Python与HDF5数据处理

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"Python and HDF5 - 通过Python处理大数据的高效工具" 在现代数据分析领域，Python和HDF5的结合已经成为处理大规模数据的重要技术。HDF5（Hierarchical Data Format 5）是一种用于存储和管理大量科学数据的文件格式，它支持复杂的数据结构，并允许高效、快速地读写大量数据。Python，作为一种强大且易学的编程语言，拥有丰富的库，使得与HDF5的交互变得简单易行。 Python和HDF5的结合主要体现在以下几个方面： 1. **数据存储**：HDF5提供了一种分层的文件结构，类似于文件系统，可以组织和存储不同类型的数据。这种结构使得数据的组织和访问更加有序。Python的`h5py`库是连接Python和HDF5的桥梁，它提供了接口，让开发者能够轻松创建、读取和修改HDF5文件。 2. **大数据处理**：HDF5支持大对象（如图像、视频或大型数组）的存储，对于处理“大数据”尤其有用。Python结合HDF5，可以实现对大规模数据集的有效管理和分析，而不必将所有数据加载到内存中，这对于内存有限的系统来说非常关键。 3. **高性能**：HDF5设计时考虑了性能，使用了高效的压缩算法和缓存机制，减少了I/O操作的时间开销。在Python中，`h5py`库允许直接在数组级别进行操作，进一步提高了数据处理速度。 4. **数据访问**：Python的灵活性使得访问HDF5文件中的数据变得简单。你可以通过键（key）来查找特定数据集，还可以利用HDF5的组（group）功能来组织相关数据。 5. **并行计算**：HDF5支持多进程并行访问，这在分布式计算环境中特别有价值。Python库如`mpi4py`可以结合HDF5，实现并行处理大数据，提高计算效率。 6. **可视化和分析**：Python有强大的可视化库如matplotlib和seaborn，以及数据分析库如pandas和numpy，这些库可以与HDF5结合，帮助用户探索和理解存储在HDF5文件中的复杂数据。 7. **应用广泛**：Python和HDF5的组合被广泛应用在天文学、物理学、生物信息学、气象学等多个科学领域，以及金融、工业等领域的数据分析。通过掌握Python和HDF5的使用，无论是初创公司还是大型企业，都能更好地利用数据驱动的洞察力，创造新产品，改进现有产品，了解客户行为，从而获取数据优势。访问O'Reilly Media的网站可以获取更多关于如何利用这些工具处理大数据和获取洞察的信息。

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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