-4a39-8a2f-a6d9a4d7b8c1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Logpearson3\n",
"Jumlah Kelas = 4\n",
"Dk = 1\n",
"X^2_hitungan = 2.121\n",
"X^2_kritis = 3.841\n",
"Result (X2_calc < X2_cr) = Distribusi Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" batas_kelas fe ft\n",
"no \n",
"1 X <= 40.0493 4 2.5\n",
"2 40.0493 < X <= 65.2875 1 2.5\n",
"3 65.2875 < X <= 114.8905 2 2.5\n",
"4 X > 114.8905 3 2.5"
],
"text/html": [
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>batas_kelas</th>\n",
" <th>fe</th>\n",
" <th>ft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>no</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>X &lt;= 40.0493</td>\n",
" <td>4</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40.0493 &lt; X &lt;= 65.2875</td>\n",
" <td>1</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>65.2875 &lt; X &lt;= 114.8905</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>X &gt; 114.8905</td>\n",
" <td>3</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 67
}
]
},
{
"cell_type": "markdown",
"source": [
"### GUMBEL"
],
"metadata": {
"id": "dRBtyyALGNvf"
}
},
{
"cell_type": "code",
"source": [
"ujidist.uji_chisquare(dataset_r24, dist='gumbel')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 310
},
"id": "5cDZeI35GKYh",
"outputId": "9c3ca4f7-fe17-4096-f2db-b3ea5c2bf452"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Periksa Kecocokan Distribusi Gumbel\n",
"Jumlah Kelas = 4\n",
"Dk = 1\n",
"X^2_hitungan = 5.162\n",
"X^2_kritis = 3.841\n",
"Result (X2_calc < X2_cr) = Distribusi Tidak Diterima\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" batas_kelas fe ft\n",
"no \n",
"1 X <= 20.5982 0 2.5\n",
"2 20.5982 < X <= 84.5713 6 2.5\n",
"3 84.5713 < X <= 165.7332 2 2.5\n",
"4 X > 165.7332 2 2.5"
],
"text/html": [
"<div>\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>batas_kelas</th>\n",
" <th>fe</th>\n",
" <th>ft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>no</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>X &lt;= 20.5982</td>\n",
" <td>0</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20.5982 &lt; X &lt;= 84.5713</td>\n",
" <td>6</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84.5713 &lt; X &lt;= 165.7332</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>X &gt; 165.7332</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 68
}
]
},
{
"cell_type": "markdown",
"source": [
"# REKAP"
],
"metadata": {
"id": "2J3ICd4SGWbR"
}
},
{
"cell_type": "code",
"source": [
"table_actual = dataset_r24.copy()\n",
"table_actual = table_actual.sort_values('hujan')\n",
"table_actual['no'] = table_actual.hujan.rank()\n",
"table_actual['prob'] = table_actual['no'] / (len(table_actual) + 1)\n",
"table_actual['period'] = 1 / (1 - table_actual.prob)\n",
"\n",
"table_actual"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "kMViaCpwKESo",
"outputId": "f5507575-22c3-4c92-a39c-31c78eb01aab"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" hujan no prob period\n",
"2012-01-01 28.750000 1.0 0.090909 1.100000\n",
"2016-01-01 34.675000 2.0 0.181818 1.222222\n",
"2015-01-01 36.100000 3.0 0.272727 1.375000\n",
"2014-01-01 37.050000 4.0 0.363636 1.571429\n",
"2018-01-01 48.750000 5.0 0.454545 1.833333\n",
"2017-01-01 81.425000 6.0 0.545455 2.200000\n",
