Expect x to not have duplicates’
时间: 2023-05-18 14:04:56 浏览: 1000
这是一个编程类的问题,它意味着我们期望变量 x 不包含重复的元素。为了实现这个目标,我们可以使用一些数据结构,比如哈希表或集合,来检查 x 中是否有重复的元素。具体实现方法取决于编程语言和具体的应用场景。
相关问题
ValueError Traceback (most recent call last) Cell In[36], line 31 28 a11 = (B2[0, 2] - B2[0, 1]) / 500 29 for i2 in np.arange(B2[0, 1], B2[0, 2] + a11, a11): ---> 31 f = interp1d(B2[0, :], B2[1, :], kind='quadratic') 32 a12 = f(i2) 33 a13 = a12 File ~\AppData\Roaming\Python\Python39\site-packages\scipy\interpolate\_interpolate.py:616, in interp1d.__init__(***failed resolving arguments***) 613 yy = np.ones_like(self._y) 614 rewrite_nan = True --> 616 self._spline = make_interp_spline(xx, yy, k=order, 617 check_finite=False) 618 if rewrite_nan: 619 self._call = self.__class__._call_nan_spline File ~\AppData\Roaming\Python\Python39\site-packages\scipy\interpolate\_bsplines.py:1293, in make_interp_spline(x, y, k, t, bc_type, axis, check_finite) 1290 raise ValueError('Shapes of x {} and y {} are incompatible' 1291 .format(x.shape, y.shape)) 1292 if np.any(x[1:] == x[:-1]): -> 1293 raise ValueError("Expect x to not have duplicates") 1294 if x.ndim != 1 or np.any(x[1:] < x[:-1]): 1295 raise ValueError("Expect x to be a 1D strictly increasing sequence.") ValueError: Expect x to not have duplicates怎么修改,写出代码
这个错误是由于输入的 x 数组中有重复值导致的。可以通过删除重复值来解决这个问题。可以使用 `np.unique` 函数来删除 x 中的重复项,然后使用删除重复项后的数组来进行插值。
以下是修改后的代码:
```
import numpy as np
from scipy.interpolate import interp1d
# 原始数据
B2 = np.array([[0, 100, 200, 300, 400, 500], [0.3, 0.5, 0.7, 1.0, 1.3, 1.5]])
# 删除 x 中的重复项
x = np.unique(B2[0, :])
y = B2[1, :]
# 进行插值
f = interp1d(x, y, kind='quadratic')
# 在新的 x 范围内进行插值
a11 = (x[-1] - x[1]) / 500
for i2 in np.arange(x[1], x[-1] + a11, a11):
a12 = f(i2)
a13 = a12
```
ValueError Traceback (most recent call last) Cell In[24], line 71 67 B3[a2 - 1, 9] = np.min(B6[0, :]) 68 B3[a2 - 1, 10] = np.max(B6[0, :]) ---> 71 f = interp1d(B6[0, :], B6[1, :], kind='quadratic') 72 xx = np.array([0, 0.03, 0.09, 0.18, 0.3, 0.45, 0.63, 0.84, 1.0]) 73 yy = f(xx) File ~\AppData\Roaming\Python\Python39\site-packages\scipy\interpolate\_interpolate.py:616, in interp1d.__init__(***failed resolving arguments***) 613 yy = np.ones_like(self._y) 614 rewrite_nan = True --> 616 self._spline = make_interp_spline(xx, yy, k=order, 617 check_finite=False) 618 if rewrite_nan: 619 self._call = self.__class__._call_nan_spline File ~\AppData\Roaming\Python\Python39\site-packages\scipy\interpolate\_bsplines.py:1293, in make_interp_spline(x, y, k, t, bc_type, axis, check_finite) 1290 raise ValueError('Shapes of x {} and y {} are incompatible' 1291 .format(x.shape, y.shape)) 1292 if np.any(x[1:] == x[:-1]): -> 1293 raise ValueError("Expect x to not have duplicates") 1294 if x.ndim != 1 or np.any(x[1:] < x[:-1]): 1295 raise ValueError("Expect x to be a 1D strictly increasing sequence.") ValueError: Expect x to not have duplicates修改方法写出代码
这个错误是因为输入的插值点存在重复值导致的,可以通过使用np.unique函数将其中一个维度去重来解决,代码如下:
```python
unique_x, index = np.unique(B6[0, :], return_index=True)
B6 = np.array([unique_x, B6[1, :][index]])
f = interp1d(B6[0, :], B6[1, :], kind='quadratic')
xx = np.array([0, 0.03, 0.09, 0.18, 0.3, 0.45, 0.63, 0.84, 1.0])
yy = f(xx)
```
其中,`np.unique(B6[0, :], return_index=True)`返回了去重后的x轴数据和去重后的数据在原数组中的下标,然后通过`B6 = np.array([unique_x, B6[1, :][index]])`将去重后的数据重新组合成数组。