!apt install jq
!npm i -g npm to update
!npm install https://info.gbiz.go.jp/tools/imi_tools/resource/imi-enrichment-address/imi-enrichment-address-2.0.0.tgz

%%writefile index.js

const enrichment = require("imi-enrichment-address")

enrichment('東京都千代田区霞が関1-3-1').then(json => {
    console.log(JSON.stringify(json));
});

!node index.js | jq

こちらのESモジュール版だとインストール不要で簡単

%%html
<script type="module">

import IMIEnrichmentAddress from "https://code4sabae.github.io/imi-enrichment-address/IMIEnrichmentAddress.mjs";

const main = async () => {
  const json = await IMIEnrichmentAddress("福井県鯖江市新横江2-3-4");
  alert(JSON.stringify(json, null, 2));
};
main();

</script>

%%html
<script type="module">

import IMIEnrichmentHojin from "https://code4sabae.github.io/imi-enrichment-hojin/IMIEnrichmentHojin.mjs";

const main = async () => {
  const json = await IMIEnrichmentHojin("4000012090001");
  alert(JSON.stringify(json, null, 2));
};
main();

</script>

2020-05-30

PythonでIMIコンポーネントツールの住所変換コンポーネントで変換

info.gbiz.go.jp

インストール

imabari.hateblo.jp

こちらの住所を変換

www.oideya.gr.jp

import json

import pandas as pd
import requests

url = "http://localhost:8080"

headers = {"Content-Type": "application/json"}

base = {"@type": "場所型", "住所": {"@type": "住所型", "表記": ""}}

result = []

with open("address.txt", encoding="utf8") as f:

    for line in f.readlines():

        print(line)

        base["住所"]["表記"] = line.strip()

        json_data = json.dumps(base)

        r = requests.post(url, json_data, headers=headers)

        data = r.json()

        result.append(data)

df = pd.json_normalize(result, sep="_")

df.to_csv("data.csv")

気になるところ

町名に町がないと「町名が見つからない」
甲乙丙があると丁目・番地がない
町名が同じ丁目と番地があると番地に変換される
- 喜田村・桜井・波止浜・東村
番地の後に漢数字があると番地に結合される（62）

2020-05-29

IMIコンポーネントツール

info.gbiz.go.jp

blog.geolonia.com

github.com

sudo apt install nodejs npm

mkdir imi-enrichment-address
cd imi-enrichment-address

npm install https://info.gbiz.go.jp/tools/imi_tools/resource/imi-enrichment-address/imi-enrichment-address-2.0.0.tgz

node node_modules/imi-enrichment-address/bin/server.js 8080

import json
from pathlib import Path

import requests

url = "http://localhost:8080"

headers = {"Content-Type": "application/json"}

json_data = json.dumps({
    "@type": "場所型",
    "住所": {
        "@type": "住所型",
        "表記": "今治市別宮町１丁目４−１"
    }
})

r = requests.post(url, json_data, headers=headers)

data = r.json()

print(data)

print(Path(data["住所"]["都道府県コード"]).name)
print(Path(data["住所"]["市区町村コード"]).name)

print(data["地理座標"]["緯度"])
print(data["地理座標"]["経度"])

2020-05-24

富山県ステータス

import re
import datetime
import pathlib

import pandas as pd
import requests
from bs4 import BeautifulSoup

import jaconv


def zen2han(s):
    result = float(jaconv.z2h(s.rstrip("人"), digit=True, ascii=True))
    return result


def str2date(s):
    y = dt_now.year
    m, d = map(int, re.findall("[0-9]{1,2}", s))

    return pd.Timestamp(y, m, d)


JST = datetime.timezone(datetime.timedelta(hours=+9), "JST")
dt_now = datetime.datetime.now(JST)

url = "http://www.pref.toyama.jp/cms_sec/1205/kj00022038.html"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko",
}

r = requests.get(url, headers=headers)

r.raise_for_status()

soup = BeautifulSoup(r.content, "html.parser")

link = soup.find("div", id="file").find("a", text="強化・緩和の判断指標（直近１週間平均）の推移").get("href")

link

df = pd.read_excel(link, index_col=[0, 1, 2], skiprows=2, skipfooter=3).T

df.index = df.index.map(str2date)
df.index.name = "日付"
df.columns = ["入院者数", "重症病床稼働率", "新規陽性者数", "感染経路不明の患者数", "陽性率", "達成状況"]

df["入院者数"] = df["入院者数"].apply(zen2han)
df["新規陽性者数"] = df["新規陽性者数"].apply(zen2han)
df["感染経路不明の患者数"] = df["感染経路不明の患者数"].apply(zen2han)

df["重症病床稼働率"] = df["重症病床稼働率"].apply(lambda x: round(x * 100, 1))
df["陽性率"] = df["陽性率"].apply(lambda x: round(x * 100, 1))

# df.to_csv("toyama_status.csv", encoding="utf_8_sig")

df_ori = pd.read_csv("toyama_status.csv", index_col="日付", parse_dates=True)

df_csv = df_ori.reindex(df_ori.index.union(df.index))

df_csv.update(df)

df_csv.sort_index(inplace=True)

df_csv.to_csv("toyama_status.csv", encoding="utf_8_sig")

import json

df_csv["日付"] = df_csv.index.strftime("%Y-%m-%d")

data = {
    "statusItems": df_csv.to_dict(orient="records"),
}

p = pathlib.Path("status.json")
p.parent.mkdir(parents=True, exist_ok=True)

with p.open(mode="w", encoding="utf-8") as fw:
    json.dump(data, fw, ensure_ascii=False, indent=4)

df_csv

2020-05-22

Github dispatch

import json
import requests

url = "https://api.github.com/repos/imabari/kumamoto-covid19/dispatches"

headers = {"Authorization": "token xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", "Content-Type": "application/json"}

json_data = json.dumps({"event_type": "on-demand-test"})

requests.post(url, json_data, headers=headers)

# This workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions

name: Python application

on:
  repository_dispatch:
    types: [on-demand-test]
  schedule:
    - cron:  '0 11 * * *'

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python 3.8
      uses: actions/setup-python@v1
      with:
        python-version: 3.8
    - name: Cache pip
      uses: actions/cache@v1
      with:
        path: ~/.cache/pip
        key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
        restore-keys: |
          ${{ runner.os }}-pip-
          ${{ runner.os }}-
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run script
      run: |
        python main.py
    - name: deploy
      uses: peaceiris/actions-gh-pages@v3
      with:
        github_token: ${{ secrets.GITHUB_TOKEN }}
        publish_dir: ./data
        publish_branch: gh-pages