- Anaconda (intall Python 2.7 version!!!)
- a collection of python libraries, includes "Jupiter" (notebook environment)
- Standard libraries (included in Anaconda and other common distributions)
- numpy - array library
for python. The NumPy (Numeric Python) package provides basic routines for manipulating large arrays and matrices of numeric data. Beside the link on the left, there is another useful You Tube tutorial here.
- linalg (basic linear algebra)
- random (random sampling, probability distributions)
- scipy - scientific and numerical tools for Python.
The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms,
like minimization, Fourier transformation, regression, and other applied mathematical techniques. See also
an intro to numpy and skipy and
specific commonly used sub-libraries of scipy:
- matplotlib - powerful plotting and visualization library. Commonly used sub-libraries are listed below:
- pyplot - tools for plotting functions, arrays, vector fields.
It can also be used to draw shapes (lines, ovals, rectangles, etc).
See the summary of pyplot functions.
- image - tools for image loading, rescaling and display.
See also a summary of image functions.
- skimage - image processing and analysis library. It includes a few image samples.
- Matplotlib backend plotting in Jupiter/IPython:
"external" (default), inline, or notebook. See a summary and the notebook examples below.
- Notebook samples for CS3335 (work in Jupiter/Anaconda):
- Open CV - an extensive library of computer vision techniques