如何解决如何按秒对 xarray 进行上采样并包括边界时间
ary["time"] = [
"2000-01-01T03:04:05",# leading records are missing,"2000-01-01T03:04:06","2000-01-01T03:04:08",# some medium records are missing,"2000-01-01T03:04:09","2000-01-01T03:04:11",...
"2000-01-01T06:54:02","2000-01-01T06:54:03" # and trailing records are missing.
]
并且想要重新索引到
ary["time"] = [
"2000-01-01T03:00:00","2000-01-01T03:00:01","2000-01-01T03:00:02",...
"2000-01-01T03:04:06","2000-01-01T03:04:07",...
"2000-01-01T06:59:57","2000-01-01T06:59:58","2000-01-01T06:59:59"
]
并在所有缺失的记录处设置 NaN。
我找到了 ary = ary.resample(time="1S").asfreq()
,但它只插入中等记录。
如何指示左右边界是每小时? (或几分钟或几天?)
示例(取自 gist):
from datetime import datetime,timedelta
import numpy as np
import pandas as pd
import xarray as xr
def make_ary():
time = []
for i in range(300,14000):
if i % 3 != 2 and i % 5 != 2:
time.append(datetime(2000,1,3,0) + timedelta(seconds=i))
data = np.random.rand(len(time))
return xr.DataArray(data=data,coords=[("time",time)],dims=["time"])
def make_expected():
expected = []
for i in range(0,4*60*60):
expected.append(
datetime(2000,0) + timedelta(seconds=i)
)
return pd.to_datetime(np.array(expected))
def make_not_expected():
'''
result of 'inserts medium records'
'''
not_expected = []
for i in range(300,14000):
not_expected.append(
datetime(2000,0) + timedelta(seconds=i)
)
return pd.to_datetime(np.array(not_expected))
def resample(ary):
return ary.resample(time="1S").asfreq()
def main():
ary = make_ary()
expected = make_expected()
not_expected = make_not_expected()
print(np.array_equal(ary["time"].values,expected)) # False
ary = resample(ary)
print(np.array_equal(ary["time"],expected)) # False
print(np.array_equal(ary["time"],not_expected)) # True,but not expected
main()
解决方法
实现您想要的一种方法,就是在开头和结尾附加一个 NaN
pad 到相应的时间戳,然后只使用 resample
:
start_timestamp = "2000-01-01T03:00:00"
stop_timestamp = "2000-01-01T06:59:59"
ary2 = xr.concat([
xr.DataArray(data=[np.nan],coords=[("time",pd.date_range(start=start_timestamp,freq="1S",periods=1))],dims=["time"]),ary,xr.DataArray(data=[np.nan],pd.date_range(start=stop_timestamp,dims=["time"])
],dim="time").resample(time="1s").asfreq()
给你:
print(ary2.time)
# <xarray.DataArray 'time' (time: 14400)>
# array(['2000-01-01T03:00:00.000000000','2000-01-01T03:00:01.000000000',# '2000-01-01T03:00:02.000000000',...,'2000-01-01T06:59:57.000000000',# '2000-01-01T06:59:58.000000000','2000-01-01T06:59:59.000000000'],# dtype='datetime64[ns]')
# Coordinates:
# * time (time) datetime64[ns] 2000-01-01T03:00:00 ... 2000-01-01T06:59:59
,
使用 DataArray.reindex (Documentation)
特别是在这种情况下,DataArray.reindex
可能是更好的选择。
在下面的代码示例中,目标数组的日期范围用date_range
指定(注意参数closed
设置为"left"
,因为我们不想要范围包括"2000-01-01T07:00:00"
。
start_time = "2000-01-01T03:00:00"
end_time = "2000-01-01T07:00:00"
new_ary = ary.reindex(time=pd.date_range(start=start_time,end=end_time,closed='left'))
print(ary)
这给出了以下输出:
<xarray.DataArray 'time' (time: 14400)>
array(['2000-01-01T03:00:00.000000000','2000-01-01T03:00:02.000000000','2000-01-01T06:59:58.000000000',dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2000-01-01T03:00:00 ... 2000-01-01T06:59:59
默认情况下,reindex
用 NaN 填充缺失值。下面测试代码的输出表明,对于新数组,"2000-01-01T03:08:06"
和 "2000-01-01T03:08:09"
之间的缺失值设置为 NaN。
print(ary[100:102])
# Non NaN values start from index 300 for new_ary
print(new_ary[486:490])
输出:
<xarray.DataArray (time: 2)>
array([0.25910861,0.07897777])
Coordinates:
* time (time) datetime64[ns] 2000-01-01T03:08:06 2000-01-01T03:08:09
<xarray.DataArray (time: 4)>
array([0.25910861,nan,0.07897777])
Coordinates:
* time (time) datetime64[ns] 2000-01-01T03:08:06 ... 2000-01-01T03:08:09
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