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SciPyCamp Syllabus

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The outline of SciPyCamp, the follow-on week to Advanced PyCamp. The organization of topics in which you will apply Python to scientific problems. This organization is unique compared to other course outlines. Rather than rote recitation of techniques before applying them to problem solving, you will learn the Pythonic way of programming by examining solutions to common programming problems.

See syllabi for other TriZPUG Python Boot Camp weeks: PyCamp Syllabus • Advanced PyCamp Syllabus

Contents

DAY ONE: NumPy, Numarray, and Numeric
DAY TWO: ScientificPython
DAY THREE: SciPy
DAY FOUR: Visualization
DAY FIVE: Applications

DAY ONE: NumPy, Numarray, and Numeric

NumPy I • NumPy II • NumPy III • back to contents

NumPy I

  • Arrays
  • Indexing
  • Functions and Operators
  • Typecodes

NumPy II

  • Scalars
  • Vectors
  • Slices

NumPy III

  • Randomization
  • Gnuplot
  • Curve Fitting
  • Structured Grids
  • Tools

DAY TWO: ScientificPython

ScientificPython I • ScientificPython II • ScientificPython III • back to contents

ScientificPython I

  • Derivatives
  • Roots
  • Interpolation
  • Least Squares
  • Polynomials

ScientificPython II

  • Geometry
  • Quaternions
  • Tensors
  • Transforms

ScientificPython III

  • Signal Models
  • Statistics
  • NetCDF
  • Protein Data Bank
  • VRML

DAY THREE: SciPy

SciPy I • SciPy II • back to contents

SciPy I

  • FFT
  • Integration
  • ODEs
  • B-Splines
  • Convolution

SciPy II

  • Hilbert Transforms
  • Matrices
  • Eigenvalues and Eigenvectors
  • Decomposition

DAY FOUR: Visualization

Matplotlib • Gnuplot • back to contents

Matplotlib

  • PyLab
  • Embedding in GUIs

Gnuplot

  • Coordinate Systems
  • Plot Objects and Expressions
  • Time Series
  • Modes of OperationSlices

DAY FIVE: Applications

Tools • back to contents

Tools

  • PyMat (MatLab)
  • PyTables (HDF)
  • RPy
  • Python Cartographic Library
  • Thuban
  • Maya
  • Blender
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