Python与HDF5在科学数据分析中的应用

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"Python and HDF5 是一本由 Andrew Collette 编写的关于使用 Python 处理 HDF5 数据的书籍。HDF5 是一种高效、灵活的数据存储格式，常用于科学计算和大数据分析。这本书旨在教读者如何利用 Python 的库与 HDF5 进行交互，以实现数据的收集、管理和分析。" 在过去的几年里，Python 已逐渐成为科学研究领域的有力替代品，挑战了传统的 IDL 或 MATLAB 等环境。Python 社区发展出了一系列稳定的核心库，如 NumPy 用于处理数值数组，SciPy 用于数据分析，以及 matplotlib 用于数据可视化。这些库的存在大大减少了编写科学代码的工作量，并提升了结果的准确性。随着四大趋势的融合，数据驱动的洞察力成为了企业和机构的重要资产。这四大趋势包括：新的数据收集、管理和分析方法；云 computing 提供了低成本的存储和灵活的、按需计算能力，适用于大规模数据集；可视化技术将复杂数据转化为直观的故事讲述图像；以及让任何人都能访问数据的工具。O'Reilly Media 的 Strata 系列提供了掌握大数据并将其转化为洞察力的机会。在这本书 "Python and HDF5" 中，作者 Andrew Collette 详细介绍了如何结合 Python 的力量来利用 HDF5。他涵盖了如何创建、读取和操作 HDF5 文件，以及如何利用 Python 库如 h5py 来高效地处理大型数据集。书中可能还包含了如何进行数据组织、压缩、切片和查询，以及如何利用 HDF5 的特性来实现数据的并发访问和版本控制。 Python 的易用性和 HDF5 的高性能相结合，使得科学家和工程师能够更便捷地存储和分析大量复杂数据。通过学习这本书，读者将能够更好地理解如何在 Python 环境中运用 HDF5 解决实际问题，提升数据处理和分析的能力，无论是用于新产品开发、现有产品的改进，还是深入理解客户行为，都能从数据中获取竞争优势。

Darren Dale and many others deserve thanks for contributing to the h5py project, along

with Francesc Alted, Antonio Valentino, and fellow authors of PyTables who first

brought the HDF5 and Python worlds together. I would also like to thank Steve Vincena

and Walter Gekelman of the UCLA Basic Plasma Science Facility, where I first began

working with large-scale scientific datasets.

xiv | Preface

CHAPTER 1

Introduction

When I was a graduate student, I had a serious problem: a brand-new dataset, made up

of millions of data points collected painstakingly over a full week on a nationally rec‐

ognized plasma research device, that contained values that were much too small.

About 40 orders of magnitude too small.

My advisor and I huddled in his office, in front of the shiny new G5 Power Mac that ran

our visualization suite, and tried to figure out what was wrong. The data had been

acquired correctly from the machine. It looked like the original raw file from the ex‐

periment’s digitizer was fine. I had written a (very large) script in the IDL programming

language on my Thinkpad laptop to turn the raw data into files the visualization tool

could use. This in-house format was simplicity itself: just a short fixed-width header

and then a binary dump of the floating-point data. Even so, I spent another hour or so

writing a program to verify and plot the files on my laptop. They were fine. And yet,

when loaded into the visualizer, all the data that looked so beautiful in IDL turned into

a featureless, unstructured mush of values all around 10

-41

Finally it came to us: both the digitizer machines and my Thinkpad used the “little-

endian” format to represent floating-point numbers, in contrast to the “big-endian”

format of the G5 Mac. Raw values written on one machine couldn’t be read on the other,

and vice versa. I remember thinking that’s so stupid (among other less polite variations).

Learning that this problem was so common that IDL supplied a special routine to deal

with it (SWAP_ENDIAN) did not improve my mood.

At the time, I didn’t care that much about the details of how my data was stored. This

incident and others like it changed my mind. As a scientist, I eventually came to rec‐

ognize that the choices we make for organizing and storing our data are also choices

about communication. Not only do standard, well-designed formats make life easier

for individuals (and eliminate silly time-wasters like the “endian” problem), but they

make it possible to share data with a global audience.

