Week 4

Week 4#

Part 0. Python libraries for scientific computing#

  • 0.1 Installing Libraries

  • 0.2 Importing Libraries

Part 1. Numpy for numerical computations#

  • 1.1 Introduction to Numpy

  • 1.2 Numpy Objects: N-dimensional arrays

    • 1.2.1 Array Attributes

    • 1.2.2 Array Indexing & Slicing

    • 1.2.3 Array Creation Functions

    • 1.2.4 Array Operations

      • Statistical functions

      • Linear Algebra operations

      • Reshaping and Transposing

      • Stacking and Splitting

      • Sorting and Searching

      • Input and Output

      • Copying and Masking

      • Important methods for working with Matrices and Vectors

  • 1.3 Non-numeric data types in Numpy

  • 1.4 Linear algebra with Numpy

    • 1.4.1 Scalar Array/Matrix Operations

    • 1.4.2 Array-Array Operations

    • 1.4.3 Matrix Multiplication and Dot Product

    • 1.4.4 Other Linear Algebra Operations

  • 1.5 Some useful Numpy tools

    • 1.5.1 Random Numbers

    • 1.5.2 Polynomial Fitting

Part 2. Matplotlib for data visualization#

  • 2.1 Matplotlib Pyplot

  • 2.2 Line Plots

    • 2.2.1 Lines

    • 2.2.2 Colour

    • 2.2.3 Width

    • 2.2.4 Line style

    • 2.2.5 Marker Types

    • 2.2.6 Axes

      • Limits

      • Ticks

    • 2.2.7 Grid

    • 2.2.8 Labels

      • Title

      • Axis Lables

    • 2.2.9 Legends

    • 2.2.10 Error Bars

    • 2.2.11 Asymptotes and Annotations

    • 2.2.12 Log-log and Semi-log plots

  • 2.3 Figure object

  • 2.4 Multiple plots

    • 2.4.1 2.4.1 Making sub-plots using plt.subplot()

    • 2.4.2 Plots in Plots

    • 2.4.3 Making sub-plots using fig.add_subplot()

    • 2.4.4 Making sub-plots using plt.subplots()

    • 2.4.5 Histogram

    • 2.4.6 Scatter

    • 2.4.7 Bar Chart

    • 2.4.8 Pie Chart

  • 2.5 3D plots and animations

    • 2.5.1 3D Line Plots

    • 2.5.2 Heat Map

    • 2.5.3 Plotting a Surface

    • 2.5.4 Interactive backends

    • 2.5.5 Animations

Part 3. SciPy for scientific computing#

  • 5.1 Scipy Linear Algebra

    • 5.1.1 Linear Systems

    • 5.1.2 Spectral Decomposition

    • 5.1.3 Matrix Functions

    • 5.1.4 Sparse Matrices

    • 5.1.5 Matrix Exponential

    • 5.1.6 Unitary Matrix

    • 5.1.7 Tensor Product

    • 5.1.8 Linalg Least Square Fit

    • 5.1.9 Single Value Decomposition

    • 5.1.10 Schur Decomposition

  • 5.2 Scipy Optimization

    • 5.2.1 Function Minimum

    • 5.2.2 Global Optimization

    • 5.2.3 Function Roots

    • 5.2.4 Optimize Least Square Fit

    • 5.2.5 Curve Fit

  • 5.3 Scipy Fourier Transform

    • 5.3.1 The Fourier Transform

    • 5.3.2 Wave Packet

    • 5.3.3 The Uncertainty Principle

    • 5.3.4 Quantum Fourier Transform

    • 5.3.5 The Bloch Sphere

  • 5.4 Scipy ODEs & PDEs

    • 5.4.1 Integration

    • 5.4.2 ODE

    • 5.4.3 Finite Difference Method

    • 5.4.4 PDE

Contents