def todofunction(root_path):
# 发现别人插值的没有SpatialReference,还是要自己插值一遍
# todo 为了论文进度，先手动都插值了一遍，等有空了自己调用api做一遍
# 1.下一步要实现 调一下QGIS的插值API,做操作，参考：https://gis.stackexchange.com/questions/100188/how-to-compute-an-interpolation-raster-from-the-python-console-in-qgis/233322#233322
# aro_stage_ETL(root_path,'乳熟')
station_list = pandas.read_csv(os.path.join(root_path, 'HHHstations.csv'))
result_file = (os.path.join(root_path, 'stations_cx.txt'))
for index, lonlatdata in station_list.iterrows():
try:
lon = float(lonlatdata['Longitude'])
lat = float(lonlatdata['Latitude'])
StationID = lonlatdata['StationID']
station_name = lonlatdata['StationName']
for i in range(2001, 2014):
year = str(i)
stage_start_day, stage_end_day = get_grow_stage_by_lonlat(root_path, lon, lat, year, stage='CX')
# print (round(stage_start_day),round(stage_end_day))
fu, start_month, start_day = Common_func.day_num_to_yyyymmdd(year, stage_start_day)
fu, end_month, end_day = Common_func.day_num_to_yyyymmdd(year, stage_end_day)
row = str(StationID) + ' ' + station_name + ' ' + str(lon) + ' ' + str(lat) + ' ' + year + ' ' + str(
round(stage_start_day)) + ' ' + str(round(stage_end_day))
print(row)
#Modis_IO.write_txt(result_file, row)
except:
print('no data')
def get_station_value_by_num_day(root_path,
year,
daydir,
StationID,
T_tpye='HighestTemperature'):
fu, month, day = Common_func.day_num_to_yyyymmdd(year, daydir)
data = pd.read_csv(os.path.join(root_path, 'meteodata.csv'))
station_value = data[(data['Year'] == int(year))
& (data['Months'] == int(month)) &
(data['Days'] == int(day)) &
(data['StationID'] == StationID)].head()
try:
station_high_value = (station_value[T_tpye].values[0].astype(int)) / 10
except:
station_high_value = -273.15
# print(station_high_value)
return fu, station_high_value
def get_heat_stress_hours_every_station(root_path, stationID, station_name,
lon, lat, year, stage_start_day,
stage_end_day, heat_temperature,
result_file):
"""
计算每个点的高温时长
:param root_path: 数据根目录
:param stationID: 点id
:param station_name: 点名字
:param lon: 经度
:param lat: 纬度
:param year: 年份
:param stage_start_day: 生育期起始日期
:param stage_end_day: 生育期截至日期
:param heat_temperature: 高温阈值
:param result_file: 结果保存文件路径
:return: 输出 输入参数+高温时长
"""
#heat_temperature =heat_temperature*10
meteo_data = pandas.read_csv(os.path.join(root_path, 'stationdata.csv'))
sum_heat_days = 0
sum_heat_hours = 0
for day_num in range(stage_start_day, stage_end_day + 1):
fu, month, day = Common_func.day_num_to_yyyymmdd(year, day_num)
heat_days = meteo_data[
(meteo_data['StationID'] == stationID)
& (meteo_data['Year'] == year) & (meteo_data['Months'] == month) &
(meteo_data['Days'] == day) &
(meteo_data['HighestTemperature'] >= heat_temperature)]
if heat_days.empty:
print(station_name + ' ' + str(year) + ' ' + str(day_num) + ' ' +
'no heat!')
continue
else:
try:
# 高温时长模型实现
# 参数 太阳赤纬
sun_chiwei = 0.39795 * math.cos(0.98563 * (day_num - 173))
T_max = heat_days['HighestTemperature'].values[0] / 10
T_min = heat_days['LowestTemperature'].values[0] / 10
# fix a bug 天数-1 之后转日期，不能直接日-1 会报错
fu_next, month_next, day_next = Common_func.day_num_to_yyyymmdd(
year, day_num + 1)
T_min_tomorrow = meteo_data[
(meteo_data['StationID'] == stationID)
& (meteo_data['Year'] == year) &
(meteo_data['Months'] == month_next) &
(meteo_data['Days']
== day_next)]['LowestTemperature'].values[0] / 10
a = math.sin(lat) * math.sin(sun_chiwei)
b = math.cos(lat) * math.cos(sun_chiwei)
DL = 12 * (1 + (2 / math.pi) * a * (math.sin(a / b)))
p = 2
heat_temperature_real = heat_temperature / 10
A1 = math.asin(
(heat_temperature_real - T_min) / (T_max - T_min))
A2 = math.asin((heat_temperature_real - T_min_tomorrow) /
(T_max - T_min_tomorrow))
heat_stress_hours = (DL + 2 * p) * (1 - (A1 + A2) / math.pi)
sum_heat_hours = sum_heat_hours + heat_stress_hours
sum_heat_days = sum_heat_days + 1
row = str(stationID) + ' ' + station_name + ' ' + str(
lon) + ' ' + str(lat) + ' ' + str(year) + ' ' + str(
round(stage_start_day)) + ' ' + str(
round(stage_end_day)) + ' ' + str(
day_num) + ' ' + str(T_max) + ' ' + str(
format(heat_stress_hours, '2f'))
print(row)
Modis_IO.write_txt(result_file, row)
except:
continue
row = str(stationID) + ' ' + station_name + ' ' + str(lon) + ' ' + str(
lat) + ' ' + str(year) + ' ' + str(sum_heat_days) + ' ' + str(
format(sum_heat_hours, '2f'))
file = str(year) + '.txt'
Modis_IO.write_txt(file, row)
station_list = grid_station_data['stationID'].unique()
for stationid in station_list:
lon, lat = stations.Station_ETL.get_lonlat_by_stationID(stationid)
sum_days = len(orig_data[(orig_data['stationID'] == stationid)])
val_days = len(
grid_station_data[(grid_station_data['stationID'] == stationid)])
cx_sum_days = 0
cx_val_days = 0
cx_start_fu = 0
cx_end_fu = 0
for year in range(2002, 2014):
cx_start, cx_end = stations.Station_ETL.get_cx_by_stationID(
stationid, year)
cx_start_fu, month, day = Common_func.day_num_to_yyyymmdd(
year, cx_start)
cx_end_fu, month, day = Common_func.day_num_to_yyyymmdd(year, cx_end)
cx_sum_days = cx_sum_days + len(
orig_data[(orig_data['stationID'] == stationid)
& (orig_data['date'].astype(int) > int(cx_start_fu)) &
(orig_data['date'].astype(int) < int(cx_end_fu))])
cx_val_days = len(
grid_station_data[(orig_data['stationID'] == stationid)
& (grid_station_data['date'] > int(cx_start_fu))
& (grid_station_data['date'] < int(cx_end_fu))])
print(stationid, lon, lat, sum_days, val_days,
"%.2f%%" % (val_days / sum_days * 100), cx_start_fu, cx_end_fu,
cx_sum_days, cx_val_days,
"%.2f%%" % (cx_val_days / cx_sum_days * 100))