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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "taruma_hk84_tabel_ringkasan_jamjaman.ipynb", | |
| "provenance": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/taruma/cad07f29ffc025ba9e7801e752be3444/taruma_hk84_tabel_ringkasan_jamjaman.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PijWaYlBOu6E" | |
| }, | |
| "source": [ | |
| "Berdasarkan isu [#84](https://github.com/taruma/hidrokit/issues/84): **request: buat ringkasan tabel jam-jaman dengan durasinya**\n", | |
| "\n", | |
| "Referensi isu:\n", | |
| "- `hidrokit.contrib.taruma.hk79` [#79](https://github.com/taruma/hidrokit/issues/79). \\([lihat notebook / manual](https://nbviewer.jupyter.org/gist/taruma/05dab67fac8313a94134ac02d0398897)\\). **request: ambil dataset hujan jam-jaman dari excel**\n", | |
| "- `hidrokit.contrib.taruma.hk73` [#73](https://github.com/taruma/hidrokit/issues/73). \\([lihat notebook / manual](https://nbviewer.jupyter.org/gist/taruma/b00880905f297013f046dad95dc2e284)\\). **request: mengolah berkas dari data bmkg**\n", | |
| "\n", | |
| "Deskripsi Permasalahan:\n", | |
| "- Setelah memperoleh dataset menggunakan `.hk79`, maka data jam-jaman akan diproses lebih lanjut lagi.\n", | |
| "- Dalam isu ini, akan dibuat tabel ringkasan mengenai hujan jam-jaman beserta durasinya.\n", | |
| "- Tabel ringkasan dapat disimpan dalam berbagai format (excel atau csv) dengan menggunakan `pandas`.\n", | |
| "\n", | |
| "Strategi Penyelesaian:\n", | |
| "- Ditentukan/diasumsikan bahwa observasi hanya dilakukan per-24 jam, maka durasi terlama dalam tabel sebesar 24 jam.\n", | |
| "- Mempersiapkan DF (`DataFrame`) yang akan digunakan dan menentukan kolom mana yang akan dibuat ringkasannya.\n", | |
| "- Ambil sub-DF dari DF setiap 24 jam, kemudian diambil informasi kejadian hujan jam-jaman.\n", | |
| " - Membuat `index_grouped` yang merupakan `list of list` index kejadian hujan yang berturut-turut. Fungsi ini telah dikembangkan pada modul `.hk73`.\n", | |
| " - Membuat tiga `list` yang merupakan `list` untuk tanggal observasi, `list` untuk jam observasi, dan `list` untuk nilai hujan observasi.\n", | |
| " - Mengubah tiga `list` tersebut ke dalam bentuk `dictionary`.\n", | |
| "- Ulangi tahap sebelumnya untuk setiap hari (24 jam). Dan menggabungkan seluruh `dictionary` dalam satu peubah.\n", | |
| "- Mengubah `dictionary` menjadi `pandas.DataFrame`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "mxXIWs-iRvPM" | |
| }, | |
| "source": [ | |
| "# PERSIAPAN DAN DATASET" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zIWmJH0_Ociy", | |
| "outputId": "c795dd86-2429-4a13-fea0-5a4afb194ba3", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "source": [ | |
| "try:\n", | |
| " import hidrokit\n", | |
| "except:\n", | |
| " !pip install git+https://github.com/hidrokit/hidrokit.git@243-refactor-hk84\n", | |
| "print(f'hidrokit version: {hidrokit.__version__}')" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "hidrokit version: 0.5.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "lxLA_9I_R2ej" | |
| }, | |
| "source": [ | |
| "!wget -O aurene_clean.csv \"https://taruma.github.io/assets/hidrokit_dataset/AURENE_STATION_clean.csv\" -q" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "uahcisYRSaFc" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "5IZgq0Lkgp0i", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "67446548-0998-4910-b4a5-92a508226266" | |
| }, | |
| "source": [ | |
| "# Load Dataset\n", | |
| "dataset = pd.read_csv('aurene_clean.csv', index_col=0, parse_dates=True)\n", | |
| "dataset.info()" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 26304 entries, 2000-01-01 00:00:00 to 2002-12-31 23:00:00\n", | |
| "Data columns (total 1 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 AURENE 2759 non-null float64\n", | |
