插值时间序列，从x中选择y值 [英] Interpolate time series, select y value from x
问题描述
一段时间以来，我一直在寻找答案，但虽然接近，但仍然遇到错误.有很多类似的问题几乎可以回答这个问题，但是我一直无法解决.任何帮助或朝着正确方向的观点表示赞赏.
I have been searching for an answer to this for a while, and have gotten close but keep running into errors. There are a lot of similar questions that almost answer this, but I haven't been able to solve it. Any help or a point in the right direction is appreciated.
我有一张图，显示温度是深度的主要非线性函数，其中x和y值取自熊猫数据框.
I have a graph showing temperature as a mostly non-linear function of depth, with the x and y values drawn from a pandas data frame.
import matplotlib.pyplot as plt
x = (22.81, 22.81, 22.78, 22.71, 22.55, 22.54, 22.51, 22.37)
y = (5, 16, 23, 34, 61, 68, 77, 86)
#Plot details
plt.figure(figsize=(10,7)), plt.plot(style='.-')
plt.title("Temperature as a Function of Depth")
plt.xlabel("Temperature"), plt.ylabel("Depth")
plt.gca().invert_yaxis()
plt.plot(x,y, linestyle='--', marker='o', color='b')
哪个给我一张像这样的图像(注意，因为我在谈论深度，所以请注意y轴的翻转):
Which gives me an image somewhat like this one (note the flipped y axis since I'm talking about depth):
我想在特定的x值22.61下找到y值，这不是数据集中的原始温度值之一.我尝试了以下步骤:
I would like to find the y value at a specific x value of 22.61, which is not one of the original temperature values in the dataset. I've tried the following steps:
np.interp(22.61, x1, y1)
哪一个给我一个我知道是不正确的值，
Which gives me a value that I know to be incorrect, as does
s = pd.Series([5,16,23,34,np.nan,61,68,77,86], index=[22.81,22.81,22.78,22.71,22.61,22.55,22.54,22.51,22.37])
s.interpolate(method='index')
我试图在其中设置框架并强制插值.我也尝试过
where I am trying to just set up a frame and force the interpolation. I also tried
line = plt.plot(x,y)
xvalues = line[0].get_xdata()
yvalues = line[0].get_ydata()
idx = np.where(xvalues==xvalues[3]) ## 3 is the position
yvalues[idx]
但是这将为已列出的特定x值返回y值，而不是内插值.
but this returns y values for a specific, already-listed x value, rather than an interpolated one.
我希望这足够清楚.我是数据科学和stackoverflow的新手，所以如果我需要改写这个问题，请告诉我.
I hope this is clear enough. I'm brand new to data science, and to stackoverflow, so if I need to rephrase the question please let me know.
推荐答案
You may indeed use the numpy.interp function. As the documentation states
数据点的x坐标必须增加[...]
The x-coordinates of the data points, must be increasing [...]
因此，在使用此功能之前，您需要对x数组上的数组进行排序.
So you need to sort the arrays on the x array, before using this function.
# Sort arrays
xs = np.sort(x)
ys = np.array(y)[np.argsort(x)]
# x coordinate
x0 = 22.61
# interpolated y coordinate
y0 = np.interp(x0, xs, ys)
完整的代码:
Complete Code:
import numpy as np
import matplotlib.pyplot as plt
x = (22.81, 22.81, 22.78, 22.71, 22.55, 22.54, 22.51, 22.37)
y = (5, 16, 23, 34, 61, 68, 77, 86)
# Sort arrays
xs = np.sort(x)
ys = np.array(y)[np.argsort(x)]
# x coordinate
x0 = 22.61
# interpolated y coordinate
y0 = np.interp(x0, xs, ys)
#Plot details
plt.figure(figsize=(10,7)), plt.plot(style='.-')
plt.title("Temperature as a Function of Depth")
plt.xlabel("Temperature"), plt.ylabel("Depth")
plt.gca().invert_yaxis()
plt.plot(x,y, linestyle='--', marker='o', color='b')
plt.plot(x0,y0, marker="o", color="C3")
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