使用不同的输入参数循环运行 ipython notebook 的一部分 [英] Run parts of a ipython notebook in a loop / with different input parameter

问题描述

我写了一个 ipython notebook，它分析了一个数据集.现在我想使用这段代码来遍历不同的数据集.

I have written a ipython notebook, which analyses a dataset. Now I want to use this code to loop over different datasets.

代码分为大约 50 个单元格(包括注释、markdown 解释……).有没有办法循环运行笔记本的一部分或使用不同的输入参数运行整个笔记本?

The code is split into about 50 cells (including comments, markdown explanations,...). Is there a way to run parts of a notebook in a loop or running a whole notebook with different input parameters?

我不想将所有单元格合并为一个函数或将代码下载为 Python 脚本，因为我真的很喜欢通过仅执行某些单元格来运行(并试验)部分分析.

I don't want to merge all cells into one function or download the code as a python script, as I really like to run (and experimenting with) parts of the analysis by executing only certain cells.

基本上它将脚本的一部分重构为一个函数并在循环中调用该函数，只是脚本的一部分"是笔记本单元.

Basically its refactoring parts of a script into a function and calling the function in a loop, just that the "parts of the script" are notebook cells.

推荐答案

我在这些场景中通常做的是将重要的单元格包装为函数(您不必合并它们中的任何一个)并有一个特定的主单元格迭代参数列表并调用这些函数.例如.这是我的一个笔记本中主细胞"的样子:

What I usually do in these scenarios is wrap the important cells as functions (you don't have to merge any of them) and have a certain master cell that iterates over a list of parameters and calls these functions. E.g. this is what a "master cell" looks like in one of my notebooks:

import itertools
# parameters
P_peak_all = [100, 200]
idle_ratio_all = [0., 0.3, 0.6]
# iterate through these parameters and call the notebook's logic
for P_peak, idle_ratio in itertools.product(P_peak_all, idle_ratio_all):
    print(P_peak, idle_ratio, P_peak*idle_ratio)
    print('========================')
    m_synth, m_synth_ns = build_synth_measurement(P_peak, idle_ratio)
    compare_measurements(m_synth, m_synth_ns, "Peak pauser", "No scheduler", file_note="-%d-%d" % (P_peak, int(idle_ratio*100)))

您仍然可以在整个笔记本中拖动一些数据(即使用您的数据调用单元格底部的每个函数)，以便能够对单个单元格进行实时测试.例如，某些单元格可能会声明:

You can still have some data dragging throughout the notebook (i.e. calling each function at the bottom of the cell with your data) to be able to test stuff live for individual cells. For example some cell might state:

def square(x):
    y = x**2
    return y
square(x) # where x is your data running from the prior cells 

这让您可以进行现场实验，并且仍然可以从主单元调用通用功能.

Which lets you experiment live and still call the generic functionality from the master cell.

我知道使用函数重构你的笔记本需要一些额外的工作，但我发现它实际上提高了我的笔记本的可读性，当你在很长一段时间后重新使用它时很有用，并且更容易将其转换为正确的"脚本或模块(如有必要).

I know it's some additional work to refactor your notebook using functions, but I found it actually increases my notebook's readability which is useful when you come back to it after a longer period and it's easier to convert it to a "proper" script or module if necessary.

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