"2013-01-01 92.400000 7.0 0.636364 2.750000\n",
"2009-01-01 125.000000 8.0 0.727273 3.666667\n",
"2011-01-01 173.333333 9.0 0.818182 5.500000\n",
"2010-01-01 307.000000 10.0 0.909091 11.000000"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hujan</th>\n",
" <th>no</th>\n",
" <th>prob</th>\n",
" <th>period</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>28.750000</td>\n",
" <td>1.0</td>\n",
" <td>0.090909</td>\n",
" <td>1.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>34.675000</td>\n",
" <td>2.0</td>\n",
" <td>0.181818</td>\n",
" <td>1.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>36.100000</td>\n",
" <td>3.0</td>\n",
" <td>0.272727</td>\n",
" <td>1.375000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>37.050000</td>\n",
" <td>4.0</td>\n",
" <td>0.363636</td>\n",
" <td>1.571429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>48.750000</td>\n",
" <td>5.0</td>\n",
" <td>0.454545</td>\n",
" <td>1.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>81.425000</td>\n",
" <td>6.0</td>\n",
" <td>0.545455</td>\n",
" <td>2.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>92.400000</td>\n",
" <td>7.0</td>\n",
" <td>0.636364</td>\n",
" <td>2.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>125.000000</td>\n",
" <td>8.0</td>\n",
" <td>0.727273</td>\n",
" <td>3.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>173.333333</td>\n",
" <td>9.0</td>\n",
" <td>0.818182</td>\n",
" <td>5.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>307.000000</td>\n",
" <td>10.0</td>\n",
" <td>0.909091</td>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"source": [
"method_anfrek = 'normal lognormal logpearson3 gumbel'.split()\n",
"\n",
"table_anfrek = []\n",
"return_period = table_actual.period.to_numpy()\n",
"\n",
"for _method in method_anfrek:\n",
" table_anfrek.append(\n",
" getattr(anfrek, f'freq_{_method}')(\n",
" dataset_r24, return_period=return_period\n",
" )\n",
" )\n",
"\n",
"table_actual_anfrek = table_actual.reset_index().set_index('period')[['hujan']]\n",
"table_anfrek.insert(0, table_actual_anfrek)\n",
"\n",
"table_anfrek = pd.concat(table_anfrek, axis=1)\n",
"table_anfrek.plot(figsize=(15, 10));"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 587
},
"id": "Lkdwf6orGQvo",
"outputId": "da4d3e13-7094-4959-e79c-37ef3429de80"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# jika dibandingkan dengan nilai probabilitas\n",
"\n",
"method_anfrek = 'normal lognormal logpearson3 gumbel'.split()\n",
"\n",
"table_prob = []\n",
"\n",
"from IPython.utils import io # ref: https://stackoverflow.com/a/52559560\n",
"\n",
"for _method in method_anfrek:\n",
" # surpress print (hidrokit/hidrokit#125)\n",
" with io.capture_output() as captured:\n",
" _table_test = ujidist.uji_kstest(\n",
" dataset_r24, dist=_method\n",
" )\n",
" _table_method = _table_test[['p_d']]\n",
" _table_method.columns = [f'Prob_{_method.title()}']\n",
" table_prob.append(_table_method)\n",
"_table_data = _table_test['no x p_w'.split()]\n",
"_table_data.columns = 'No X Prob_Weibull'.split()\n",
"table_prob.insert(0, _table_data)\n",
"\n",
"table_prob = pd.concat(table_prob, axis=1)\n",
"table_prob.index.name = 'Date'\n",
"table_prob = table_prob.reset_index().set_index('No')\n",