Python and HDF5

In the Python world, consensus is rapidly converging on Hierarchical Data Format

version 5, or “HDF5,” as the standard mechanism for storing large quantities of nu‐

merical data. As data volumes get larger, organization of data becomes increasingly

important; features in HDF5 like named datasets (Chapter 3), hierarchically organized

groups (Chapter 5), and user-defined metadata “attributes” (Chapter 6) become essen‐

tial to the analysis process.

Structured, “self-describing” formats like HDF5 are a natural complement to Python.

Two production-ready, feature-rich interface packages exist for HDF5, h5py, and PyT‐

ables, along with a number of smaller special-purpose wrappers.

Organizing Data and Metadata

Here’s a simple example of how HDF5’s structuring capability can help an application.

Don’t worry too much about the details; later chapters explain both the details of how

the file is structured, and how to use the HDF5 API from Python. Consider this a taste

of what HDF5 can do for your application. If you want to follow along, you’ll need

Python 2 with NumPy installed (see Chapter 2).

Suppose we have a NumPy array that represents some data from an experiment:

>>> import numpy as np

>>> temperature = np.random.random(1024)

>>> temperature

array([ 0.44149738, 0.7407523 , 0.44243584, ..., 0.19018119,

0.64844851, 0.55660748])

Let’s also imagine that these data points were recorded from a weather station that

sampled the temperature, say, every 10 seconds. In order to make sense of the data, we

have to record that sampling interval, or “delta-T,” somewhere. For now we’ll put it in

a Python variable:

>>> dt = 10.0

The data acquisition started at a particular time, which we will also need to record. And

of course, we have to know that the data came from Weather Station 15:

>>> start_time = 1375204299 # in Unix time

>>> station = 15

We could use the built-in NumPy function np.savez to store these values on disk. This

simple function saves the values as NumPy arrays, packed together in a ZIP file with

associated names:

>>> np.savez("weather.npz", data=temperature, start_time=start_time, station=

station)

We can get the values back from the file with np.load:

2 | Chapter 1: Introduction

guages like C and FORTRAN. It’s now relatively common to deal with datasets hundreds

of gigabytes or even terabytes in size; HDF5 itself can scale up to exabytes.

On all but the biggest machines, it’s not feasible to load such datasets directly into

memory. One of HDF5’s greatest strengths is its support for subsetting and partial I/O.

For example, let’s take the 1024-element “temperature” dataset we created earlier:

>>> dataset = f["/15/temperature"]

Here, the object named dataset is a proxy object representing an HDF5 dataset. It

supports array-like slicing operations, which will be familiar to frequent NumPy users:

>>> dataset[0:10]

array([ 0.44149738, 0.7407523 , 0.44243584, 0.3100173 , 0.04552416,

0.43933469, 0.28550775, 0.76152561, 0.79451732, 0.32603454])

>>> dataset[0:10:2]

array([ 0.44149738, 0.44243584, 0.04552416, 0.28550775, 0.79451732])

Keep in mind that the actual data lives on disk; when slicing is applied to an HDF5

dataset, the appropriate data is found and loaded into memory. Slicing in this fashion

leverages the underlying subsetting capabilities of HDF5 and is consequently very fast.

Another great thing about HDF5 is that you have control over how storage is allocated.

For example, except for some metadata, a brand new dataset takes zero space, and by

default bytes are only used on disk to hold the data you actually write.

For example, here’s a 2-terabyte dataset you can create on just about any computer:

>>> big_dataset = f.create_dataset("big", shape=(1024, 1024, 1024, 512),

dtype='float32')

Although no storage is yet allocated, the entire “space” of the dataset is available to us.

We can write anywhere in the dataset, and only the bytes on disk necessary to hold the

data are used:

>>> big_dataset[344, 678, 23, 36] = 42.0

When storage is at a premium, you can even use transparent compression on a dataset-

by-dataset basis (see Chapter 4):

>>> compressed_dataset = f.create_dataset("comp", shape=(1024,), dtype='int32',

compression='gzip')

>>> compressed_dataset[:] = np.arange(1024)

>>> compressed_dataset[:]

array([ 0, 1, 2, ..., 1021, 1022, 1023])

What Exactly Is HDF5?

HDF5 is a great mechanism for storing large numerical arrays of homogenous type, for

data models that can be organized hierarchically and benefit from tagging of datasets

with arbitrary metadata.

4 | Chapter 1: Introduction

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Python与HDF5在科学数据分析中的应用

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