Python for quantum chemistry : a full stack programming guide / Qiming Sun.

Format:

Book

Author/Creator:

Sun, Qiming, author.

Contributor:

ScienceDirect (Online service)

Series:

Theoretical and computational chemistry ; 23.

Theoretical and computational chemistry ; Volume 23

Language:

English

Subjects (All):

Quantum chemistry--Data processing.

Quantum chemistry.

Quantum chemistry--Computer programs.

Python (Computer program language).

Physical Description:

1 online resource.

Place of Publication:

Amsterdam, Netherlands : Elsevier, 2025.

Contents:

Front Cover

Python for Quantum Chemistry

Copyright

Contents

Preface

The scope of this book

Who is the audience

Outline of this book

Where to find support information

Acknowledgments

References

List of acronyms

1 Python ecosystems and tools for scientific computing

1 Python programming environment

1.1 Python package system

1.1.1 Python runtime

1.1.1.1 Python executables

1.1.1.2 Python runtime paths

1.1.1.3 Inspecting dependencies

1.1.1.4 The importlib library

1.1.2 Package, sub-package, and module

1.1.2.1 Adding packages

1.1.2.2 Package dependencies

1.1.2.3 Namespace packages

1.1.3 Python program as CLI tools

1.1.4 Virtual environment

1.1.5 Conda

1.2 Python programs in Docker

1.3 Managing a Python project

1.3.1 Version control

Downloading projects from GitHub

Synchronizing remote repository to local repository

Contributing to a GitHub repository

Inspecting changes made in another branch

Reverting modifications

Identifying the commit where a bug was introduced

Sub-packages distributed in different repositories

1.3.2 Testing a Python program

1.3.2.1 Testing tools

1.3.2.2 Testing automation

1.3.2.3 Configuring tests for selective execution

1.3.3 Coding style and static checks

1.3.4 Releasing a Python package

1.3.4.1 Python project structure

1.3.4.2 pyproject.toml and setup.py for Python package configuration

1.3.4.3 Building wheels

1.3.4.4 Files in the PyPI package

1.3.4.5 Building Conda packages

1.3.5 CI and DevOps workflow

1.3.5.1 Self-hosted runners

1.3.5.2 PyPI release workflow

1.4 Modular design and object-oriented programming

1.4.1 Method resolution order in multiple inheritances

1.4.2 Mixins

1.5 Working with IPython and Jupyter

1.5.1 Startup configurations

1.5.2 Extensions.

1.5.3 Magics

1.5.4 Remote execution

Summary

2 Data processing

2.1 Vectorized data processing with NumPy

2.1.1 The basics of NumPy

2.1.2 Universal functions (ufunc)

2.1.3 Inplace operations

2.1.4 Broadcasting

2.1.5 Fancy indexing

2.1.6 Mask array

Indexing a dense array

Indexing a sparse array

Randomly distributed mask array

Indexing a high-dimensional array

2.1.7 Data structure of NumPy ndarray

2.1.7.1 The strides attribute for flexible array structure

2.1.7.2 C-contiguous and F-contiguous storage

2.1.8 Array views

Slicing an array

NumPy functions that return a view of the input array

Calling np.ndarray() with the keyword argument buffer

2.1.9 The reshape function

2.2 Data types in NumPy

2.2.1 Type casting

2.2.2 Scalar type and zero-dimensional array

2.2.3 Infinity ( inf ) and not-a-number ( nan )

2.2.4 Data with high precision

2.2.5 Structured array

2.3 Data with labels: Pandas

2.3.1 Pandas data objects

2.3.1.1 Series

2.3.1.2 DataFrame

2.3.2 Broadcasting

2.3.3 Indexing

2.3.3.1 Location-based indexing

2.3.3.2 Label-based indexing

2.3.3.3 Attribute-based indexing

2.3.3.4 Indexing elements in DataFrame

2.3.4 query and eval methods

2.3.5 Altering structure of DataFrame

2.3.5.1 Changing axes labels

2.3.5.2 Inserting or removing rows and columns

2.3.5.3 Reorganizing rows and columns

2.3.6 Data types

2.3.7 Missing data

2.3.8 Grouping and aggregation

2.3.9 View and copy

3 Visualization

3.1 Matplotlib

3.2 Pandas visualization

3.3 Mayavi for 3D plotting

3.4 Quantum chemistry visualization

3.4.1 Jinja template

Expression

Control statements

Whitespace control

3.4.2 Molden format

3.4.3 Cube format

4 Scientific computing tools.

4.1 Linear algebra

4.2 Sparse matrices

4.2.1 Storage formats

4.2.2 Linear algebra for sparse matrix

4.2.3 Linear operator

4.3 Tensor contractions

4.4 Discrete Fourier transforms

4.4.1 SciPy FFT

4.4.2 Intel FFT

4.4.3 PyFFTW

4.4.4 FFT performance benchmark

4.4.5 How to choose FFT libraries?

5 Meta-programming and non-numerical computation

5.1 Code generation

5.1.1 eval and exec

5.1.2 Composing code programmatically

5.1.3 Manipulating classes and functions at runtime

5.1.4 Python AST

5.2 Symbolic computation

5.3 Automatic differentiation

5.3.1 PyTorch

5.3.2 JAX

6 Input and output

6.1 Serialization

6.1.1 Pickle

What can be pickled?