"table_prob"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "382mAM6IGtJL",
"outputId": "b4febf39-8358-42ab-da66-0591102b82be"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date X Prob_Weibull Prob_Normal Prob_Lognormal \\\n",
"No \n",
"1 2012-01-01 28.750000 0.090909 0.219951 0.128027 \n",
"2 2016-01-01 34.675000 0.181818 0.240479 0.183970 \n",
"3 2015-01-01 36.100000 0.272727 0.245568 0.197735 \n",
"4 2014-01-01 37.050000 0.363636 0.248992 0.206935 \n",
"5 2018-01-01 48.750000 0.454545 0.293158 0.318358 \n",
"6 2017-01-01 81.425000 0.545455 0.431955 0.568367 \n",
"7 2013-01-01 92.400000 0.636364 0.481581 0.629710 \n",
"8 2009-01-01 125.000000 0.727273 0.627690 0.761383 \n",
"9 2011-01-01 173.333333 0.818182 0.809802 0.868959 \n",
"10 2010-01-01 307.000000 0.909091 0.991850 0.967093 \n",
"\n",
" Prob_Logpearson3 Prob_Gumbel \n",
"No \n",
"1 0.114777 0.281 \n",
"2 0.185319 0.304 \n",
"3 0.202598 0.310 \n",
"4 0.214091 0.314 \n",
"5 0.348026 0.360 \n",
"6 0.608311 0.488 \n",
"7 0.664750 0.529 \n",
"8 0.778668 0.639 \n",
"9 0.867339 0.767 \n",
"10 0.952599 0.940 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>X</th>\n",
" <th>Prob_Weibull</th>\n",
" <th>Prob_Normal</th>\n",
" <th>Prob_Lognormal</th>\n",
" <th>Prob_Logpearson3</th>\n",
" <th>Prob_Gumbel</th>\n",
" </tr>\n",
" <tr>\n",
" <th>No</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-01-01</td>\n",
" <td>28.750000</td>\n",
" <td>0.090909</td>\n",
" <td>0.219951</td>\n",
" <td>0.128027</td>\n",
" <td>0.114777</td>\n",
" <td>0.281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016-01-01</td>\n",
" <td>34.675000</td>\n",
" <td>0.181818</td>\n",
" <td>0.240479</td>\n",
" <td>0.183970</td>\n",
" <td>0.185319</td>\n",
" <td>0.304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-01-01</td>\n",
" <td>36.100000</td>\n",
" <td>0.272727</td>\n",
" <td>0.245568</td>\n",
" <td>0.197735</td>\n",
" <td>0.202598</td>\n",
" <td>0.310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-01-01</td>\n",
" <td>37.050000</td>\n",
" <td>0.363636</td>\n",
" <td>0.248992</td>\n",
" <td>0.206935</td>\n",
" <td>0.214091</td>\n",
" <td>0.314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2018-01-01</td>\n",
" <td>48.750000</td>\n",
" <td>0.454545</td>\n",
" <td>0.293158</td>\n",
" <td>0.318358</td>\n",
" <td>0.348026</td>\n",
" <td>0.360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-01-01</td>\n",
" <td>81.425000</td>\n",
" <td>0.545455</td>\n",
" <td>0.431955</td>\n",
" <td>0.568367</td>\n",
" <td>0.608311</td>\n",
" <td>0.488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2013-01-01</td>\n",
" <td>92.400000</td>\n",
" <td>0.636364</td>\n",
" <td>0.481581</td>\n",
" <td>0.629710</td>\n",
" <td>0.664750</td>\n",
" <td>0.529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2009-01-01</td>\n",
" <td>125.000000</td>\n",
" <td>0.727273</td>\n",
" <td>0.627690</td>\n",
" <td>0.761383</td>\n",
" <td>0.778668</td>\n",
" <td>0.639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2011-01-01</td>\n",
" <td>173.333333</td>\n",
" <td>0.818182</td>\n",
" <td>0.809802</td>\n",
" <td>0.868959</td>\n",
" <td>0.867339</td>\n",
" <td>0.767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2010-01-01</td>\n",
" <td>307.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.991850</td>\n",