| "dtypes: float64(1)\n", | |
| "memory usage: 411.0 KB\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "D8bCLVRfSdjx" | |
| }, | |
| "source": [ | |
| "# KODE" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AbFHYIzwSc0U" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "from hidrokit.contrib.taruma import hk73\n", | |
| "\n", | |
| "\n", | |
| "def _time_grouped(df, index_grouped, col, date_fmt=\"%Y-%m-%d\", hour_fmt=\"%H:%M\"):\n", | |
| " \"\"\"\n", | |
| " Return index_grouped as (list of date, list of hour)\n", | |
| "\n", | |
| " Parameters:\n", | |
| " - df: pandas DataFrame\n", | |
| " The DataFrame containing the data.\n", | |
| " - index_grouped: list of tuples\n", | |
| " The index groups to be processed.\n", | |
| " - col: int or str\n", | |
| " The column index or name to extract the date and hour values from.\n", | |
| " - date_fmt: str, optional\n", | |
| " The format string for the date values. Default is '%Y-%m-%d'.\n", | |
| " - hour_fmt: str, optional\n", | |
| " The format string for the hour values. Default is '%H:%M'.\n", | |
| "\n", | |
| " Returns:\n", | |
| " - date: list of lists\n", | |
| " The list of date values for each index group.\n", | |
| " - hour: list of lists\n", | |
| " The list of hour values for each index group.\n", | |
| " \"\"\"\n", | |
| " date = []\n", | |
| " hour = []\n", | |
| " for item in index_grouped:\n", | |
| " date_val = df.iloc[[item[0]], col].index.strftime(date_fmt).to_list()\n", | |
| " hour_val = df.iloc[[item[0]], col].index.strftime(hour_fmt).to_list()\n", | |
| " date.append(date_val)\n", | |
| " hour.append(hour_val)\n", | |
| " return date, hour\n", | |
| "\n", | |
| "\n", | |
| "def _value_grouped(df, index_grouped, col):\n", | |
| " \"\"\"Return index_grouped as a list of value lists.\n", | |
| "\n", | |
| " Args:\n", | |
| " df (pandas.DataFrame): The input DataFrame.\n", | |
| " index_grouped (list): The list of indices to group.\n", | |
| " col (int): The column index to extract values from.\n", | |
| "\n", | |
| " Returns:\n", | |
| " list: A list of value lists corresponding to the grouped indices.\n", | |
| " \"\"\"\n", | |
| " value = []\n", | |
| " for item in index_grouped:\n", | |
| " value_val = df.iloc[item, col].to_list()\n", | |
| " value.append(value_val)\n", | |
| " return value\n", | |
| "\n", | |
| "\n", | |
| "def _dict_grouped(date_list, hour_list, value_list, start=0):\n", | |
| " \"\"\"\n", | |
| " Join three lists and return as a dictionary.\n", | |
| "\n", | |
| " Args:\n", | |
| " date_list (list): List of dates.\n", | |
| " hour_list (list): List of hours.\n", | |
| " value_list (list): List of values.\n", | |
| " start (int, optional): Starting index for the dictionary keys. Defaults to 0.\n", | |
| "\n", | |
| " Returns:\n", | |
| " dict: Dictionary with keys as indices and values as concatenated date, hour, and value.\n", | |
| "\n", | |
| " \"\"\"\n", | |
| " item_list = enumerate(zip(date_list, hour_list, value_list), start=start)\n", | |
| " return {i: date + hour + value for i, (date, hour, value) in item_list}\n", | |
| "\n", | |
| "\n", | |
| "def summary_hourly(\n", | |
| " dataframe,\n", | |
| " column,\n", | |
| " n_hours=24,\n", | |
| " text_date=None,\n", | |
| " return_as_dataframe=True,\n", | |
| " date_format=\"%Y-%m-%d\",\n", | |
| " hour_format=\"%H:%M\",\n", | |
| "): # pylint: disable=too-many-arguments,too-many-locals\n", | |
| " \"\"\"\n", | |
| " Generate a summary of hourly data from a DataFrame.\n", | |
| "\n", | |
| " Args:\n", | |
| " df (pandas.DataFrame): The input DataFrame.\n", | |
| " column (str): The name of the column containing the hourly data.\n", | |
| " n_hours (int, optional): The number of hours to group together. Defaults to 24.\n", | |
| " text_date (list, optional):\n", | |
