気象庁のホームページから世界103地点の気象データを入手して、気候区分を判定するプログラム

koppen.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__version__ = '0.0.1'
## import required libraries
import pandas
from decimal import Decimal
import numpy as np

## CONSTANT
PROGRAM_DESCRIPTION = '''
###############################################################################
ケッペンの気候区分判定プログラム
作者: KouQ7802
ライセンス: GPLv3
''' + 'バージョン: ' + __version__ + '''
###############################################################################
'''

REGION = {0:'東アジア・シベリア',
          1:'南アジア',
          2:'ヨーロッパ',
          3:'中東・アフリカ',
          4:'北アメリカ',
          5:'南アメリカ',
          6:'オセアニア',
          7:'南極'}

KOPPEN_CLIMATIC_DIVISION_LIST = {'Af':'熱帯雨林気候',
                                 'Am':'熱帯雨林気候(弱い乾季あり)',
                                 'Aw':'サバナ気候',
                                 'BS':'ステップ気候',
                                 'BW':'砂漠気候',
                                 'Cfa':'温暖湿潤気候',
                                 'Cfb':'西岸海洋性気候',
                                 'Cs':'地中海性気候',
                                 'Cw':'温暖冬季少雨気候',
                                 'Df':'亜寒帯湿潤気候',
                                 'Dw':'亜寒帯冬季少雨気候',
                                 'ET':'ツンドラ気候',
                                 'EF':'氷雪気候'}


## preprocess
# fetch climatic data from server of Japan Meteorological Agency
# 気象庁のWebページより世界の主要な地点の月別の気温・降水量平年値のデータを得る
CLIMATIC_DATA_SOURCE = 'http://www.data.jma.go.jp/gmd/cpd/monitor/mainstn/nrmlist.php'
RAW_CLIMATIC_DATA = pandas.io.html.read_html(CLIMATIC_DATA_SOURCE)
 
# rearrange CLIMATIC_DATA
# CLIMATIC_DATAを整理する
for i in range(len(RAW_CLIMATIC_DATA)):
    for j in range(2, len(RAW_CLIMATIC_DATA[i]), 2):
        for k in range(12, -1, -1):
            RAW_CLIMATIC_DATA[i].xs(j)[k+2] = RAW_CLIMATIC_DATA[i].xs(j)[k]
        RAW_CLIMATIC_DATA[i].xs(j)[0] = np.nan
        RAW_CLIMATIC_DATA[i].xs(j)[1] = np.nan


## define functions
def show_region():
    for i in range(len(REGION)):
        print(i, ':', REGION[i])
    print(len(REGION), ':', '全地域')

def show_city(region_key):
    if region_key == len(REGION):
        for i in range(len(RAW_CLIMATIC_DATA)):
            for j in range(1, len(RAW_CLIMATIC_DATA[i]), 2):
                print(i, REGION[i], ':', RAW_CLIMATIC_DATA[i][0][j])
        print('===============================================================================')
    else:
        for i in range(1, len(RAW_CLIMATIC_DATA[region_key]), 2):
            print(i, ':', RAW_CLIMATIC_DATA[region_key][0][i])        
        
def search_index(city_name):
    for i in range(len(RAW_CLIMATIC_DATA)):
        for j in range(1, len(RAW_CLIMATIC_DATA[i]), 2):
            if city_name in RAW_CLIMATIC_DATA[i][0][j]:
                city_index = (i, j)
                break
    return city_index

def get_city_index():
    while True:
        show_region()
        while True:
            print('判定対象の都市がある地域の番号を入力してください')
            try:
                region_key = int(input('-->'))
                break
            except ValueError:
                continue
        if region_key<0 or 8<region_key:
            continue
        show_city(region_key)
        if region_key == 8:
            continue
        print('判定対象の都市は見つかりましたか？(Y/n)')
        switch = input('-->')
        if switch == 'Y' or switch == 'y':
            break
        else:
            continue
        
    while True:
        print('都市名または都市の番号を入力してください')
        city_name = input('-->')
        try:
            if isinstance(int(city_name), int):
                if int(city_name) % 2 == 1:
                    city_index = (region_key, int(city_name))
                    break
                else:
                    pass
        except ValueError:
            pass
        if isinstance(city_name, str):
            try:
                city_index = search_index(city_name)
                break
            except UnboundLocalError:
                print('入力された都市は見つかりませんでした')
                continue
        else:
            continue
    return city_index

def show_target_city(city_index):
    print('都市名: ', RAW_CLIMATIC_DATA[city_index[0]][0][city_index[1]])
    print('国名または地域名: ', RAW_CLIMATIC_DATA[city_index[0]][1][city_index[1]])
    print('太州名: ', REGION[city_index[0]])

# generate climatic data(pandas.DataFrame)
def gen_pandas_dataframe(city_index):
    city_climatic_data = pandas.DataFrame()
    city_temperature = pandas.DataFrame()
    city_precipitation = pandas.DataFrame()
    for i in range(3,15):
        city_temperature = \
            city_temperature.append( [Decimal(RAW_CLIMATIC_DATA[city_index[0]].xs(city_index[1])[i])] )
        if RAW_CLIMATIC_DATA[city_index[0]].xs(city_index[1]+1)[i] == '---':
            city_precipitation = city_precipitation.append([np.nan])
        else:
            city_precipitation = \
                city_precipitation.append( [Decimal(RAW_CLIMATIC_DATA[city_index[0]].xs(city_index[1]+1)[i])] )        
    city_temperature = city_temperature.append([city_temperature.mean()])
    city_precipitation = city_precipitation.append([city_precipitation.sum()])
    city_climatic_data = pandas.concat([city_temperature, city_precipitation], axis=1)
    city_climatic_data.index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 'annual']
    city_climatic_data.columns = ['temperature', 'precipitation']
    return city_climatic_data