" <td>0.967093</td>\n",
" <td>0.952599</td>\n",
" <td>0.940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"source": [
"# menghitung delta\n",
"\n",
"prob_source = 'normal lognormal logpearson3 gumbel weibull'.split()\n",
"\n",
"for _prob in prob_source[:-1]:\n",
" table_prob[f'delta_{_prob}'] = table_prob['Prob_Weibull'] - table_prob[f'Prob_{_prob.title()}']\n",
"\n",
"table_prob"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "fw7SwfmfVkHE",
"outputId": "b66d6887-18f0-4dfb-bf6d-535239980e25"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date X Prob_Weibull Prob_Normal Prob_Lognormal \\\n",
"No \n",
"1 2012-01-01 28.750000 0.090909 0.219951 0.128027 \n",
"2 2016-01-01 34.675000 0.181818 0.240479 0.183970 \n",
"3 2015-01-01 36.100000 0.272727 0.245568 0.197735 \n",
"4 2014-01-01 37.050000 0.363636 0.248992 0.206935 \n",
"5 2018-01-01 48.750000 0.454545 0.293158 0.318358 \n",
"6 2017-01-01 81.425000 0.545455 0.431955 0.568367 \n",
"7 2013-01-01 92.400000 0.636364 0.481581 0.629710 \n",
"8 2009-01-01 125.000000 0.727273 0.627690 0.761383 \n",
"9 2011-01-01 173.333333 0.818182 0.809802 0.868959 \n",
"10 2010-01-01 307.000000 0.909091 0.991850 0.967093 \n",
"\n",
" Prob_Logpearson3 Prob_Gumbel delta_normal delta_lognormal \\\n",
"No \n",
"1 0.114777 0.281 -0.129042 -0.037118 \n",
"2 0.185319 0.304 -0.058661 -0.002152 \n",
"3 0.202598 0.310 0.027160 0.074992 \n",
"4 0.214091 0.314 0.114645 0.156701 \n",
"5 0.348026 0.360 0.161388 0.136187 \n",
"6 0.608311 0.488 0.113499 -0.022913 \n",
"7 0.664750 0.529 0.154783 0.006653 \n",
"8 0.778668 0.639 0.099583 -0.034110 \n",
"9 0.867339 0.767 0.008379 -0.050777 \n",
"10 0.952599 0.940 -0.082760 -0.058002 \n",
"\n",
" delta_logpearson3 delta_gumbel \n",
"No \n",
"1 -0.023868 -0.190091 \n",
"2 -0.003501 -0.122182 \n",
"3 0.070129 -0.037273 \n",
"4 0.149546 0.049636 \n",
"5 0.106519 0.094545 \n",
"6 -0.062856 0.057455 \n",
"7 -0.028386 0.107364 \n",
"8 -0.051395 0.088273 \n",
"9 -0.049157 0.051182 \n",
"10 -0.043508 -0.030909 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>X</th>\n",
" <th>Prob_Weibull</th>\n",
" <th>Prob_Normal</th>\n",
" <th>Prob_Lognormal</th>\n",
" <th>Prob_Logpearson3</th>\n",
" <th>Prob_Gumbel</th>\n",
" <th>delta_normal</th>\n",
" <th>delta_lognormal</th>\n",
" <th>delta_logpearson3</th>\n",
" <th>delta_gumbel</th>\n",
" </tr>\n",
" <tr>\n",
" <th>No</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-01-01</td>\n",
" <td>28.750000</td>\n",
" <td>0.090909</td>\n",
" <td>0.219951</td>\n",
" <td>0.128027</td>\n",
" <td>0.114777</td>\n",
" <td>0.281</td>\n",
" <td>-0.129042</td>\n",
" <td>-0.037118</td>\n",
" <td>-0.023868</td>\n",
" <td>-0.190091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016-01-01</td>\n",
" <td>34.675000</td>\n",
" <td>0.181818</td>\n",
" <td>0.240479</td>\n",
" <td>0.183970</td>\n",
" <td>0.185319</td>\n",
" <td>0.304</td>\n",
" <td>-0.058661</td>\n",
" <td>-0.002152</td>\n",
" <td>-0.003501</td>\n",
" <td>-0.122182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-01-01</td>\n",
" <td>36.100000</td>\n",
" <td>0.272727</td>\n",
" <td>0.245568</td>\n",
" <td>0.197735</td>\n",
" <td>0.202598</td>\n",
" <td>0.310</td>\n",
" <td>0.027160</td>\n",
" <td>0.074992</td>\n",
" <td>0.070129</td>\n",
