{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b8d4789-78b3-4f31-9207-4ba6fcf90ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = \"data/data-3d9Ex.csv\"\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "colorscales = px.colors.named_colorscales()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ef312bf-3c18-4417-806b-3d07830d96d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hungary counties shapefile\n",
    "url = \"https://maps.princeton.edu/download/file/stanford-dt251rh6351-shapefile.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6822dbd-d897-445f-9743-f7267d66537c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!cd data && wget https://maps.princeton.edu/download/file/stanford-dt251rh6351-shapefile.zip  && unzip stanford-dt251rh6351-shapefile.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb150ab8-aa94-4b76-849f-554f32a30fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read shp data into geopandas\n",
    "gg = gpd.read_file(\"data/dt251rh6351.shp\")\n",
    "ggjson = gg.to_json()\n",
    "gg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e6b826-8563-4c3e-bc81-6141bf6d08f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gg.iloc[1].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a29a6736-1cf5-485e-abcc-dd5970773d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will need gejson\n",
    "dggjson = json.loads(ggjson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bf4ed85-0f56-4ede-b75a-cbc7a8bdbd5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turns out one of the county's name is misspelled so we rename it for now in the dataframe\n",
    "[f['properties']['name_1'] for f in dggjson['features']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6b0eda-51f1-4bf9-b161-487db05e4ebc",
   "metadata": {},
   "source": [
    "## Heti adatok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770e6ba1-e48a-4fde-aa77-5b898e9853e5",
   "metadata": {},
   "source": [
    "### Letoltes\n",
    "https://atlo.team/koronaterkep/#megyeibovebb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5eeb9f-847c-4e73-8b73-9fc6a97ba56b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2/gis-files/hungary-shapefile\n",
    "url = \"https://docs.google.com/spreadsheets/d/1djH-yUHLPwuEExCjiXS__6-8W2Yp_msFvShpL4bBcuM/export?format=xlsx&gid=1283792994\"\n",
    "heti_df = pd.read_excel(url)         \n",
    "\n",
    "heti_df['date'] = pd.to_datetime(heti_df['Dátum']) #, format='%y-%m-%d')\n",
    "heti_df = heti_df.drop(columns=['Dátum', 'Összesen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e89ed3a-3afe-42ee-b923-cd7e659ae9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "heti_df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4290d58d-5dfc-46ee-afd7-c46148c62e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename Győr to Gyor\n",
    "heti_df = heti_df.rename(columns={'Győr-Moson-Sopron':'Gyor-Moson-Sopron'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33c27409-bebd-4cd6-a1a6-14935565e94b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "153128f5-3e5b-4176-aebf-5bf40ddb703f",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = heti_df.loc[0,'date']\n",
    "end_date = start_date + dt.timedelta(days=7)\n",
    "\n",
    "mask = (heti_df['date'] >= start_date) & (heti_df['date'] < end_date)\n",
    "\n",
    "hhdf = heti_df.loc[mask].drop(columns=['date']).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13aee1a-6702-44fb-8382-985ff582a012",
   "metadata": {},
   "outputs": [],
   "source": [
    "hhdf['sum'] = hhdf.sum(axis=1)\n",
    "hhdf = hhdf.reset_index().rename(columns={'index':'name_1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4fe7f4-796b-4b1c-bcdf-6b440b4ef3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "hhdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50af1bc-b8d4-4fee-9992-f9939071945b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the mean of the county's centroids\n",
    "cc = gg.centroid\n",
    "\n",
    "clon = cc.apply(lambda x: x.x).mean()\n",
    "clat = cc.apply(lambda x: x.y).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c319fee-1e1b-4b8a-bea0-690b5e3d67d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.choropleth_mapbox(hhdf.drop(columns=[0,1,2,3,4,5,6]), geojson=dggjson, locations='name_1', color='sum',\n",
    "                           color_continuous_scale=\"Viridis\",\n",
    "                           #locationmode='geojson-id',\n",
    "                    featureidkey='properties.name_1',\n",
    "                           range_color=(0, 1800),\n",
    "                           mapbox_style=\"carto-positron\",\n",
    "                           zoom=5.7, center = {\"lat\": clat, \"lon\": clon},\n",
    "                           opacity=0.5,\n",
    "                           labels={'sum':'Megfertozodesek szama'}\n",
    "                          )\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a757a20-9c59-4e31-b1db-bc187f1e3aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "thhdf = hhdf.drop(columns=[0,1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2294fa4-d611-40e7-a768-a39943b9020b",
   "metadata": {},
   "source": [
    "## Using the built-in plotly choropleth maps\n",
    "This is not very decorative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76163eb4-4587-4561-9dc2-02a321a4e27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.choropleth(thhdf, geojson=dggjson, locations='name_1',#, color='sum',\n",
    "                       color_continuous_scale=\"Viridis\",\n",
    "                           range_color=(0, 1800),\n",
    "                        locationmode='geojson-id',\n",
    "                    featureidkey='properties.name_1',\n",
    "                    color=thhdf['sum'],\n",
    "                           labels={'sum' : 'Megfertozodesek szama'}\n",
    "                          )\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\": 0,\"b\":0}, geo_scope='europe'\n",
    "                 )\n",
    "fig.layout.geo.center.lat = clat\n",
    "fig.layout.geo.center.lon = clon\n",
    "fig.layout.geo.projection.scale = 9\n",
    "fig.show()\n"
   ]
  }
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