# judge North Hemisphere or South Hemisphere
def judge_north_south(city_climatic_data):
    temperature_high_to_low = city_climatic_data[:12].sort('temperature', ascending=False)
    if 4 <= temperature_high_to_low.index[0] <= 9:
        north_south = True
    else:
        north_south = False
    return north_south

# judge climatic region of target city
def judge_fsw(city_climatic_data):
    max_precipitation = city_climatic_data['precipitation'][:12].max()
    min_precipitation = city_climatic_data['precipitation'][:12].min()
    precipitation_high_to_low = city_climatic_data[:12].sort('precipitation', ascending=False)
    if judge_north_south(city_climatic_data):
        if 4<= precipitation_high_to_low.index[11] <= 9:
            if min_precipitation*3 <= max_precipitation:
                climatic_region = 's'
            else:
                climatic_region = 'f'
        else:
            if min_precipitation*10 <= max_precipitation:
                climatic_region = 'w'
            else:
                climatic_region = 'f'
    else:
        if 4<= precipitation_high_to_low.index[11] <= 9:
            if min_precipitation*10 <= max_precipitation:
                climatic_region = 'w'
            else:
                climatic_region = 'f'
        else:
            if min_precipitation*3 <= max_precipitation:
                climatic_region = 's'
            else:
                climatic_region = 'f'
    return climatic_region

# judge climatic zone of target city
def judge_ACDE(city_climatic_data):
    climatic_division = {"climatic_zone":"", "climatic_region":"", "temperature":""}
    max_temperature = city_climatic_data['temperature'][:12].max()
    min_temperature = city_climatic_data['temperature'][:12].min()
    max_precipitation = city_climatic_data['precipitation'][:12].max()
    min_precipitation = city_climatic_data['precipitation'][:12].min()
    
    if max_temperature < 10:
        climatic_division["climatic_zone"] = "E"
        if max_temperature >= 0:
            climatic_division["climatic_region"] = "T"
        else:
            climatic_division["climatic_region"] = "F"

    elif min_temperature >= 18:
        climatic_division["climatic_zone"] = "A"
        if min_precipitation >= 60:
            climatic_division["climatic_region"] = "f"
        else:
            if (Decimal(-0.04)*Decimal(city_climatic_data['precipitation']['annual'])+100) > min_precipitation:
                climatic_division["climatic_region"] = "w"
            else:
                climatic_division["climatic_region"] = "m"

    elif min_temperature >= -3:
        climatic_division["climatic_zone"] = "C"
        climatic_division["climatic_region"] = judge_fsw(city_climatic_data)
        if climatic_division["climatic_region"] == "f":
            if max_temperature >= 22:
                climatic_division["temperature"] = "a"
            else:
                climatic_division["temperature"] = "b"
        
    else:
        climatic_division["climatic_zone"] = "D"
        climatic_division["climatic_region"] = judge_fsw(city_climatic_data)
    return climatic_division    

# judge climatic zone whether "B" or not
def judge_B(city_climatic_data,climatic_division):
    if climatic_division["climatic_zone"] != "E":
        annual_mean_temperature = city_climatic_data['temperature']['annual']
        annual_precipitation = city_climatic_data['precipitation']['annual']
        if climatic_division["climatic_region"] == "s":
            arid_boundary = 20 * annual_mean_temperature
        elif climatic_division["climatic_region"] == "f":
            arid_boundary = 20 * (annual_mean_temperature+7)
        elif climatic_division["climatic_region"] == "m":
            arid_boundary = 20 * (annual_mean_temperature+7)
        elif climatic_division["climatic_region"] == "w":
            arid_boundary = 20 * (annual_mean_temperature+14)
        if annual_precipitation < arid_boundary:
            climatic_division["climatic_zone"] = "B"
            if annual_precipitation < arid_boundary/2:
                climatic_division["climatic_region"] = "W"
                climatic_division["temperature"] = ""
            else:
                climatic_division["climatic_region"] = "S"
                climatic_division["temperature"] = ""
    else:
        pass
    return climatic_division

def show_result(city_index, city_climatic_data, climatic_division):
    climatic_division_str = climatic_division["climatic_zone"]+\
                            climatic_division["climatic_region"]+\
                            climatic_division["temperature"]
    print('=================================== result ====================================')
    show_target_city(city_index)
    if judge_north_south(city_climatic_data) == True:
        print('北半球')
    else:
        print('南半球')
    print('-------------------------------------------------------------------------------')
    print(city_climatic_data)
    print('-------------------------------------------------------------------------------')
    print('気候区分: ', climatic_division_str)
    print('気候区名: ', KOPPEN_CLIMATIC_DIVISION_LIST[climatic_division_str])
    print("===============================================================================")

# main function
def main():
    print(PROGRAM_DESCRIPTION)
    city_index = get_city_index()
    print(show_target_city(city_index))
    print('対象地点のINDEX:', city_index)
    city_climatic_data = gen_pandas_dataframe(city_index)
    climatic_division = judge_ACDE(city_climatic_data)
    climatic_division = judge_B(city_climatic_data,climatic_division)
    show_result(city_index, city_climatic_data, climatic_division)


## main process
if __name__ == '__main__':
    main()

## end