get.py

import pandas as pd
import numpy as np
import os
import json


def convert_to_int(value):
    return int(value)


def get_word_epidemic(date):
    """
    根据日期获取所有国家对应的累计确诊、累计死亡、累计治愈
    以及最大的累计确诊值和最小的累计确诊值
    格式： [["country",[confirmed,death,recovered]],...]
    """
    date_name = date.replace("/", "-")
    directory = "data/world-epidemic/"
    if os.path.isfile(directory + date_name + ".csv"):
        file = open(os.path.join(directory, date_name + ".csv"),
                    'r', encoding="utf-8")
        data = pd.read_csv(file)
        file.close()
        max = data["Confirmed"].max()
        min = data["Confirmed"].min()
        result_list = []
        for index in range(0, data.shape[0]):
            item = data.iloc[index]
            result_list.append([item["Country_Region"], [int(item["Confirmed"]),
                                                         int(item["Deaths"]), int(item["Recovered"])]])
        return int(max), int(min), result_list
    else:
        return None, None, None


def get_country_epidemic(date, country):
    """
    获取国家的疫情数据
    :param date: 04/25/2020
    :param country:  China
    :return: list
    """
    date_name = date.replace("/", "-")
    file_name = "data/country-epidemic/" \
                + date_name + "-" + country + ".csv"
    if os.path.isfile(file_name):
        data = pd.read_csv(
            os.path.join("data/country-epidemic/", date_name + "-" + country + ".csv"),
            encoding="utf-8")
        confirmed = data["Confirmed"].astype(int)
        max = confirmed.max()
        min = confirmed.min()
        result = []
        for index in range(0, data.shape[0]):
            result.append(data.iloc[index].tolist())
        return max, min, result
    else:
        return None


def get_country_epidemic_summary(country):
    """
    获取国家疫情概述数据
    :param country:
    :return: list
    """
    file_name = "data/country-epidemic-summary/" + country + ".csv"
    if os.path.exists(file_name):
        file = open(file_name, mode="r", encoding="utf-8")
        data = pd.read_csv(file, dtype=np.object)
        file.close()
        confirmed_list = []
        confirmed_change_list = []
        death_list = []
        death_change_list = []
        date_list = []
        for index in range(0, data.shape[0]):
            item = data.iloc[index]
            date_list.append(item["Updated"])
            confirmed_list.append(item["Confirmed"])
            confirmed_change_list.append(item["ConfirmedChange"])
            death_list.append(item["Deaths"])
            death_change_list.append(item["DeathsChange"])
        return date_list, confirmed_list, confirmed_change_list, \
               death_list, death_change_list
    else:
        return None


# "Côte d'Ivoire.csv

def get_confirmed_distribution(country, date):
    """
    获取累计确诊的分布情况
    :param country: China
    :param date: 04/25/2020
    :return: list
    """
    date_name = date.replace("/", "-")
    file_name = "data/country-epidemic/" \
                + date_name + "-" + country + ".csv"
    world = "Worldwide"
    if os.path.isfile(file_name):
        file = open(os.path.join("data/country-epidemic/",
                                 date_name + "-" + country + ".csv"),
                    "r", encoding="utf-8")
        data = pd.read_csv(file)
        file.close()
        if country == world:
            data = data[["Country_Region", "Confirmed"]]
        else:
            data = data[["AdminRegion1", "Confirmed"]]
        data.sort_values(by="Confirmed", inplace=True, ascending=False)
        result = []
        if data.shape[0] > 20:
            data1 = data.head(20)
            confirmed = data.tail(-20)["Confirmed"].sum()
            dict1 = {}
            if country == world:
                dict1["Country_Region"] = "其它"
            else:
                dict1["AdminRegion1"] = "其它"
            dict1["Confirmed"] = confirmed
            data = data1.append(dict1, ignore_index=True)
        if country == world:
            for index in range(0, data.shape[0]):
                row = data.iloc[index]
                result.append([row["Country_Region"], int(row["Confirmed"])])
        else:
            for index in range(0, data.shape[0]):
                row = data.iloc[index]
                result.append([row["AdminRegion1"], int(row["Confirmed"])])
        return result
    else:
        return None