" <td>-0.037273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-01-01</td>\n",
" <td>37.050000</td>\n",
" <td>0.363636</td>\n",
" <td>0.248992</td>\n",
" <td>0.206935</td>\n",
" <td>0.214091</td>\n",
" <td>0.314</td>\n",
" <td>0.114645</td>\n",
" <td>0.156701</td>\n",
" <td>0.149546</td>\n",
" <td>0.049636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2018-01-01</td>\n",
" <td>48.750000</td>\n",
" <td>0.454545</td>\n",
" <td>0.293158</td>\n",
" <td>0.318358</td>\n",
" <td>0.348026</td>\n",
" <td>0.360</td>\n",
" <td>0.161388</td>\n",
" <td>0.136187</td>\n",
" <td>0.106519</td>\n",
" <td>0.094545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-01-01</td>\n",
" <td>81.425000</td>\n",
" <td>0.545455</td>\n",
" <td>0.431955</td>\n",
" <td>0.568367</td>\n",
" <td>0.608311</td>\n",
" <td>0.488</td>\n",
" <td>0.113499</td>\n",
" <td>-0.022913</td>\n",
" <td>-0.062856</td>\n",
" <td>0.057455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2013-01-01</td>\n",
" <td>92.400000</td>\n",
" <td>0.636364</td>\n",
" <td>0.481581</td>\n",
" <td>0.629710</td>\n",
" <td>0.664750</td>\n",
" <td>0.529</td>\n",
" <td>0.154783</td>\n",
" <td>0.006653</td>\n",
" <td>-0.028386</td>\n",
" <td>0.107364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2009-01-01</td>\n",
" <td>125.000000</td>\n",
" <td>0.727273</td>\n",
" <td>0.627690</td>\n",
" <td>0.761383</td>\n",
" <td>0.778668</td>\n",
" <td>0.639</td>\n",
" <td>0.099583</td>\n",
" <td>-0.034110</td>\n",
" <td>-0.051395</td>\n",
" <td>0.088273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2011-01-01</td>\n",
" <td>173.333333</td>\n",
" <td>0.818182</td>\n",
" <td>0.809802</td>\n",
" <td>0.868959</td>\n",
" <td>0.867339</td>\n",
" <td>0.767</td>\n",
" <td>0.008379</td>\n",
" <td>-0.050777</td>\n",
" <td>-0.049157</td>\n",
" <td>0.051182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2010-01-01</td>\n",
" <td>307.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.991850</td>\n",
" <td>0.967093</td>\n",
" <td>0.952599</td>\n",
" <td>0.940</td>\n",
" <td>-0.082760</td>\n",
" <td>-0.058002</td>\n",
" <td>-0.043508</td>\n",
" <td>-0.030909</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 72
}
]
},
{
"cell_type": "code",
"source": [
"# plot bar selisih terhadap weibull\n",
"\n",
"col_delta_prob = [f'delta_{_prob}' for _prob in prob_source[:-1]]\n",
"table_prob[col_delta_prob].plot.bar(figsize=(15, 10));"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 602
},
"id": "2Q4hC450XmFU",
"outputId": "e4f9dc6d-aaf4-4a2c-b972-a063b9faa9bd"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# menggunakan absolut untuk nilai delta\n",
"table_prob[col_delta_prob] = table_prob[col_delta_prob].abs()\n",
"table_prob"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "Gm7XQ-bwpebW",
"outputId": "a2541d02-b4c9-4879-eae3-8d267a047850"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date X Prob_Weibull Prob_Normal Prob_Lognormal \\\n",
"No \n",
"1 2012-01-01 28.750000 0.090909 0.219951 0.128027 \n",
"2 2016-01-01 34.675000 0.181818 0.240479 0.183970 \n",
"3 2015-01-01 36.100000 0.272727 0.245568 0.197735 \n",
"4 2014-01-01 37.050000 0.363636 0.248992 0.206935 \n",
"5 2018-01-01 48.750000 0.454545 0.293158 0.318358 \n",
"6 2017-01-01 81.425000 0.545455 0.431955 0.568367 \n",
