python中的interp函数就像matlab一样

Could someone explain how to use an interpolation function on an existing array, in python as the one exists in matlab ?

有人可以解释如何在现有数组中使用插值函数，在python中如matlab中存在的那样？

example:

例：

x =

interp(x,2)

interp的（X，2）

ans =

I'd like a function in python which does exactly this, i.e. adds more points keeping the original intact.

我想在python中使用一个函数来完成这个，即添加更多的点来保持原始的完整。

2 个解决方案

#1

A couple of issues need to be raised:

需要提出几个问题：

Are you just looking at linear interpolation (ie "connect the dots" with straight line segments)? That's simple but kind of nasty. You can get nicer results with higher-order curves (ie bicubic splines) but for that you need to provide more information to nail down a unique solution (ie endpoint first-derivatives).

你只是看线性插值（即用直线段“连接点”）？这很简单但很讨厌。您可以使用高阶曲线（即双三次样条曲线）获得更好的结果，但为此您需要提供更多信息来确定唯一解决方案（即端点一阶导数）。
Do you want to smooth the curve at the same time, or do you expect it to go exactly through your given points?

您是否希望同时平滑曲线，或者您是否希望它完全通过您的给定点？
Are your input points uniformly spaced (ie along the x axis)?

您的输入点是均匀间隔的（即沿x轴）？
Your data shows not just interpolation but also extrapolation (ie your last point is off the end of your data) - is this actually what you want?

您的数据不仅显示插值，还显示外推（即您的最后一点不在数据末尾） - 这实际上是您想要的吗？

Matlab documentation says "interp inserts 0s into the original signal and then applies a lowpass interpolating filter to the expanded sequence".

Matlab文档说“interp将0插入原始信号，然后将低通内插滤波器应用于扩展序列”。

Edit: I think the closest equivalent is scipy.interpolate.interp1d - see http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d

编辑：我认为最接近的等价物是scipy.interpolate.interp1d - 请参阅http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d

You could make a wrapper like so:

你可以像这样制作一个包装器：

import numpy as np
from scipy.interpolate import interp1d

def interp(ys, mul):
    # linear extrapolation for last (mul - 1) points
    ys = list(ys)
    ys.append(2*ys[-1] - ys[-2])
    # make interpolation function
    xs = np.arange(len(ys))
    fn = interp1d(xs, ys, kind="cubic")
    # call it on desired data points
    new_xs = np.arange(len(ys) - 1, step=1./mul)
    return fn(new_xs)

which then works like

然后工作就像

>>> interp([1,2,3,4,5,6,7,8,9,10], 2)
array([  1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,   5. ,
         5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,
        10. ,  10.5])

#2

You can get the results similar to Matlab's interp() function for example like this:

您可以获得类似于Matlab的interp（）函数的结果，例如：

def interpolate_1d_vector(vector, factor):
    """
    Interpolate, i.e. upsample, a given 1D vector by a specific interpolation factor.
    :param vector: 1D data vector
    :param factor: factor for interpolation (must be integer)
    :return: interpolated 1D vector by a given factor
    """
    x = np.arange(np.size(vector))
    y = vector
    f = scipy.interpolate.interp1d(x, y)

    x_extended_by_factor = np.linspace(x[0], x[-1], np.size(x) * factor)
    y_interpolated = np.zeros(np.size(x_extended_by_factor))

    i = 0
    for x in x_extended_by_factor:
        y_interpolated[i] = f(x)
        i += 1

    return y_interpolated

#1