Python导入txt表文件中的等高线图 [英] Contour plot in Python importing txt table file
问题描述
我正在尝试绘制等高线图，
I am trying to make a contour plot like:
使用一个txt文件中的数据表(如3列)，行数长.
Using a table of data like 3 columns in a txt file, with a long number of lines.
使用此代码:
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
data = np.loadtxt(r'dataa.txt')
a = [data[:,0]]
b = [data[:,1]]
n = [data[:,2]]
x = np.asarray(a)
y = np.asarray(b)
z = np.asarray(n)
print "x = ", x
print "y = ", y
print "z = ", z
fig=plt.figure()
CF = contour(x,y,z,colors = 'k')
plt.xlabel("X")
plt.ylabel("Y")
plt.colorbar()
plt.show()
我不知道为什么，它不起作用.Python 为我提供了我期望看到的值的正确轴，但图中只是一个空白，我知道它正在以正确的方式导入数据，因为它在绘图之前向我显示了我的值.
I don't know why, it is not working. Python gives me the right axes for the values that I am expecting to see, but in the graph is just a blank and I know that it is importing the data in right way because it shows me my values before the plot.
表示例:(不同之处在于我的表有90000行)
Example of table: (the diference is because my table has 90000 lines)
使用此代码:
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
N = 1000 #number of points for plotting/interpolation
x, y, z = np.genfromtxt(r'dataa.txt', unpack=True)
xi = np.linspace(x.min(), x.max(), N)
yi = np.linspace(y.min(), y.max(), N)
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic')
fig = plt.figure()
plt.contour(xi, yi, zi)
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
我得到了这个结果:我想我得到的建议是错误的.
Ive got this result: I think I've got the advices wrongly.
推荐答案
我的评论后续...首先，我将替换所有这些行:
Followup from my comment... first, I would replace all these lines:
data = np.loadtxt(r'dataa.txt')
a = [data[:,0]]
b = [data[:,1]]
n = [data[:,2]]
x = np.asarray(a)
y = np.asarray(b)
z = np.asarray(n)
与:
x, y, z = np.genfromtxt(r'dataa.txt', unpack=True)
您的原始代码在前面添加了一条额外的轴，因为 [data [:，0]] 是具有一个元素的数组的列表.结果是 x.shape 将是 (1, N) 而不是 (N,).所有这些都可以使用上面的最后一行自动完成，或者您可以使用相同的 data 加载并说:
Your original code is adding an extra axis at the front, since [data[:,0]] is a list of arrays with one element. The result is that x.shape will be (1, N) instead if (N,). All of this can be done automatically using the last line above, or you could just use the same data loading and say:
x = data[:,0]
y = data[:,1]
z = data[:,2]
因为这些切片将为您提供阵列.
since those slices will give you an array back.
然而，您还没有完全完成，因为 plt.contour 期望您为 z 提供一个二维数组，而不是一个一维值数组.现在，您似乎在给定的 x，y 点处具有 z 值，但是 contour 希望您为其提供一个2d数组，例如图像.
However, you're not quite done, because plt.contour expects you to give it a 2d array for z, not a 1d array of values. Right now, you seem to have z values at given x, y points, but contour expects you to give it a 2d array, like an image.
在我回答之前，我需要知道 x 和 y 是如何间隔的.如果定期，你可以很容易地填充一个数组.如果不规律，基本上要插值才能做出等高线图.
Before I can answer that, I need to know how x and y are spaced. If regularly, you can just populate an array pretty easily. If not regularly, you basically have to interpolate before you can make a contour plot.
要进行插值，请使用
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
N = 1000 #number of points for plotting/interpolation
x, y, z = np.genfromtxt(r'dataa.txt', unpack=True)
xi = np.linspace(x.min(), x.max(), N)
yi = np.linspace(y.min(), y.max(), N)
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic')
fig = plt.figure()
plt.contour(xi, yi, zi)
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
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