"7 2013-01-01 92.400000 0.636364 0.481581 0.629710 \n",
"8 2009-01-01 125.000000 0.727273 0.627690 0.761383 \n",
"9 2011-01-01 173.333333 0.818182 0.809802 0.868959 \n",
"10 2010-01-01 307.000000 0.909091 0.991850 0.967093 \n",
"\n",
" Prob_Logpearson3 Prob_Gumbel delta_normal delta_lognormal \\\n",
"No \n",
"1 0.114777 0.281 0.129042 0.037118 \n",
"2 0.185319 0.304 0.058661 0.002152 \n",
"3 0.202598 0.310 0.027160 0.074992 \n",
"4 0.214091 0.314 0.114645 0.156701 \n",
"5 0.348026 0.360 0.161388 0.136187 \n",
"6 0.608311 0.488 0.113499 0.022913 \n",
"7 0.664750 0.529 0.154783 0.006653 \n",
"8 0.778668 0.639 0.099583 0.034110 \n",
"9 0.867339 0.767 0.008379 0.050777 \n",
"10 0.952599 0.940 0.082760 0.058002 \n",
"\n",
" delta_logpearson3 delta_gumbel \n",
"No \n",
"1 0.023868 0.190091 \n",
"2 0.003501 0.122182 \n",
"3 0.070129 0.037273 \n",
"4 0.149546 0.049636 \n",
"5 0.106519 0.094545 \n",
"6 0.062856 0.057455 \n",
"7 0.028386 0.107364 \n",
"8 0.051395 0.088273 \n",
"9 0.049157 0.051182 \n",
"10 0.043508 0.030909 "
],
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" <th></th>\n",
" <th>Date</th>\n",
" <th>X</th>\n",
" <th>Prob_Weibull</th>\n",
" <th>Prob_Normal</th>\n",
" <th>Prob_Lognormal</th>\n",
" <th>Prob_Logpearson3</th>\n",
" <th>Prob_Gumbel</th>\n",
" <th>delta_normal</th>\n",
" <th>delta_lognormal</th>\n",
" <th>delta_logpearson3</th>\n",
" <th>delta_gumbel</th>\n",
" </tr>\n",
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" <th>No</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-01-01</td>\n",
" <td>28.750000</td>\n",
" <td>0.090909</td>\n",
" <td>0.219951</td>\n",
" <td>0.128027</td>\n",
" <td>0.114777</td>\n",
" <td>0.281</td>\n",
" <td>0.129042</td>\n",
" <td>0.037118</td>\n",
" <td>0.023868</td>\n",
" <td>0.190091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016-01-01</td>\n",
" <td>34.675000</td>\n",
" <td>0.181818</td>\n",
" <td>0.240479</td>\n",
" <td>0.183970</td>\n",
" <td>0.185319</td>\n",
" <td>0.304</td>\n",
" <td>0.058661</td>\n",
" <td>0.002152</td>\n",
" <td>0.003501</td>\n",
" <td>0.122182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-01-01</td>\n",
" <td>36.100000</td>\n",
" <td>0.272727</td>\n",
" <td>0.245568</td>\n",
" <td>0.197735</td>\n",
" <td>0.202598</td>\n",
" <td>0.310</td>\n",
" <td>0.027160</td>\n",
" <td>0.074992</td>\n",
" <td>0.070129</td>\n",
" <td>0.037273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-01-01</td>\n",
" <td>37.050000</td>\n",
" <td>0.363636</td>\n",
" <td>0.248992</td>\n",
" <td>0.206935</td>\n",
" <td>0.214091</td>\n",
" <td>0.314</td>\n",
" <td>0.114645</td>\n",
" <td>0.156701</td>\n",
" <td>0.149546</td>\n",
" <td>0.049636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2018-01-01</td>\n",
" <td>48.750000</td>\n",
" <td>0.454545</td>\n",
" <td>0.293158</td>\n",
" <td>0.318358</td>\n",
" <td>0.348026</td>\n",
" <td>0.360</td>\n",
" <td>0.161388</td>\n",
" <td>0.136187</td>\n",
" <td>0.106519</td>\n",
" <td>0.094545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-01-01</td>\n",
" <td>81.425000</td>\n",
" <td>0.545455</td>\n",
" <td>0.431955</td>\n",
" <td>0.568367</td>\n",
" <td>0.608311</td>\n",
" <td>0.488</td>\n",
" <td>0.113499</td>\n",
" <td>0.022913</td>\n",
" <td>0.062856</td>\n",
