numpy.savetxt fmt='%f'，将连接的数组附加到列表中

Question

我在一个类中有一个函数，它在一个 while 循环中被调用。 此函数获取 2 个 6x6 矩阵并返回一个按 np.linspace 中指定的维度放大的向量，即 6x91。我想通过将这三个数组连接为浮点数将它们存储在一个文件中（使用 savetxt）。下面是一个与类分开运行的函数示例，它运行良好。 当我在循环之前声明列表输出并通过 Tkinter 窗口更新 EulArr 时，问题就出现了，将数组附加到列表中。我只能用 %s 格式保存数组，但不能再用 %f 保存。为什么？

LegForces(self, Li, ti)

import numpy as np
from math import pi
M=100.   #total estimated mass to move (upper platform+upper legs+payload)
g=9.81
nsteps= 91
Li=np.matrix([[-10.64569774, -93.1416122, 116.35191853],\
  [-10.68368329, 93.17236065, 116.35542498],\
  [85.985087,37.35196994, 116.20350534],[-75.34703551,-55.83790049, 115.44528196],\
  [-75.33938926, 55.78964226, 115.44457613],[86.0307188,-37.33446016, 116.19929305]])
ti=np.matrix([[88.15843159,88.04450508,50.10006323, -138.28903445, -138.26610178,50.2369224],\
 [-108.75675186,  108.84408749,  130.72504635, 21.82594871, -21.97569549,-130.6774372],\
 [ 119.40585161, 119.40170883,  119.23577854, 118.41560138, 118.41643529,119.24075525]])
ti=ti.T  #transpose the ti for compatible format
x_cm= 1.
y_cm= -1.
z_cm=87.752  
z=np.linspace(0,90,nsteps)  
Fx=-M*g*np.cos(pi*z/180)
Fy= M*g*np.sin(pi*z/180)
Fz=50 # including braking forces [N]
#specify here the centre of mass coordinates  retrieved from Inventor
Mx=Fz*y_cm-Fy*z_cm
My=-Fz*x_cm+Fx*z_cm
Mz=Fy*x_cm-Fx*y_cm
mc=np.zeros((6,3),'float')
ex_forces=np.array([Fx,Fy,Fz,Mx,My,Mz])
for i in range(6):
   mc[i,:]=np.cross(ti[i,:],Li[i,:])
r1=[Li[0,0],Li[1,0],Li[2,0],Li[3,0],Li[4,0],Li[5,0]]
r2=[Li[0,1],Li[1,1],Li[2,1],Li[3,1],Li[4,1],Li[5,1]]
r3=[Li[0,2],Li[1,2],Li[2,2],Li[3,2],Li[4,2],Li[5,2]]
r4=[mc[0,0],mc[1,0],mc[2,0],mc[3,0],mc[4,0],mc[5,0]]
r5=[mc[0,1],mc[1,1],mc[2,1],mc[3,1],mc[4,1],mc[5,1]]
r6=[mc[0,2],mc[1,2],mc[2,2],mc[3,2],mc[4,2],mc[5,2]]
DMatrix=np.vstack([r1,r2,r3,r4,r5,r6])
print   'DMatrix form:\n', DMatrix
invD=np.linalg.inv(DMatrix)
print ' inv(DMatrix) is:\n', invD
legF=np.dot(ex_forces,invD)
#slice it!
legF=legF.tolist()
a,b=np.shape(legF)
print 'check identity matrix:\n', np.dot(invD,DMatrix)
print 'leg forces:\n',legF, type(legF)
newlegF=np.reshape(legF,(1,a*b))
strokeValues= np.array([[-0.3595, .1450483, -0.3131,0.4210,-0.0825,.19124]])
print 'strokeValues shape:\n', np.shape(strokeValues)
print 'leg forces vector shape:', np.shape(newlegF)
EulArr=np.array([[0.12,0.2,0,-3.,-1.,15.]]) 
output=np.concatenate((strokeValues,EulArr,newlegF),axis=1)
np.savetxt('leg_forces.dat', output,fmt=' %f')
print output ,np.shape(output)

该类将如下所示：

class Hexapod(self,....):
   output=[]
   while 1:
      ...
      LegAxialF=self.LegForces(Li,ti)
      #create a list for EUl parameters which can be refreshed   
      EulArr=np.zeros((6,1),'float')
      EulArr[0]=180/pi*self.EulPhi
      EulArr[1]=180/pi*self.EulTheta
      EulArr[2]=180/pi*self.EulPsi
      EulArr[3]=self.EulX
      EulArr[4]=self.EulY
      EulArr[5]=self.EulZ-self.height 
      #print meaningful values to the specified file  
      EulArr=np.reshape(EulArr,(1,6))
      EulArrList=EulArr.tolist()
      strokes=np.reshape(strokeValues,(1,6))
      strokeList=strokes.tolist()
      output.append(np.concatenate((strokeList,\
         EulArrList,LegFList),axis=1))
      np.savetxt('act_lengths.dat', output, fmt='%f')  





def LegForces(self, Li, ti):

我收到以下错误：

   np.savetxt('act_lengths.dat', output, fmt='%f')
  File "C:\Python27\lib\site-packages\numpy\lib\npyio.py", line 1047, in savetxt
    fh.write(asbytes(format % tuple(row) + newline))
TypeError: float argument required, not numpy.ndarray

Answer 1

输出是一个包含ndarrays 的list。我认为在您的代码中，output 的元素必须是不同大小的ndarrays，而internally NumPy 由于数据参差不齐而无法将其转换为浮点数组，而是创建了一个objects 数组*。我可以重现您的错误

>>> output = [np.random.rand(3), np.random.rand(4)]
>>> np.savetxt("test", output, fmt='%f')
Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "/Users/yotam/anaconda/lib/python2.7/site-packages/numpy/lib/npyio.py", line 1083, in savetxt
    fh.write(asbytes(format % tuple(row) + newline))
TypeError: float argument required, not numpy.ndarray
>>>

而如果我改用 output = [np.random.rand(3), np.random.rand(3)] 它就可以了。

(*) 即你得到

>>> np.asarray(output)
array([array([ 0.87346791,  0.10046296,  0.60304887]),
       array([ 0.25116526,  0.29174373,  0.26067348,  0.68317986])], dtype=object)