Python一起处理2个for循环
嗯。下面是完整的代码。我想循环两个不同的数据集,每个数据集为期一年。获取每个hail prob值处的hailindx值百分位数,并绘制它们。因为我需要循环这两个1年的数据集,但速度非常慢Python一起处理2个for循环,python,loops,itertools,Python,Loops,Itertools,嗯。下面是完整的代码。我想循环两个不同的数据集,每个数据集为期一年。获取每个hail prob值处的hailindx值百分位数,并绘制它们。因为我需要循环这两个1年的数据集,但速度非常慢 from matplotlib import pyplot as plt from matplotlib import mlab import netCDF4 as net import numpy as np import itertools days=["01","02","03","04","05"
from matplotlib import pyplot as plt
from matplotlib import mlab
import netCDF4 as net
import numpy as np
import itertools days=["01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31"]
months=["01","02","03","04","05","06","07","08","09","10","11","12"]
hp_values=range(0,100)
for value in hp_values:
value1=[]
print value
for month,day in itertools.product(months,days):
print month,day
try:
hailindx1="/Trunk/2015HailIndx/HailIndx2015%s%sL0S_CONUS.nc"%(month,day)
hailprob1="/Trunk/2015/aerHailProb2015%s%s.nc" %(month,day)
hailindx=net.Dataset(hailindx1)
hailprob=net.Dataset(hailprob1)
hp=hailprob.variables['HailProb'][:]
hs=hailindx.variables['HailIndx'][:]
p=[0.05,0.1,0.2]
hp=np.array(hp)
hs=np.array(hs)
mask=(hp>0) & (hs>0)
hs=hs[mask]
hp=hp[mask]
value2=hs[hp==value]
if len(value2)>0:
value1.append(value2)
else:
continue
except:
continue
value_list=[value,value,value]
print value_list
if len(value1)>0:
perc=np.percentile(value1,p)
plt.plot(value_list,perc,marker='o',color='r')
else:
continue
plt.xlabel('HailProb')
plt.ylabel('HailIndx')
plt.show()
如果有人知道如何使循环更快 您可以使用获取所有组合。像这样:
for month, day in itertools.product(months, days):
...do something...
您可以使用
itertools
中的product()
函数:
from itertools import product
months=["01","02","03","04","05","06","07","08","09","10","11","12"]
days=["01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31"]
answer = list(product(months, days))
输出
[('01', '01'),
('01', '02'),
('01', '03'),
('01', '04'),
('01', '05'),
...
('12', '28'),
('12', '29'),
('12', '30'),
('12', '31')]
然后,您可以根据需要迭代
answer
变量。请注意,您的循环将返回不可能的日期,如2015/02/31。直接处理日期可能更好
还要注意,您正在加载和过滤每个数据文件100次;你真的只需要加载它一次,如果你聪明的话,你可以一次过滤它
另外,您的hp\u值
可能应该是范围(0,101)
ie 100是一个可能的值吗
差不多
from datetime import date, timedelta
import numpy as np
YEAR = 2015
# using datetime.strftime format codes
INDEX_FILE = "/Trunk/%YHailIndx/HailIndx%Y%m%dL0S_CONUS.nc"
PROB_FILE = "/Trunk/%Y/aerHailProb%Y%m%d.nc"
def date_range(start_date, end_date, step=timedelta(1)):
day = start_date
while day < end_date:
yield day
day += step
def main():
start = date(YEAR, 1, 1)
end = date(YEAR + 1, 1, 1)
for day in date_range(start, end):
# load index file
try:
index_file = day.strftime(INDEX_FILE)
index_data = net.Dataset(index_file)
except RuntimeError as re:
print(re)
print("Failed to load index file:", index_file)
continue
# load probability file
try:
prob_file = day.strftime(PROB_FILE)
prob_data = net.Dataset(prob_file)
except RuntimeError as re:
print(re)
print("Failed to load probability file:", prob_file)
continue
# start calculating
index = np.array(index_data.variables['HailIndx'])
prob = np.array(prob_data .variables['HailProb'])
#
# Here I started to get a bit lost trying to follow what
# you are doing; a sample index file and probability file
# would probably help in debugging, as would a better
# description of exactly what you are trying to do to
# the numbers ;-)
#
if __name__ == "__main__":
main()
from datetime导入日期,timedelta
将numpy作为np导入
年份=2015年
#使用datetime.strftime格式代码
INDEX_FILE=“/Trunk/%YHailIndx/HailIndx%Y%m%dL0S_CONUS.nc”
PROB_FILE=“/Trunk/%Y/aerHailProb%Y%m%d.nc”
定义日期范围(开始日期、结束日期、步长=时间增量(1)):
日期=开始日期
日期<结束日期:
收成日
天+=步数
def main():
开始=日期(年份,1,1)
结束=日期(年份+1,1,1)
对于日期范围内的日期(开始、结束):
#加载索引文件
尝试:
index\u file=day.strftime(index\u文件)
index\u data=net.Dataset(index\u文件)
除运行时错误外,请参阅:
打印(re)
打印(“加载索引文件失败:”,索引文件)
继续
#装载概率文件
尝试:
prob_文件=day.strftime(prob_文件)
prob_data=net.Dataset(prob_文件)
除运行时错误外,请参阅:
打印(re)
打印(“加载概率文件失败:”,prob_文件)
继续
#开始计算
index=np.array(index_data.variables['HailIndx']))
prob=np.array(prob_数据变量['HailProb'])
#
#在这里,我开始有点迷路了,试图去了解什么
#你在做什么;一个示例索引文件和概率文件
#可能有助于调试,更好的
#描述您正试图对其执行的操作
#数字;-)
#
如果名称=“\uuuuu main\uuuuuuuu”:
main()
它的速度慢是因为循环还是因为你在循环中所做的事情?itertools.product不会提供加速。。。只是说你是对的,只是试过了,都一样(它的速度慢是因为你对月/日所做的事情……重复这几乎是瞬间。你想用月和日做什么来表示它太慢?这不会给他一个加速……更糟糕的是,他在做什么更不清晰(即使这是OP要求的:P)