| " The list of column names to include in the summary. Defaults to ['date', 'hour'].\n", | |
| " as_df (bool, optional): Whether to return the summary as a DataFrame. Defaults to True.\n", | |
| " date_fmt (str, optional): The date format string. Defaults to '%Y-%m-%d'.\n", | |
| " hour_fmt (str, optional): The hour format string. Defaults to '%H:%M'.\n", | |
| "\n", | |
| " Returns:\n", | |
| " pandas.DataFrame or dict:\n", | |
| " The summary of hourly data. If `as_df` is True, a DataFrame is returned.\n", | |
| " Otherwise, a dictionary is returned.\n", | |
| " \"\"\"\n", | |
| " col = dataframe.columns.get_loc(column)\n", | |
| " nrows, _ = dataframe.shape\n", | |
| " results = {}\n", | |
| " text_date = [\"date\", \"hour\"] if text_date is None else text_date\n", | |
| "\n", | |
| " for i in range(0, nrows, n_hours):\n", | |
| " sub_df = dataframe.iloc[i : i + n_hours]\n", | |
| " ix_array = hk73.get_missing_data_indices(~sub_df.iloc[:, col].isna().values)\n", | |
| " ix_grouped = hk73.group_consecutive_elements(ix_array)\n", | |
| " date, hour = _time_grouped(\n", | |
| " sub_df, ix_grouped, col, date_fmt=date_format, hour_fmt=hour_format\n", | |
| " )\n", | |
| " value = _value_grouped(sub_df, ix_grouped, col)\n", | |
| " each_hours = _dict_grouped(date, hour, value, start=i)\n", | |
| " results.update(each_hours)\n", | |
| "\n", | |
| " if return_as_dataframe:\n", | |
| " columns_name = text_date + [i for i in range(1, n_hours + 1)]\n", | |
| " df_results = pd.DataFrame.from_dict(\n", | |
| " results, orient=\"index\", columns=columns_name\n", | |
| " )\n", | |
| " return df_results\n", | |
| " return results\n" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hUCLu72zUHE1" | |
| }, | |
| "source": [ | |
| "# PENERAPAN" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "g4nxkghGU7GM", | |
| "outputId": "73c4403e-af16-4754-8ce7-cc7be55efd67", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 236 | |
| } | |
| }, | |
| "source": [ | |
| "summary = summary_hourly(dataset, 'AURENE')\n", | |
| "summary.head()" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " date hour 1 2 3 4 5 6 7 8 ... 15 16 17 18 \\\n", | |
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| "49 2000-01-03 05:00 0.6 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n", | |
| "50 2000-01-03 09:00 2.2 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n", | |
| "\n", | |
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| "\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5376</th>\n", | |
| " <td>2000-08-12</td>\n", | |
| " <td>12:00</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15433</th>\n", | |
| " <td>2001-10-05</td>\n", | |
| " <td>20:00</td>\n", | |
| " <td>0.1</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16106</th>\n", | |
| " <td>2001-11-02</td>\n", | |
| " <td>14:00</td>\n", | |
| " <td>0.2</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11952</th>\n", | |
| " <td>2001-05-13</td>\n", | |
| " <td>07:00</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11234</th>\n", | |
| " <td>2001-04-13</td>\n", | |
| " <td>09:00</td>\n", | |
| " <td>0.2</td>\n", | |
| " <td>0.1</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18266</th>\n", | |
| " <td>2002-01-31</td>\n", | |
| " <td>13:00</td>\n", | |
| " <td>5.8</td>\n", | |
| " <td>0.2</td>\n", | |
| " <td>0.3</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50</th>\n", | |
| " <td>2000-01-03</td>\n", | |
| " <td>09:00</td>\n", | |
| " <td>2.2</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>15 rows × 26 columns</p>\n", | |
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| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
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| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vtYDmGTeW0cS", | |
| "outputId": "7aace334-992b-4433-cc64-93fe34f2613f", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 363 | |
