SCALE-Analysis
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Namespaces | Functions | Variables
Amedas.py File Reference

Namespaces

namespace  Amedas
 アメダスデータ取得・描画
 

Functions

 ll2dist (lat1, lon1, lat2, lon2)
 

Variables

bool savefig = True
 
str dir_out = "./fig/"
 
bool by_target_pos = True
 
float lon0 = 140.1115746
 
float lat0 = 36.0827355
 
bool by_target_name = not by_target_pos
 
str tname = "東京"
 
int year = 2007
 
int month = 7
 
int day = 15
 
str datatype0 = "hourly"
 
str url = 'https://www.data.jma.go.jp/obd/stats/etrn/select/prefecture00.php?prec_no=&block_no=&year=&month=&day=&view='
 
 html = urllib.request.urlopen(url)
 
 soup = BeautifulSoup(html, 'html.parser')
 
 elements = soup.find_all('area')
 
list area_list = [element['alt'] for element in elements]
 
list area_link_list = [element['href'] for element in elements]
 
list total_station_list = []
 
list total_station_link_list = []
 
list total_station_etc_list = []
 
int ind = 0
 
list station_etc_list = []
 
list station_list = []
 
list station_link_list = []
 
list total_station_list_flatten = [tmp1 for tmp2 in total_station_list for tmp1 in tmp2]
 
list total_station_link_list_flatten = [tmp1 for tmp2 in total_station_link_list for tmp1 in tmp2]
 
list total_station_etc_list_flatten = [tmp1 for tmp2 in total_station_etc_list for tmp1 in tmp2]
 
list total_station_type_list_flatten = [etc.split("javascript:viewPoint(")[1].split(",")[0][1] for etc in total_station_etc_list_flatten]
 
list total_station_lat_list_flatten = [float(etc.split("javascript:viewPoint(")[1].split(",")[4][1:-1]) + float(etc.split("javascript:viewPoint(")[1].split(",")[5][1:-1])/60 for etc in total_station_etc_list_flatten]
 
list total_station_lon_list_flatten = [float(etc.split("javascript:viewPoint(")[1].split(",")[6][1:-1]) + float(etc.split("javascript:viewPoint(")[1].split(",")[7][1:-1])/60 for etc in total_station_etc_list_flatten]
 
 stype = np.array(total_station_type_list_flatten)
 
 lat = np.array(total_station_lat_list_flatten)
 
 lon = np.array(total_station_lon_list_flatten)
 
 dist = ll2dist(lat0,lon0,lat,lon)
 
 argmin_dist = np.argmin(dist)
 
 station_index = argmin_dist
 
list station = total_station_list_flatten[station_index]
 
list station_link = total_station_link_list_flatten[station_index]
 
 stype0 = stype[station_index]
 
 lon_target = lon[station_index]
 
 lat_target = lat[station_index]
 
list block_no_str = station_link.split("block_no=")[1].split("&")[0]
 
str datatype = datatype0 + f"_{stype0}1.php?"
 
 fig
 
 ax = axes[1]
 
 figsize
 
 s
 
 table = pd.read_html(url)
 
 df = table[0]
 
 time = df["時"].values[:,0]
 
list jstdatetimelist = [datetime.datetime(year,month,day) + datetime.timedelta(hours=tmp) for tmp in time]
 
list utcdatetimelist = [tmp-datetime.timedelta(hours=9) for tmp in jstdatetimelist]
 
list time_target = jstdatetimelist[0].strftime("%Y-%m-%d ")
 
str varname_j = "降水量 (mm)"
 
str varname = "PREC"
 
str varunit = "(mm)"
 
 prec = df[varname_j].values[:,0]
 
 axes
 
 marker
 
str fnamelabel = f"Amedas_{varname}_{time_target}_lon{lon_target}_lat{lat_target}_{station}"