Numerical Python (English course)
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Algemeen

This course will be scheduled by request only, or as an in-company training. Our course administration maintains a waiting list of interested individuals. Joining that list is without obligation whatsoever.

The course Numerical Python covers the Python packages NumPy, SciPy en Matplotlib. The packages provide facilities for scientific and technical calculations. They are Open Source, covered by an issue-free license. Main targets during their design were ease of use and efficiency of the calculations on large volumes of data.

Course attendees receive the following documentation:

  • Course attendees receive a practice book containing copies of the presentations, exercises and answers to the exercises. Furthermore, attendees receive an extensive handout in English language, purpose-built for AT Computing.
  • Shortly after the course the student will receive a certificate as a proof of participation

Doel

  • This course will enable the students to use the special NumPy-ndarray-facilities. Laboratory exercises will have demonstrated the most popular NumPy functions (methodes) and data types. Visualising one and two dimensional data using Matplotlib, and using plot functions to explore the functionality of SciPy will become part of the student's repertoire. The SciPy functions allow the construction of programs to support complex scientific problems.

Doelgroep

Programmers planning to use the Python language for scientific calculations.

Voorkennis

Onderstaande voorkennis is vereist:

Programming experience with the Python language.
A background in mathematics as required for scientific applications: complex numbers, goniometry, polynomes, calculus, distributions, Fourier transformations.

Bijzonderheden

Not covered during this course:The relationship between NumPy/SciPy and MatLab

Onderwerpen

  • NumPy arrays (ndarray), corresponding data types and operations
  • Relationship between Python’s standard Math functions and their ‘vectorised’ NumPy counterparts
  • Scalar and array operations, linspace(), augmented assignments
  • Array comparisons, any(),all(), slicing, indexing, reshape()
  • Views vs. copies, ravel(),flatten(),transpose(), more methods
  • NaN and inf
  • Data in text files, loadtxt
  • Random numbers, distributions, Monte Carlo simulations, polynomes
  • Matrices and their operations
  • Matplotlib: 2D and 3D plots, image and contour plots, enhanced plots
  • Special classes: figure, axes, axis, patch, histogram
  • Surface plots using meshgrid
  • SciPy modules misc, optimize, leastsq
  • SciPy: the args parameter for function arguments

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Cursus: Numerical Python (English course)

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Numerical Python (English course)
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