| } | |
| }, | |
| "source": [ | |
| "summary2 = summary_hourly(dataset, 'AURENE', n_hours=10, text_date=['Tanggal', 'Jam'], date_format='%d %b %Y', hour_format='%I:%M %p')\n", | |
| "summary2.sample(n=10)" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Tanggal Jam 1 2 3 4 5 6 7 8 9 10\n", | |
| "15470 06 Oct 2001 05:00 PM 0.7 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", | |
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| "7350 02 Nov 2000 06:00 AM 2.2 0.2 NaN NaN NaN NaN NaN NaN NaN NaN\n", | |
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| "17120 14 Dec 2001 08:00 AM 0.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", | |
| "2710 23 Apr 2000 06:00 AM 4.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", | |
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| "15850 22 Oct 2001 12:00 PM 9.4 1.4 0.4 0.6 NaN NaN NaN NaN NaN NaN\n", | |
| "18140 26 Jan 2002 05:00 AM 0.2 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", | |
| "2880 30 Apr 2000 05:00 AM 40.0 17.0 0.5 NaN NaN NaN NaN NaN NaN NaN" | |
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| " <th>18700</th>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21200</th>\n", | |
| " <td>02 Jun 2002</td>\n", | |
| " <td>10:00 AM</td>\n", | |
| " <td>13.6</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17120</th>\n", | |
| " <td>14 Dec 2001</td>\n", | |
| " <td>08:00 AM</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2710</th>\n", | |
| " <td>23 Apr 2000</td>\n", | |
| " <td>06:00 AM</td>\n", | |
| " <td>4.5</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9790</th>\n", | |
| " <td>12 Feb 2001</td>\n", | |
| " <td>12:00 AM</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15850</th>\n", | |
| " <td>22 Oct 2001</td>\n", | |
| " <td>12:00 PM</td>\n", | |
| " <td>9.4</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>0.4</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18140</th>\n", | |
| " <td>26 Jan 2002</td>\n", | |
| " <td>05:00 AM</td>\n", | |
| " <td>0.2</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2880</th>\n", | |
| " <td>30 Apr 2000</td>\n", | |
| " <td>05:00 AM</td>\n", | |
| " <td>40.0</td>\n", | |
| " <td>17.0</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-002b57fd-a33b-48ee-b056-a8e9473fdb28')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-002b57fd-a33b-48ee-b056-a8e9473fdb28 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-002b57fd-a33b-48ee-b056-a8e9473fdb28');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-357768ff-1ca5-48ae-a420-9e744702d7a4\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-357768ff-1ca5-48ae-a420-9e744702d7a4')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-357768ff-1ca5-48ae-a420-9e744702d7a4 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "repr_error": "0" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "xtlMdmTlhShI", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ccffe276-fe40-4c7b-9286-c9546f8684a0" | |
| }, | |
| "source": [ | |
| "summary3 = summary_hourly(dataset, 'AURENE', n_hours=24, return_as_dataframe=False)\n", | |
| "print(type(summary3))\n", | |
| "print(len(summary3.keys()))\n", | |
| "print(list(summary3.keys())[:10])" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<class 'dict'>\n", | |
| "1093\n", | |
| "[0, 24, 48, 49, 50, 72, 96, 97, 120, 144]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "EhPfCQ6LVZYK" | |
| }, | |
| "source": [ | |
| "# Changelog\n", | |
| "\n", | |
| "```\n", | |
| "- 20240414 - 1.1.0 / 0.5.0 - Refactor hk84\n", | |
| "- 20191209 - 1.0.0 - Initial\n", | |
| "```\n", | |
| "\n", | |
| "#### Copyright © 2019-2024 [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" | |
| ] | |
| } | |
| ] | |
| } |
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