Cross-platform compatibility

Software version dependency

Security problem

6.1.2 JSON

6.1.3 YAML

6.2 File I/O

6.2.1 The npy format

6.2.2 HDF5 storage

6.2.3 Memory mapping

6.3 I/O buffering

6.4 In-memory I/O

6.5 Network I/O

6.5.1 Network requests over HTTP

6.5.2 REST API

6.5.3 RPC over HTTP

6.5.4 gPRC

6.5.5 Apache arrow

7 Working with cloud

7.1 Utilizing cloud computing

7.1.1 Comparison between cloud and supercomputers

7.1.2 Designing a workflow

7.1.3 Computing resources

7.1.4 Communications among cloud services

The configuration of the DFT task executor

The configuration of the RPC server

Deploying RPC server

7.1.5 Function-as-a-Service

7.2 Distributed job executors

7.2.1 Celery

7.2.2 Dask

7.2.3 Ray

2 High performance computing with Python

8 Foreign language interfaces

8.1 Inter-process communication interfaces

8.2 C/C++ interfaces

8.2.1 Data types and type conversion

8.2.1.1 Integer

8.2.1.2 Float

8.2.1.3 String.

8.2.1.4 Class and structure

8.2.1.5 Array

8.2.1.6 Function pointer and callback

8.2.2 Cython

The cythonize compiler

The setuptools extension

8.2.3 pybind11

8.2.4 Compiling C++ code into a Python module

Enabling OpenMP

Using Intel compilers

Linking to external libraries

Extensions with MPI

Invoking CMake

NumPy header files

8.3 Foreign function interfaces

8.3.1 Ctypes

8.3.1.1 Function prototype

8.3.1.2 NumPy array

8.3.1.3 Pointers

8.3.1.4 Structure and complex numbers

8.3.1.5 Callbacks

8.3.2 CFFI

8.3.3 Memory leaks

8.3.4 Duplicated function names in extensions

8.4 Fortran interfaces

8.4.1 ctypes for Fortran

8.4.2 f2py compiler

8.5 Rust interfaces

9 Program performance optimization

9.1 Principles for performance optimization

9.1.1 Cost comparison between Python and C/C++ operations

Atomic instructions

Dictionary lookup

Referencing a variable

Vectorized operations in NumPy arrays

Branching

Function calls

Data type conversion

I/O operations

Running shell commands

9.1.2 Hardware and operating system overhead

Memory allocation

Page fault

Context switch

9.1.3 Latency and throughput

CPU

CPU cache

Memory

Storage

Network

9.1.4 Strategies for optimizing latency and throughput

9.1.5 Computation bound and I/O bound

9.1.5.1 Is a program computation-bound or memory-bound?

9.1.5.2 How to accelerate memory-bound applications?

9.1.5.3 Optimizing for cache size

9.1.5.4 Optimizing data locality

9.1.5.5 Optimizing cache coherence

9.1.6 Instruction level parallelism

9.2 Profiling

9.2.1 Benchmark tests

9.2.2 Python built-in profiling tools

9.2.3 Line profiler

9.2.4 Sampling profiler

9.2.4.1 py-spy

9.2.5 Perf

9.3 Python level optimization.

9.3.1 The dis module

9.3.2 Performance-friendly Python code

Using enumerate for index and object iteration

Using generator instead of explicitly constructing list or tuple

List comprehension for creating new lists

Dictionary comprehension for creating dictionaries

Merging dictionaries

Swapping two variables

Concatenating strings

Using local variables

9.3.3 Utilizing tensor operations

9.3.4 Optimizing tensor indexing efficiency

9.4 Compiling Python code

9.4.1 Numba

9.4.1.1 Unrolling

9.4.1.2 Numba vectorization issues

9.4.2 Cython

9.4.3 Pythran

9.4.4 Comparison of Cython, Pythran, and Numba

Ease of use

Support for Python features

Optimization hints

Profiling

NumPy specialization

SIMD vectorization

Compilation cost

Execution speed

9.5 Optimization with compiled languages

9.5.1 GCC compiler

9.6 Optimization for I/O

9.6.1 Storage layout

HDF5 format

Memory mapping

HDF5 vs memmap

9.6.2 Compressing data

9.6.3 Overlapping computation and I/O

9.7 Precomputation and memoization

9.7.1 LRU cache

9.7.2 Functional programming

9.7.3 Dynamic programming

9.8 Optimization with lazy evaluation

10 Parallel computation

10.1 Multithreading

10.1.1 The threading module

How to manage data sharing between threads?

How to monitor tasks running on child threads?

How to ensure threading efficiency?

10.1.2 ThreadPoolExecutor

10.2 Lock and thread synchronization

10.2.1 Mutex

10.2.2 Event and condition variable

10.3 Producer-consumer model

10.4 Pipeline executor

10.5 Asynchronous program

10.5.1 Similarity between asynchronous programming and lazy evaluation

10.5.2 Asynchronous program with coroutines and asyncio

10.6 Multiprocessing

10.6.1 Data communication in multiprocessing.Process.

Notes:

Includes bibliographical references and index.

Electronic reproduction. Amsterdam Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on June 17, 2025).

Other Format:

Print version: Sun, Qiming Python for Quantum Chemistry

Print version:

ISBN:

0443238367

9780443238369

Publisher Number:

90104286437

CIPO000210218

Access Restriction:

Restricted for use by site license.