使用scipy.interpn插值时出现维度0 xi越界的ValueError求助

尝试将48×86的时间×高度风速网格插值为71×81的目标网格，使用scipy.interpolate.interpn时出现错误：

ValueError: One of the requested xi is out of bounds in dimension 0

原代码 #

目标插值网格

rangeTime = np.arange(np.datetime64('2018-05-30T00:20:00'),np.datetime64('2018-05-31T00:00:00'),np.timedelta64(20,'m'))
rangeHeight = np.arange(200,4250,50)
rangeTime = rangeTime.astype("float64")
inTime, inHeight = np.meshgrid(rangeTime,rangeHeight,indexing='ij')

待插值数据

times= ds.time.values #1d time array
heightvalues = ds.heights.values #48 x 86 grid with height data
height1d = heightvalues[0,:] #only need 1st slice, so I get 1d array with all my heights.
wspeed = ds.wspd.values #wind speed data, 48 x 86 grid

插值调用

grid_wspeed = interpn((times,height1d),wspeed,(inTime,inHeight), method = 'linear')

错误原因 #

这个错误明确说明目标网格的时间点（维度0）超出了原始数据的时间范围，同时存在一个潜在问题：

1. 目标rangeTime被转为float64，但原始times如果仍为datetime64类型，两者数值基准不一致，会导致范围判断出错。
2. 目标时间序列rangeTime的起止可能超出了原始times的最小/最大值。

解决方法 #

1. 统一时间数据类型

将原始times也转为float64，和目标网格保持一致的数值格式：

times= ds.time.values.astype("float64")

2. 验证并修正范围

先打印原始与目标的时间、高度范围，确认是否存在越界：

# 检查时间范围
print(f"原始时间范围: [{times.min()}, {times.max()}]")
print(f"目标时间范围: [{rangeTime.min()}, {rangeTime.max()}]")

# 检查高度范围
print(f"原始高度范围: [{height1d.min()}, {height1d.max()}]")
print(f"目标高度范围: [{rangeHeight.min()}, {rangeHeight.max()}]")

如果目标范围超出原始范围，可选择两种处理方式：

• 截断目标范围：将超出部分裁剪到原始范围内

  rangeTime = np.clip(rangeTime, times.min(), times.max())
  rangeHeight = np.clip(rangeHeight, height1d.min(), height1d.max())
  inTime, inHeight = np.meshgrid(rangeTime, rangeHeight, indexing='ij')
  

• 允许越界并填充值：在interpn中关闭边界检查，用指定值填充越界区域（比如np.nan）

  grid_wspeed = interpn(
      (times, height1d), 
      wspeed, 
      (inTime, inHeight), 
      method='linear',
      bounds_error=False,
      fill_value=np.nan
  )
  

3. 完整修正后的代码

# 目标插值网格代码
rangeTime = np.arange(np.datetime64('2018-05-30T00:20:00'),np.datetime64('2018-05-31T00:00:00'),np.timedelta64(20,'m'))
rangeHeight = np.arange(200,4250,50)
rangeTime = rangeTime.astype("float64")

# 待插值数据相关代码
times= ds.time.values.astype("float64")  # 统一类型
heightvalues = ds.heights.values
height1d = heightvalues[0,:]
wspeed = ds.wspd.values

# 可选：裁剪目标范围到原始数据范围内
rangeTime = np.clip(rangeTime, times.min(), times.max())
rangeHeight = np.clip(rangeHeight, height1d.min(), height1d.max())

inTime, inHeight = np.meshgrid(rangeTime, rangeHeight, indexing='ij')

# 调用interpn
grid_wspeed = interpn((times, height1d), wspeed, (inTime, inHeight), method='linear')

内容的提问来源于stack exchange，提问作者DLW9900