Matplotlib

Transcript

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2. Nicolas P. Rougier:
   - Researcher in computational cognitive neuroscience, located in
   Bordeaux, France
   - Inria (the French institute for computer science)
   - Institute of Neurodegenerative Diseases
   - Author of several scientific computing books (All Free!)
   - Python & OpenGL for Scientific Visualization
   - From Python to Numpy
   - Scipy Lecture Notes
   Jake VanderPlas:
   - Visiting Researcher Google Seattle Office
   - Former Director of Open Software at the University of
   Washington’s eScience institute
   - Contributions made in
   - Scikit-Learn
   - SciPy
   - AstroPy
   - Altair
   

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4. Part One:
   The Origin Story
   

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5. Matplotlib was created by John D. Hunter in 2003 to
   visualize electrocorticography data. In a post to the official
   python mailing list John writes ...
   My goal is to make high quality,
   publication quality plotting easy
   in python, with a syntax familiar
   to matlab users.
   

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6. Matplotlib was created by John D. Hunter in 2003 to
   visualize electrocorticography data. In a post to the official
   python mailing list John writes ...
   My goal is to make high quality,
   publication quality plotting easy
   in python, with a syntax familiar
   to matlab users.
   Also motivated later in the post ...
   * make easy things easy
   (subplots, lines styles, colors)
   * make hard things possible
   (OO interface for full control)
   

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7. Matplotlib Development Timeline
   

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8. The Python Visualization Landscape
   

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9. Important Questions:
   - Do you target desktop or web rendering?
   - Do you need complex 3D rendering?
   - Do you need publication quality?
   - Do you have very large data?
   - Is there an active community?
   - Are there documentation and tutorials?
   

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13. Strengths:
    - Designed like MatLab: switching was easy
    

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14. Strengths:
    - Designed like MatLab: switching was easy
    - Many rendering backends
    

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15. Strengths:
    - Designed like MatLab: switching was easy
    - Many rendering backends
    - Can reproduce just about any plot (with a bit of effort)
    

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16. Strengths:
    - Designed like MatLab: switching was easy
    - Many rendering backends
    - Can reproduce just about any plot (with a bit of effort)
    - Well-tested, standard tool for over a decade
    

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20. Strengths:
    - Designed like MatLab: switching was easy
    - Many rendering backends
    - Can reproduce just about any plot (with a bit of effort)
    - Well-tested, standard tool for over a decade
    Weaknesses:
    - API is imperative & often overly verbose
    - Poor support for web/interactive graphics
    - Often slow for large & complicated data
    

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21. Part Two: The Tale of Two API’s
    

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22. Matplotlib is
    organized in a
    hierarchy.
    

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23. Matplotlib is
    organized in a
    hierarchy.
    At the top of the hierarchy is the matplotlib "state-machine
    environment" which is provided by the
    matplotlib.pyplot module. At this level, simple
    functions are used to add plot elements (lines, images, text,
    etc.) to the current axes in the current figure.
    

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24. Matplotlib is
    organized in a
    hierarchy.
    At the top of the hierarchy is the matplotlib "state-machine
    environment" which is provided by the
    matplotlib.pyplot module. At this level, simple
    functions are used to add plot elements (lines, images, text,
    etc.) to the current axes in the current figure.
    The next level down in the hierarchy is the first level of the
    object-oriented interface. At this level, the user uses pyplot
    to create figures, and axes objects which are then used for
    most plotting actions.
    

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25. Matplotlib is
    organized in a
    hierarchy.
    At the top of the hierarchy is the matplotlib "state-machine
    environment" which is provided by the
    matplotlib.pyplot module. At this level, simple
    functions are used to add plot elements (lines, images, text,
    etc.) to the current axes in the current figure.
    The next level down in the hierarchy is the first level of the
    object-oriented interface. At this level, the user uses pyplot
    to create figures, and axes objects which are then used for
    most plotting actions.
    At the lowest level matplotlib manages a variety of graphics
    primitives and engines to render and display figures.
    ⫶
    

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26. MATLAB-style
    Interface
    Object-Oriented
    Interface
    

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27. MATLAB-style
    Interface
    Object-Oriented
    Interface
    plt.plot(x, y)
    plt.xlim(-1, 1)
    plt.title(“My Plot”)
    ...
    

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28. MATLAB-style
    Interface
    Object-Oriented
    Interface
    plt.plot(x, y)
    plt.xlim(-1, 1)
    plt.title(“My Plot”)
    ...
    fig = plt.figure()
    ax = fig.add_axes([0.1,0.1,0.8,0.8])
    ax.plot(x, y)
    ax.set(xlim=(-1, 1), title=”My Plot”)
    ...
    

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29. Fast and
    Convenient
    Detailed and
    Powerful
    plt.plot(x, y)
    plt.xlim(-1, 1)
    plt.title(“My Plot”)
    ...
    fig = plt.figure()
    ax = fig.add_axes([0.1,0.1,0.8,0.8])
    ax.plot(x, y)
    ax.set(xlim=(-1, 1), title=”My Plot”)
    ...
    

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30. MATLAB-style
    Interface
    Object-Oriented
    Interface
    plt.gcf() or plt.gca()
    

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31. Demo
    Time
    

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