I am trying to fit a linear line of best fit to my matplotlib graph. I keep getting the error that x and y do not have the same first dimension. But they both have lengths of 15. What am I doing wrong?
我试图在我的matplotlib图中拟合最适合的线性线。我不断得到x和y没有相同的第一个维度的错误。但他们都有15个长度。我做错了什么?
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = [0.46,0.59,0.68,0.99,0.39,0.31,1.09,0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78]
y = [0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,0.478,0.335,0.365,0.424,0.390,0.585,0.511]
xerr = [0.01]*15
yerr = [0.001]*15
plt.rc('font', family='serif', size=13)
m, b = np.polyfit(x, y, 1)
plt.plot(x,y,'s',color='#0066FF')
plt.plot(x, m*x + b, 'r-') #BREAKS ON THIS LINE
plt.errorbar(x,y,xerr=xerr,yerr=0,linestyle="None",color='black')
plt.xlabel('$\Delta t$ $(s)$',fontsize=20)
plt.ylabel('$\Delta p$ $(hPa)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=False)
plt.grid(False)
plt.xlim(0.2,1.2)
plt.ylim(0,0.8)
plt.show()
2 个解决方案
#1
32
You should make x and y numpy arrays, not lists:
你应该制作x和y numpy数组,而不是列表:
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,
0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78])
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,
0.478,0.335,0.365,0.424,0.390,0.585,0.511])
With this change, it produces the expect plot. If they are lists, m * x will not produce the result you expect, but an empty list. Note that m is anumpy.float64 scalar, not a standard Python float.
通过这种变化,它可以产生预期的情节。如果它们是列表,则m * x将不会产生您期望的结果,而是一个空列表。请注意,m是anumpy.float64标量,而不是标准的Python浮点数。
I actually consider this a bit dubious behavior of Numpy. In normal Python, multiplying a list with an integer just repeats the list:
我实际上认为这是Numpy有点可疑的行为。在普通的Python中,将列表与整数相乘只重复列表:
In [42]: 2 * [1, 2, 3]
Out[42]: [1, 2, 3, 1, 2, 3]
while multiplying a list with a float gives an error (as I think it should):
将列表与float相乘会产生错误(我认为应该如此):
In [43]: 1.5 * [1, 2, 3]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-d710bb467cdd> in <module>()
----> 1 1.5 * [1, 2, 3]
TypeError: can't multiply sequence by non-int of type 'float'
The weird thing is that multiplying a Python list with a Numpy scalar apparently works:
奇怪的是,将Python列表与Numpy标量相乘显然有效:
In [45]: np.float64(0.5) * [1, 2, 3]
Out[45]: []
In [46]: np.float64(1.5) * [1, 2, 3]
Out[46]: [1, 2, 3]
In [47]: np.float64(2.5) * [1, 2, 3]
Out[47]: [1, 2, 3, 1, 2, 3]
So it seems that the float gets truncated to an int, after which you get the standard Python behavior of repeating the list, which is quite unexpected behavior. The best thing would have been to raise an error (so that you would have spotted the problem yourself instead of having to ask your question on *) or to just show the expected element-wise multiplication (in which your code would have just worked). Interestingly, addition between a list and a Numpy scalar does work:
所以似乎float被截断为一个int,之后你得到重复列表的标准Python行为,这是非常意外的行为。最好的做法是提出一个错误(这样你就可以自己发现问题,而不必在*上提出你的问题)或者只是显示预期的逐元素乘法(你的代码将在其中工作) 。有趣的是,列表和Numpy标量之间的添加确实有效:
In [69]: np.float64(0.123) + [1, 2, 3]
Out[69]: array([ 1.123, 2.123, 3.123])
#2
6
Changing your lists to numpy arrays will do the job!!
将列表更改为numpy数组将完成工作!
