我对一些非常基本的问题有疑问，但我已经花了一个小时试图找出问题所在，而且不能不，所以我在这里寻求帮助。
我有一个数组
_array([-0.44723986+0.j , -0.95965416+0.j ,
-0.45242139+0.27410111j, -0.45242139-0.27410111j,
-0.01292882+0.j ])
如果我综合运行以下列表：
mapping_1=np.array(
[
cmath.sqrt(
complex_num**2+1
) for complex_num in _.ravel()
]
)
mapping_1
一切都很好，我得到：
array([1.09545584+0.j , 1.3859784 +0.j ,
1.06911548-0.11599234j, 1.06911548+0.11599234j,
1.00008357+0.j ])
但是，如果我定义一个仅包含该列表的函数，则会出现一个巨大的错误：
功能定义
def complex_mapping(complex_array,curvature='+',number=1):
mapping_1=np.array(
[
cmath.sqrt(
complex_num**2+number if curvature=='+' else complex_num**2-number
) for complex_num in complex_array
]
)
return(mapping_1)
代码行：
complex_mapping(_.ravel())我明白了：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[87], line 1
----> 1 complex_mapping(_.ravel())
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2328, in vectorize.__call__(self, *args, **kwargs)
2325 vargs = [args[_i] for _i in inds]
2326 vargs.extend([kwargs[_n] for _n in names])
-> 2328 return self._vectorize_call(func=func, args=vargs)
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2406, in vectorize._vectorize_call(self, func, args)
2404 res = func()
2405 else:
-> 2406 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
2408 # Convert args to object arrays first
2409 inputs = [asanyarray(a, dtype=object) for a in args]
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2366, in vectorize._get_ufunc_and_otypes(self, func, args)
2362 raise ValueError('cannot call `vectorize` on size 0 inputs '
2363 'unless `otypes` is set')
2365 inputs = [arg.flat[0] for arg in args]
-> 2366 outputs = func(*inputs)
2368 # Performance note: profiling indicates that -- for simple
2369 # functions at least -- this wrapping can almost double the
2370 # execution time.
2371 # Hence we make it optional.
2372 if self.cache:
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2328, in vectorize.__call__(self, *args, **kwargs)
2325 vargs = [args[_i] for _i in inds]
2326 vargs.extend([kwargs[_n] for _n in names])
-> 2328 return self._vectorize_call(func=func, args=vargs)
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2406, in vectorize._vectorize_call(self, func, args)
2404 res = func()
2405 else:
-> 2406 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
2408 # Convert args to object arrays first
2409 inputs = [asanyarray(a, dtype=object) for a in args]
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2366, in vectorize._get_ufunc_and_otypes(self, func, args)
2362 raise ValueError('cannot call `vectorize` on size 0 inputs '
2363 'unless `otypes` is set')
2365 inputs = [arg.flat[0] for arg in args]
-> 2366 outputs = func(*inputs)
2368 # Performance note: profiling indicates that -- for simple
2369 # functions at least -- this wrapping can almost double the
2370 # execution time.
2371 # Hence we make it optional.
2372 if self.cache:
[... skipping similar frames: vectorize.__call__ at line 2328 (3 times), vectorize._get_ufunc_and_otypes at line 2366 (3 times), vectorize._vectorize_call at line 2406 (3 times)]
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2328, in vectorize.__call__(self, *args, **kwargs)
2325 vargs = [args[_i] for _i in inds]
2326 vargs.extend([kwargs[_n] for _n in names])
-> 2328 return self._vectorize_call(func=func, args=vargs)
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2406, in vectorize._vectorize_call(self, func, args)
2404 res = func()
2405 else:
-> 2406 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
2408 # Convert args to object arrays first
2409 inputs = [asanyarray(a, dtype=object) for a in args]
File D:\Python\Lib\site-packages\numpy\lib\function_base.py:2366, in vectorize._get_ufunc_and_otypes(self, func, args)
2362 raise ValueError('cannot call `vectorize` on size 0 inputs '
2363 'unless `otypes` is set')
2365 inputs = [arg.flat[0] for arg in args]
-> 2366 outputs = func(*inputs)
2368 # Performance note: profiling indicates that -- for simple
2369 # functions at least -- this wrapping can almost double the
2370 # execution time.
2371 # Hence we make it optional.
2372 if self.cache:
Cell In[5], line 14, in complex_mapping(complex_array, curvature, number)
12 def complex_mapping(complex_array,curvature='+',number=1):
13 mapping_1=np.array(
---> 14 [
15 cmath.sqrt(
16 complex_num**2+number if curvature=='+' else complex_num**2-number
17 ) for complex_num in complex_array
18 ]
19 )
20 return(mapping_1)
TypeError: 'numpy.complex128' object is not iterable
我希望通过许多数组运行此函数，这就是我创建该函数但它不起作用的原因。
我编写的代码我认为问题可能在于您传递的不是数组而是单个元素，因此编写了相同的检查。
import numpy as np
import cmath
def complex_mapping(complex_array, curvature='+', number=1):
if isinstance(complex_array, np.ndarray):
mapping_1 = np.array([cmath.sqrt(complex_num**2 + number if curvature=='+' else complex_num**2 - number)
for complex_num in complex_array])
return mapping_1
else:
raise TypeError("Input must be an array of complex numbers")
complex_array = np.array([-0.44723986+0.j, -0.95965416+0.j, -0.45242139+0.27410111j, -0.45242139-0.27410111j, -0.01292882+0.j])
result = complex_mapping(complex_array)
print(result)
输出：
array([1.09545584+0.j , 1.38597839+0.j ,
1.06911548-0.11599234j, 1.06911548+0.11599234j,
1.00008357+0.j ])
希望这能解决问题。