parser = argparse.ArgumentParser(
description='Reads one argument -t (TYPE (e.g csv , json , api ))')
parser.add_argument('-t', '--input_file_type', action='store',default = "csv",
help='FILE TYPE.')
args = parser.parse_args()
return args
if __name__ == '__main__':
# v_args = GetArgs()
# v_input_file_type = v_args.input_file_type
v_input_file_type = "csv"
# Import Data : class get_data
obj_get_data = getdata()
source_df = obj_get_data.ReadCSVData()
# Transformation : class transformation
obj_transformation = Transformation(source_df)
obj_transformation.set_time_readable()
obj_transformation.set_medium_source_path()
top_source_medium_df = obj_transformation.final_dataframe()
#v_name = top_source_medium_df.
print(top_source_medium_df)
distinctusers_batch = obj_transformation.execute_metrics(10000)
print(distinctusers_batch)
distinctusers_day = obj_transformation.calc_distinctusers_perday()
print(distinctusers_day)
def main():
X, Y = getdata()
print(len(X))
model = ann(200)
model.fit(X, Y)
print(model.score(X, Y))
position.append((y2,x2))
extreme.append(h_extreme2)
return position,extreme
if __name__ == "__main__":
data = read() # 读取全部数据
# 根据风场设置假定中心点 , 滤波前，由500hPa风场确定的大致中心位置,m是纬度，n是经度
m = 32;n=86
p0 = (m,n)
d = 0.25
# 取得想要的初始范围内的数据,y纬度,x经度, 初始范围内的位势高度场和涡度场
height, vorticity= getdata(data,m,n,d)
h40,h45,h50,h55 = height
vo40,vo45,vo50,vo55 = vorticity
# 迭代过程中的一系列位置
ps_h40,exh40 = pos_height(vo55,m,n,d)
ps_h45,exh45 = pos_height(h45,m,n,d)
ps_h50,exh50 = pos_height(h50,m,n,d)
ps_h55,exh55 = pos_height(h55,m,n,d)
ps_vo40,exvo40 = pos_vorticity(vo40,m,n,d)
ps_vo45,exvo45 = pos_vorticity(vo45,m,n,d)
ps_vo50,exvo50 = pos_vorticity(vo50,m,n,d)
ps_vo55,exvo55 = pos_vorticity(vo55,m,n,d)
print(ps_h40,exh40)
# -*- coding: utf-8 -*-
# @Time : 2020/3/16 11:07
# @Author : 永
# @File : MovingAverage.py
# @Software: PyCharm
import numpy as np
import pandas as pd
from get_data import getdata
import talib
import cgitb
# 这句放在所有程序开始前，这样就可以正常打印异常了
cgitb.enable(format="text")
df = getdata()
def movingaverage(l, N):
sum = 0
result = list(0 for x in l)
for i in range(0, N):
sum = sum + l[i]
result[i] = sum / (i + 1)
for i in range(N, len(l)):
sum = sum - l[i - N] + l[i]
result[i] = sum / N
return result
def rsiFunc(prices, n=14):
deltas = np.diff(prices)
seed = deltas[:n + 1]
position.append((y2,x2))
extreme.append(h_extreme2)
return position,extreme
if __name__ == "__main__":
data = read() # 读取全部数据
# 第0步，根据风场假定中心点
# 根据风场设置假定中心点
m = 32;n=86 # 滤波前，由500hPa风场确定的大致中心位置,m是纬度，n是经度
p0 = (m,n)
d = 0.25
# 初始范围内的位势高度场和涡度场
height, vorticity= getdata(data,m,n,d) # 取得想要的初始范围内的数据,y纬度,x经度
h40,h45,h50,h55 = height
vo40,vo45,vo50,vo55 = vorticity
# ps0 = pos(data0)
# 迭代过程中的一系列位置
ps_h40,exh40 = pos_height(vo55,m,n,d)
ps_h45,exh45 = pos_height(h45,m,n,d)
ps_h50,exh50 = pos_height(h50,m,n,d)
ps_h55,exh55 = pos_height(h55,m,n,d)
ps_vo40,exvo40 = pos_vorticity(vo40,m,n,d)
ps_vo45,exvo45 = pos_vorticity(vo45,m,n,d)
ps_vo50,exvo50 = pos_vorticity(vo50,m,n,d)
ps_vo55,exvo55 = pos_vorticity(vo55,m,n,d)