Daily_Forecast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import gzip\n",
    "import pandas as pd\n",
    "import shutil\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(pwd)/bq_sa.json\n"
     ]
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "import os\n",
    "\n",
    "print(os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftpstream = requests.get(\"https://smn.conagua.gob.mx/tools/GUI/webservices/?method=1\", headers={'User-Agent': 'Mozilla/5.0'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./file.gz', 'wb') as file:\n",
    "    file.write(ftpstream.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with gzip.open('./file.gz', 'rb') as f_in:\n",
    "    with open('response.json', 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json('./response.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['fecha', 'hora']] = df['dloc'].str.split('T', expand=True)\n",
    "df['fecha'] = pd.to_datetime(df['fecha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hora'] = df['hora'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df[(df['fecha'] == datetime.date.today().isoformat())].groupby(['ides', 'idmun', 'nes', 'nmun'])[['tmax','tmin', 'prec']].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# S3 integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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  "orig_nbformat": 4
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