从返回NumPy数组的循环中收集结果 [英] Collecting results from a loop that returns NumPy Arrays

问题描述

我是一位公认的非常基本的Python程序员，试图在遇到各种研究问题的过程中学习.而且我遇到了这些问题之一-特别是如何处理循环，在该循环中我要返回一堆数据，而不是通常的单数输出"示例，您只需将循环的结果添加到之前的所有内容中

I'm an admittedly pretty basic Python programmer, trying to learn as I encounter problems implementing various research problems. And I've hit one of those problems - particularly, how to handle loops where I'm returning a bunch of data, rather than the usual "out comes a single number" examples where you just add the result of the loop to everything previous.

以下是我要运行的未循环脚本的要点: https://gist.github.com/1390355

Here's a Gist of the unlooped script I'm trying to run: https://gist.github.com/1390355

真正的重点是model_solve函数的结尾:

The really salient point is the end of the model_solve function:

def model_solve(t):
    # lots of variables set
    params = np.zeroes((n_steps,n_params)
    params[:,0] = beta
    params[:,1] = gamma
    timer = np.arange(n_steps).reshape(n_steps,1)
    SIR = spi.odeint(eq_system, startPop, t_interval)
    output = np.hstack((timer,SIR,params))
    return output

这将返回ODE积分位(spi.odeint)的结果以及一个简单的我们在什么时间步?"计时器和本质上是两列的两个随机变量的值以4950行和7列NumPy数组的形式重复很多次.

That returns the results of the ODE integration bit (spi.odeint) along with a simple "What time step are we on?" timer and essentially two columns of the value of two random variables repeated many, many times in the form of 4950 row and 7 column NumPy array.

但是，目标是对两个具有随机值的参数(β和γ)进行蒙特卡洛分析.本质上，我想制作一个像这样循环的函数:

The goal however is to run a Monte Carlo analysis of the two parameters (beta and gamma) which have random values. Essentially, I want to make a function that loops somewhat like so:

def loop_function(runs):
  for i in range(runs):
    model_solve(100)
    # output of those model_solves collected here
  # return collected output

所收集的输出随后将被写入文件.通常，我只需要将每个model_solve函数的结果写入文件中，但是此代码将在 PiCloud 上运行或其他平台，在结果返回到本地计算机之前，我不一定有能力写文件.相反，我试图返回由runs * 7列和4950行组成的NumPy大型数组-然后可以将其写入本地计算机上的文件中.

That collected output would then be written to a file. Normally, I'd just have each model_solve function write its results to a file, but this code is going to be run on PiCloud or another platform where I don't necessarily have the ability to write a file until the results are returned to the local machine. Instead, I'm trying to get a return of a huge NumPy array of runs*7 columns and 4950 rows - which can then be written to a file on my local machine.

关于如何解决这个问题的任何线索吗?

Any clues as to how to approach this?

推荐答案

使用列表保存所有结果:

use a list to save all the results:

results = []
for i in range(runs):
    results.append(model_solve(100))

然后通过以下方式获取输出数组:

then get the output array by:

np.hstack(results)

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