Part 3: Useful Libraries and Solutions to Common Problems
11
Working with Data
Modern Scientific Computing with Julia
About
Preface
Part 1: Programming in Julia
1
Basics of the Julia Language
2
Julia’s Type System
3
Methods & Multiple Dispatch
4
Working with Arrays
5
Design Patterns
Part 2: Research Software Engineering
6
Package Development
7
Git and GitHub
8
Software Sustainability
9
Test Driven Development
10
Performance & Introspection
Part 3: Useful Libraries and Solutions to Common Problems
11
Working with Data
12
Differentiable Programming
13
Linear and Nonlinear Equations
14
Differential Equations
15
Machine Learning
Part 4: Parallel and GPU Programming
16
Parallel Programming Paradigms
17
Parallel Arrays
18
GPU Programming
19
Hybrid Parallel Programming
20
Case Studies
Summary
References
Table of contents
11.1
Reading and Writing CSV
11.2
Reading and Writing HDF5
11.3
Reading and Writing Config Files
11.4
Manipulating Tabulated Data with DataFrames.jl
11.5
Basic Plotting with Makie
11.6
Augmented Data
11.7
Quadruple and Arbitrary Precision
11.8
Uncertainties (Measurements.jl)
11.9
Physical Units (Unitful.jl)
Part 3: Useful Libraries and Solutions to Common Problems
11
Working with Data
11
Working with Data
11.1
Reading and Writing CSV
11.2
Reading and Writing HDF5
11.3
Reading and Writing Config Files
11.4
Manipulating Tabulated Data with DataFrames.jl
11.5
Basic Plotting with Makie
11.6
Augmented Data
11.7
Quadruple and Arbitrary Precision
11.8
Uncertainties (Measurements.jl)
11.9
Physical Units (Unitful.jl)
Part 3: Useful Libraries and Solutions to Common Problems
12
Differentiable Programming