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78]) # x is a numpy array now
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,0.478,0.335,0.365,0.424,0.390,0.585,0.511]) # y is a numpy array now
xerr = [0.01]*15
yerr = [0.001]*15
plt.rc('font', family='serif', size=13)
m, b = np.polyfit(x, y, 1)
plt.plot(x,y,'s',color='#0066FF')
plt.plot(x, m*x + b, 'r-') #BREAKS ON THIS LINE
plt.errorbar(x,y,xerr=xerr,yerr=0,linestyle="None",color='black')
plt.xlabel('$\Delta t$ $(s)$',fontsize=20)
plt.ylabel('$\Delta p$ $(hPa)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=False)
plt.grid(False)
plt.xlim(0.2,1.2)
plt.ylim(0,0.8)
plt.show()
#1
32
You should make x and y numpy arrays, not lists:
你应该制作x和y numpy数组,而不是列表:
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,
0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78])
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,
0.478,0.335,0.365,0.424,0.390,0.585,0.511])
With this change, it produces the expect plot. If they are lists, m * x will not produce the result you expect, but an empty list. Note that m is anumpy.float64 scalar, not a standard Python float.
通过这种变化,它可以产生预期的情节。如果它们是列表,则m * x将不会产生您期望的结果,而是一个空列表。请注意,m是anumpy.float64标量,而不是标准的Python浮点数。
I actually consider this a bit dubious behavior of Numpy. In normal Python, multiplying a list with an integer just repeats the list:
我实际上认为这是Numpy有点可疑的行为。在普通的Python中,将列表与整数相乘只重复列表:
In [42]: 2 * [1, 2, 3]
Out[42]: [1, 2, 3, 1, 2, 3]
while multiplying a list with a float gives an error (as I think it should):
将列表与float相乘会产生错误(我认为应该如此):
In [43]: 1.5 * [1, 2, 3]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-d710bb467cdd> in <module>()
----> 1 1.5 * [1, 2, 3]
TypeError: can't multiply sequence by non-int of type 'float'
The weird thing is that multiplying a Python list with a Numpy scalar apparently works:
奇怪的是,将Python列表与Numpy标量相乘显然有效:
In [45]: np.float64(0.5) * [1, 2, 3]
Out[45]: []
In [46]: np.float64(1.5) * [1, 2, 3]
Out[46]: [1, 2, 3]
In [47]: np.float64(2.5) * [1, 2, 3]
Out[47]: [1, 2, 3, 1, 2, 3]
So it seems that the float gets truncated to an int, after which you get the standard Python behavior of repeating the list, which is quite unexpected behavior. The best thing would have been to raise an error (so that you would have spotted the problem yourself instead of having to ask your question on *) or to just show the expected element-wise multiplication (in which your code would have just worked). Interestingly, addition between a list and a Numpy scalar does work:
所以似乎float被截断为一个int,之后你得到重复列表的标准Python行为,这是非常意外的行为。最好的做法是提出一个错误(这样你就可以自己发现问题,而不必在*上提出你的问题)或者只是显示预期的逐元素乘法(你的代码将在其中工作) 。有趣的是,列表和Numpy标量之间的添加确实有效:
In [69]: np.float64(0.123) + [1, 2, 3]
Out[69]: array([ 1.123, 2.123, 3.123])
#2
6
Changing your lists to numpy arrays will do the job!!
将列表更改为numpy数组将完成工作!
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78]) # x is a numpy array now
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,0.478,0.335,0.365,0.424,0.390,0.585,0.511]) # y is a numpy array now
xerr = [0.01]*15
yerr = [0.001]*15
plt.rc('font', family='serif', size=13)
m, b = np.polyfit(x, y, 1)
plt.plot(x,y,'s',color='#0066FF')
plt.plot(x, m*x + b, 'r-') #BREAKS ON THIS LINE
plt.errorbar(x,y,xerr=xerr,yerr=0,linestyle="None",color='black')
plt.xlabel('$\Delta t$ $(s)$',fontsize=20)
plt.ylabel('$\Delta p$ $(hPa)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=False)
plt.grid(False)
plt.xlim(0.2,1.2)
plt.ylim(0,0.8)
plt.show()