def get_new_confirmed_top5(country, date):
    """
    根据日期和国家获取新增确诊的top5
    :param country:"China"
    :param date: "04/24/2020
    :return: list
    """
    date_name = date.replace("/", "-")
    file_name = "data/country-epidemic/" \
                + date_name + "-" + country + ".csv"
    if os.path.isfile(file_name):
        file = open(file_name, "r", encoding="utf-8")
        data = pd.read_csv(file, dtype=np.object)
        file.close()
        if data.shape[0] == 0:
            return None
        data.sort_values(by="ConfirmedChange", ascending=False, inplace=True)
        result = []
        area = []
        if data.shape[0] > 5:
            data = data.head(5)
        for index in range(0, data.shape[0]):
            item = data.iloc[index]
            area.append(item["AdminRegion1"])
            result.append(item["ConfirmedChange"])
        return [area, result]
    else:
        return None


def get_region_comparion(country, date, attribute):
    """
    获取地区对比的疫情数据
    :param country: "China"
    :param date:  04/25/2020
    :param attribbute:  属性名称{Confirmed|Deaths|Recovered|ConfirmedChange}
    :return:Nothing
    """
    date_name = date.replace("/", "-")
    file_name1 = "data/country-epidemic/" \
                 + date_name + "-" + country + ".csv"
    file_name2 = "data/region-comparision/" + country + ".csv"
    if os.path.isfile(file_name1) and os.path.isfile(file_name2):
        file1 = open(file_name1, "r", encoding="utf-8")
        data = pd.read_csv(file1, dtype=np.object)
        file1.close()
        if attribute not in data.columns:
            return None
        data.sort_values(by=attribute, inplace=True, ascending=False)

        if data.shape[0] > 5:
            data = data.head(5)
        region_list = data["AdminRegion1"].tolist()

        data = pd.read_csv(
            open(os.path.join("data/region-comparision/", country + ".csv"),
                 "r", encoding="utf-8"), dtype=np.object)
        region_column = data["AdminRegion1"]
        result = {}
        for region in region_list:
            item = data[region_column == region][["Updated", attribute]]
            date_list = item["Updated"].tolist()
            data_list = item[attribute].tolist()
            result[region] = [date_list, data_list]
        return result
    else:
        return None

def get_date_list():
    """

    """
    file_name = "data/country-epidemic-summary/Worldwide.csv"
    if os.path.isfile(file_name):
        return pd.read_csv(file_name)["Updated"].tolist()
    else:
        return None


def get_time_axis_data(date):
    """

    :return:
    """
    date_name = date.replace("/", "-")
    file_name = "data/world-epidemic/" + date_name + ".csv"
    if os.path.isfile(file_name):
        data = pd.read_csv(file_name, encoding="utf-8")
        data.sort_values(by="Confirmed", ascending=False, inplace=True)
        result = []
        max = int(data.head(1)["Confirmed"])
        min = int(data.tail(1)["Confirmed"])
        for index in range(0, data.shape[0]):
            row = data.iloc[index]
            result.append([str(row["Country_Region"]),
                           [int(row["Confirmed"]), str(row["Country_Region"])]])
        return max, min, result


def get_world_confirmed():
    """

    """
    result = []
    date_list = get_date_list()
    for date in date_list:
        date_name = date.replace("/", "-")
        file_name = "data/world-epidemic/" + date_name + ".csv"
        data = pd.read_csv(file_name, encoding="utf-8")
        result.append(int(data.iloc[0]["Confirmed"]))
    return result


def get_country_status(country="China"):
    """

