EDINET②

import os
import time
import zipfile

import pandas as pd
import requests

API_ENDPOINT = "https://disclosure.edinet-fsa.go.jp/api/v2"  # v2を使用する


def save_csv(docID, type=5):
    """EDINETからデータを取得してフォルダに保存する

    Args:
        docID (str): DocID
    """
    assert type in [1, 2, 3, 4, 5], "typeの指定が間違っている"
    if type == 1:
        print(f"{docID}のXBRLデータを取得中")
    elif type == 2:
        print(f"{docID}のpdfデータを取得中")
    elif type in {3, 4}:
        print(f"{docID}のデータを取得中")
    elif type == 5:
        print(f"{docID}のcsvデータを取得中")
        time.sleep(5)

    r = requests.get(
        f"{API_ENDPOINT}/documents/{docID}",
        {
            "type": type,
            "Subscription-Key": os.environ.get("EDINET_API_KEY"),
        },
    )

    if r is None:
        print("データの取得に失敗しました。csvFlag==1かどうか確認してください。")
    else:
        os.makedirs(f"{docID}", exist_ok=True)
        temp_zip = "uuid_89FD71B5_CD7B_4833-B30D‗5AA5006097E2.zip"

        with open(temp_zip, "wb") as f:
            for chunk in r.iter_content(chunk_size=1024):
                f.write(chunk)

        with zipfile.ZipFile(temp_zip) as z:
            z.extractall(f"{docID}")

        os.remove(temp_zip)

docID_dict = {
    "商船三井": "S100STH6",
    "日本郵船": "S100SS7P",
    "玉井商船株式会社": "S100STLS",
    "川崎汽船": "S100SRTI",
    "飯野海運": "S100SP9O",
}

for docID in docID_dict.values():
    save_csv(docID, type=5)

dfs = []

for companyName, docID in docID_dict.items():
    csv_savedir = os.path.join(docID, "XBRL_TO_CSV")
    filelist = [f for f in os.listdir(csv_savedir) if f.startswith("jpcrp")]
    if len(filelist) > 0:
        df = pd.read_csv(
            os.path.join(csv_savedir, filelist[0]), encoding="utf-16", sep="\t"
        )
        df["会社名"] = [companyName for _ in range(df.shape[0])]
        dfs.append(df)

all_data = pd.concat(dfs)
all_data.head()

print(
    all_data.query(f"要素ID=='jpcrp_cor:NotesRegardingDividendTextBlock'")[["会社名", "値"]]
)

import japanize_matplotlib
import matplotlib.pyplot as plt
import seaborn as sns


def compare_company_IR(data, contextId, elementId, elementJpName):
    plot_data = data.query(f"要素ID=='{elementId}' and コンテキストID=='{contextId}'").copy()
    plot_data[elementJpName] = pd.to_numeric(plot_data["値"])
    sns.barplot(data=plot_data, x="会社名", y=elementJpName)
    plt.ylabel(elementJpName)
    plt.show()


compare_company_IR(
    all_data,
    "CurrentQuarterDuration",
    "jpcrp_cor:BasicEarningsLossPerShareSummaryOfBusinessResults",
    "EPS",
)

compare_company_IR(all_data, "CurrentYTDDuration", "jppfs_cor:GrossProfit", "粗利益")

for docID in docID_dict.values():
    save_csv(docID, type=1)

from edinet_xbrl.edinet_xbrl_parser import EdinetXbrlParser

parser = EdinetXbrlParser()
edinet_xbrl_object = parser.parse_file(
    "S100QZLI/XBRL/PublicDoc/jpcrp030000-asr-001_E04236-000_2023-03-31_01_2023-06-20.xbrl"
)

key = "jppfs_cor:Assets"
context_ref = "Prior1YearInstant"
current_year_assets = edinet_xbrl_object.get_data_by_context_ref(
    key, context_ref
).get_value()
current_year_assets

key = "jpcrp_cor:DividendPaidPerShareSummaryOfBusinessResults"
edinet_xbrl_object = parser.parse_file(
    r"S100QZLI\XBRL\PublicDoc\jpcrp030000-asr-001_E04236-000_2023-03-31_01_2023-06-20.xbrl"
)
interimDividendPaidPerShares = []
context_refs = [
    "Prior4YearDuration_NonConsolidatedMember",
    "Prior3YearDuration_NonConsolidatedMember",
    "Prior2YearDuration_NonConsolidatedMember",
    "Prior1YearDuration_NonConsolidatedMember",
    "CurrentYearDuration_NonConsolidatedMember",
]

for context_ref in context_refs:
    current_year_assets = edinet_xbrl_object.get_data_by_context_ref(
        key, context_ref
    ).get_value()
    interimDividendPaidPerShares.append(pd.to_numeric(current_year_assets))


plt.bar(
    [i for i in range(len(interimDividendPaidPerShares))], interimDividendPaidPerShares
)