" <td>0.057455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2013-01-01</td>\n",
" <td>92.400000</td>\n",
" <td>0.636364</td>\n",
" <td>0.481581</td>\n",
" <td>0.629710</td>\n",
" <td>0.664750</td>\n",
" <td>0.529</td>\n",
" <td>0.154783</td>\n",
" <td>0.006653</td>\n",
" <td>0.028386</td>\n",
" <td>0.107364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2009-01-01</td>\n",
" <td>125.000000</td>\n",
" <td>0.727273</td>\n",
" <td>0.627690</td>\n",
" <td>0.761383</td>\n",
" <td>0.778668</td>\n",
" <td>0.639</td>\n",
" <td>0.099583</td>\n",
" <td>0.034110</td>\n",
" <td>0.051395</td>\n",
" <td>0.088273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2011-01-01</td>\n",
" <td>173.333333</td>\n",
" <td>0.818182</td>\n",
" <td>0.809802</td>\n",
" <td>0.868959</td>\n",
" <td>0.867339</td>\n",
" <td>0.767</td>\n",
" <td>0.008379</td>\n",
" <td>0.050777</td>\n",
" <td>0.049157</td>\n",
" <td>0.051182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2010-01-01</td>\n",
" <td>307.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.991850</td>\n",
" <td>0.967093</td>\n",
" <td>0.952599</td>\n",
" <td>0.940</td>\n",
" <td>0.082760</td>\n",
" <td>0.058002</td>\n",
" <td>0.043508</td>\n",
" <td>0.030909</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 74
}
]
},
{
"cell_type": "code",
"source": [
"# menjumlah selisih\n",
"\n",
"table_prob_sum = table_prob[col_delta_prob].sum().to_frame(name='SUM')\n",
"table_prob_sum"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "3xSapBMRXzaG",
"outputId": "51be4825-0e58-4dd1-b7a5-8b190cc020c0"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" SUM\n",
"delta_normal 0.949899\n",
"delta_lognormal 0.579606\n",
"delta_logpearson3 0.588865\n",
"delta_gumbel 0.828909"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SUM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>delta_normal</th>\n",
" <td>0.949899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>delta_lognormal</th>\n",
" <td>0.579606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>delta_logpearson3</th>\n",
" <td>0.588865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>delta_gumbel</th>\n",
" <td>0.828909</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 75
}
]
},
{
"cell_type": "code",
"source": [
"# nilai terkecil\n",
"\n",
"table_prob_sum[table_prob_sum.SUM == table_prob_sum.SUM.min()]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "vzBgI-OKY7dI",
"outputId": "58581bd6-a0d6-44cb-affd-8194ed50add2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" SUM\n",
"delta_lognormal 0.579606"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SUM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>delta_lognormal</th>\n",
" <td>0.579606</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 76
}
]
},
{
"cell_type": "markdown",
"source": [
"# Changelog\n",
"\n",
"```\n",
"- 20220405 - 1.0.0 - Initial\n",
"```\n",
"\n",
"#### Copyright &copy; 2022 [Taruma Sakti Megariansyah](https://taruma.github.io)\n",
"\n",
"Source code in this notebook is licensed under a [MIT License](https://choosealicense.com/licenses/mit/). Data in this notebook is licensed under a [Creative Common Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). \n"
],
"metadata": {
"id": "5YlzR9BLl1Tk"
}
}
]
}
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