当 x 和 y 形状相同时，为什么我得到 'x and y must have same first dimension, but have shapes (1,) and (319,)'？

Question

我正在使用 lmfit 包对我的数据拟合双指数函数。我首先从 CSV 文件中获取 x 和 y 坐标值并绘制它们。它工作正常。其次，我正在定义一个 double_exponential 函数，并使用 lmfit 包，我正在根据我的数据绘制 double_exponential 函数。我已经打印出我的 x 和 y 值的长度 (length=319) 及其形状以查看它们是否匹配 (319,)。他们是这样！。但我仍然收到 'x and y must have the same first dimension, but have shapes (1,) and (319,) ' 错误。我附上了下面的代码。解决此问题的任何建议都会有所帮助。

xData=[]
yData=[]
for file in files:
        basename = os.path.basename(file)
        file_name = os.path.splitext(basename)[0]
        # print(file_name)
        #File_List.append(file_name)

        with open(file, "r") as f_in:
            reader = csv.reader(f_in)
            next(reader)
            next(reader)
            for line in reader:
                try:
                    float_1, float_2 = float(line[0]), float(line[1])
                    xData=np.append(xData,float_1)
                    yData=np.append(yData,float_2)                  
                except ValueError:
                    continue
        xData = np.array(xData)
        yData = np.array(yData)
        plt.plot(xData,yData)
        plt.show()
        def double_exp(a,t,T):
            return a*np.exp(-t/T)
        print(xData.shape)
        print(yData.shape)        
        mod = Model(double_exp)
        result = mod.fit(yData,x=xData, a=1, t=1, T=1)
        result.plot()            
        xData=[]
        yData=[]

我的回溯错误是：

(319,)
(319,)

C:\ANACONDA\lib\site-packages\lmfit\model.py:968: UserWarning: The keyword argument x does not match any arguments of the model function. It will be ignored.
  warnings.warn("The keyword argument %s does not " % name +
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-29-085a1a8ec72a> in <module>
     35         mod = Model(double_exp)
     36         result = mod.fit(yData,x=xData, a=1, t=1, T=1)
---> 37         result.plot()
     38 
     39 

C:\ANACONDA\lib\site-packages\lmfit\model.py in wrapper(*args, **kws)
     48         @wraps(function)
     49         def wrapper(*args, **kws):
---> 50             return function(*args, **kws)
     51         return wrapper
     52 

C:\ANACONDA\lib\site-packages\lmfit\model.py in plot(self, datafmt, fitfmt, initfmt, xlabel, ylabel, yerr, numpoints, fig, data_kws, fit_kws, init_kws, ax_res_kws, ax_fit_kws, fig_kws, show_init, parse_complex)
   2115         ax_fit = fig.add_subplot(gs[1], sharex=ax_res, **ax_fit_kws)
   2116 
-> 2117         self.plot_fit(ax=ax_fit, datafmt=datafmt, fitfmt=fitfmt, yerr=yerr,
   2118                       initfmt=initfmt, xlabel=xlabel, ylabel=ylabel,
   2119                       numpoints=numpoints, data_kws=data_kws,

C:\ANACONDA\lib\site-packages\lmfit\model.py in wrapper(*args, **kws)
     48         @wraps(function)
     49         def wrapper(*args, **kws):
---> 50             return function(*args, **kws)
     51         return wrapper
     52 

C:\ANACONDA\lib\site-packages\lmfit\model.py in plot_fit(self, ax, datafmt, fitfmt, initfmt, xlabel, ylabel, yerr, numpoints, data_kws, fit_kws, init_kws, ax_kws, show_init, parse_complex)
   1890                         fmt=datafmt, label='data', **data_kws)
   1891         else:
-> 1892             ax.plot(x_array, reduce_complex(self.data),
   1893                     datafmt, label='data', **data_kws)
   1894 

C:\ANACONDA\lib\site-packages\matplotlib\axes\_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
   1741         """
   1742         kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1743         lines = [*self._get_lines(*args, data=data, **kwargs)]
   1744         for line in lines:
   1745             self.add_line(line)

C:\ANACONDA\lib\site-packages\matplotlib\axes\_base.py in __call__(self, data, *args, **kwargs)
    271                 this += args[0],
    272                 args = args[1:]
--> 273             yield from self._plot_args(this, kwargs)
    274 
    275     def get_next_color(self):

C:\ANACONDA\lib\site-packages\matplotlib\axes\_base.py in _plot_args(self, tup, kwargs)
    397 
    398         if x.shape[0] != y.shape[0]:
--> 399             raise ValueError(f"x and y must have same first dimension, but "
    400                              f"have shapes {x.shape} and {y.shape}")
    401         if x.ndim > 2 or y.ndim > 2:

ValueError: x and y must have same first dimension, but have shapes (1,) and (319,)

Answer 1

错误消息会准确告诉您问题所在：

UserWarning: The keyword argument x does not match any arguments of the model function. It will be ignored.

您向 fit 方法提供了 x=xData 的值，但您的函数 doubl_exp 不包含此变量。在这里，正如您提出的关于 lmfit 的其他各种问题，最好的建议是：阅读文档并查看网站上提供的示例。他们可能会涵盖 >95% 的用例，然后阅读错误消息 Python 给你的总是有帮助的 ;)

当 x 和 y 形状相同时，为什么我得到 'x and y must have same first dimension, but have shapes (1,) and (319,)'？

Why am I getting 'x and y must have same first dimension, but have shapes (1,) and (319,)' when the x and y shapes are same?

python

arrays

matplotlib

curve-fitting

lmfit