    """
    file_name = "data/country-status/country_status.csv"
    if os.path.isfile(file_name):
        file = open(file_name, "r", encoding="utf-8")
        data = pd.read_csv(file, dtype=np.str)
        file.close()
        data = data[data["Country_Region"] == country]
        if data.shape[0] == 0:
            return None
        data = data.iloc[0].tolist()
        c = int(data[0])
        nc = int(data[1])
        d = int(data[2])
        nd = int(data[3])
        r = int(data[4])
        nr = int(data[5])
        total = c + r + d
        total_today = nc + nd + nr
        dict = {
            'total': format(total, ','),
            'total_today': format(total_today, ','),
            'confirm_total': format(c, ','),
            'confirm_today': format(nc, ','),
            'recover_total': format(d, ','),
            'recover_today': format(nd, ','),
            'death_total': format(r, ','),
            'death_today': format(nr, ',')
        }
        return dict
    else:
        return None


def get_country_list_with_data():
    """

    """
    file_name = "data/country-status/country_status.csv"
    if os.path.isfile(file_name):
        data = pd.read_csv(file_name, encoding="utf-8")
        country_list = []
        tmp = []
        for index in range(0, data.shape[0]):
            row = data.iloc[index]
            total = int(row["Confirmed"]) \
                    + int(row["Deaths"]) \
                    + int(row["Recovered"])
            country = row["Country_Region"]
            tmp.append([country, total])
        tmp_df = pd.DataFrame(data=tmp, columns=["country", "total"])
        tmp_df.sort_values(by="total", inplace=True, ascending=False)
        for index in range(0, tmp_df.shape[0]):
            row = tmp_df.iloc[index]
            country_list.append({"name": row["country"],
                                 "number": format(row["total"], ',')})
        return country_list
    else:
        return None


def get_today():
    """
    返回最新日期
    :return str
    """
    file_name = "data/country-epidemic-summary/Worldwide.csv"
    if os.path.isfile(file_name):
        return str(pd.read_csv(file_name)["Updated"].tolist()[-1])
    else:
        return None


def get_treemap_data(today):
    """

    """
    data_list = []
    source = pd.read_csv("data/Bing-COVID19-Data.csv", encoding="utf-8")
    source = source[source.Updated == today]
    country_list = source["Country_Region"].unique()
    country_list = country_list[1:]
    for country_name in country_list:
        data = source[source.Country_Region == country_name]
        if data.shape[0] == 0:
            continue
        data = data[["Confirmed", "Country_Region", "AdminRegion1", "AdminRegion2"]]
        country_value = int(data[data["AdminRegion1"].isna()]["Confirmed"])
        if data.shape[0] <= 1:
            data_list.append({"name": str(country_name), "value": int(country_value), "children": []})
            continue
        ar1_list = []
        tmp = data[data["AdminRegion2"].isna()].fillna(value="nan")
        for index in range(0, tmp.shape[0]):
            row = tmp.iloc[index]
            if row["AdminRegion1"] == "nan":
                continue
            tmp1 = data[data.AdminRegion1 == row["AdminRegion1"]]
            if tmp1.shape[0] == 0:
                ar1_list.append({"name": str(row["AdminRegion1"]), "value": int(row["Confirmed"]), "children": []})
                continue
            ar2_list = []
            tmp1 = tmp1[tmp1["AdminRegion2"].notna()]
            for j in range(0, tmp1.shape[0]):
                item = tmp1.iloc[j]
                ar2_list.append({"name": str(item["AdminRegion2"]), "value": int(item["Confirmed"]), "children": []})
            ar1_list.append({"name": str(row["AdminRegion1"]), "value": int(row["Confirmed"]), "children": ar2_list})
        data_list.append({"name": str(country_name), "value": int(country_value), "children": ar1_list})
    return {"children": data_list}


def get_confirmed_pre(country):
    file_name = "prediction_data/" + country + ".csv"
    if os.path.isfile(file_name):
        data = pd.read_csv(file_name, encoding="utf-8")
        confirmed = []
        for index in range(0, data.shape[0]):
            confirmed.append(int(data.iloc[index]["Confirmed"]))
        return confirmed
    else:
        return None