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How IPython |
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1 | How IPython works | |
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2 | 2 | ================= |
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3 | 3 | |
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4 | 4 | Terminal IPython |
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5 | 5 | ---------------- |
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6 | 6 | |
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7 | 7 | When you type ``ipython``, you get the original IPython interface, running in |
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8 | 8 | the terminal. It does something like this:: |
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9 | 9 | |
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10 | 10 | while True: |
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11 | 11 | code = input(">>> ") |
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12 | 12 | exec(code) |
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13 | 13 | |
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14 | 14 | Of course, it's much more complicated, because it has to deal with multi-line |
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15 | 15 | code, tab completion using :mod:`readline`, magic commands, and so on. But the |
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16 | 16 | model is like that: prompt the user for some code, and when they've entered it, |
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17 | 17 | exec it in the same process. |
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18 | 18 | |
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19 | 19 | The IPython Kernel |
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20 | 20 | ------------------ |
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21 | 21 | |
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22 | 22 | All the other interfaces—the Notebook, the Qt console, ``ipython console`` in |
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23 | 23 | the terminal, and third party interfaces—use the IPython Kernel. This is a |
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24 | 24 | separate process which is responsible for running user code, and things like |
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25 | 25 | computing possible completions. Frontends communicate with it using JSON |
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26 | messages sent over ZeroMQ sockets; the protocol they use is described in | |
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26 | messages sent over `ZeroMQ <http://zeromq.org/>`_ sockets; the protocol they use is described in | |
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27 | 27 | :doc:`messaging`. |
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28 | 28 | |
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29 | 29 | The core execution machinery for the kernel is shared with terminal IPython: |
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30 | 30 | |
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31 | 31 | .. image:: figs/ipy_kernel_and_terminal.png |
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32 | 32 | |
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33 | 33 | A kernel process can be connected to more than one frontend simultaneously. In |
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34 | 34 | this case, the different frontends will have access to the same variables. |
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35 | 35 | |
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36 | 36 | .. TODO: Diagram illustrating this? |
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37 | 37 | |
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38 | 38 | This design was intended to allow easy development of different frontends based |
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39 | 39 | on the same kernel, but it also made it possible to support new languages in the |
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40 | 40 | same frontends, by developing kernels in those languages, and we are refining |
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41 | 41 | IPython to make that more practical. |
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42 | 42 | |
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43 | 43 | Today, there are two ways to develop a kernel for another language. Wrapper |
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44 | 44 | kernels reuse the communications machinery from IPython, and implement only the |
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45 | 45 | core execution part. Native kernels implement execution and communications in |
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46 | 46 | the target language: |
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47 | 47 | |
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48 | 48 | .. image:: figs/other_kernels.png |
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49 | 49 | |
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50 | Wrapper kernels are easier to write quickly for languages that have good Python | |
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51 | wrappers, like `Oct2Py <http://blink1073.github.io/oct2py/>`_ for Octave, or | |
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52 | languages where it's impractical to implement the communications machinery, like | |
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53 | `bash_kernel <https://pypi.python.org/pypi/bash_kernel>`_. Native kernels are | |
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54 | likely to be better maintained by the community using them, like | |
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55 | `IJulia <https://github.com/JuliaLang/IJulia.jl>`_ or `IHaskell <https://github.com/gibiansky/IHaskell>`_. | |
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56 | ||
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50 | 57 | .. seealso:: |
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51 | 58 | |
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52 | 59 | :doc:`kernels` |
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53 | 60 | |
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54 | 61 | :doc:`wrapperkernels` |
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55 | 62 | |
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56 | 63 | Notebooks |
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57 | 64 | --------- |
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58 | 65 | |
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59 | 66 | The Notebook frontend does something extra. In addition to running your code, it |
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60 | 67 | stores code and output, together with markdown notes, in an editable document |
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61 | 68 | called a notebook. When you save it, this is sent from your browser to the |
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62 | 69 | notebook server, which saves it on disk as a JSON file with a ``.ipynb`` |
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63 | 70 | extension. |
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64 | 71 | |
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65 | 72 | .. image:: figs/notebook_components.png |
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66 | 73 | |
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67 | 74 | The notebook server, not the kernel, is responsible for saving and loading |
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68 | 75 | notebooks, so you can edit notebooks even if you don't have the kernel for that |
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69 | 76 | language—you just won't be able to run code. The kernel doesn't know anything |
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70 | 77 | about the notebook document: it just gets sent cells of code to execute when the |
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71 | 78 | user runs them. |
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72 | 79 | |
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73 | 80 | Exporting to other formats |
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74 | 81 | `````````````````````````` |
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75 | 82 | |
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76 | 83 | The Nbconvert tool in IPython converts notebook files to other formats, such as |
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77 | 84 | HTML, LaTeX, or reStructuredText. This conversion goes through a series of steps: |
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78 | 85 | |
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79 | 86 | .. image:: figs/nbconvert.png |
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80 | 87 | |
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81 | 88 | 1. Preprocessors modify the notebook in memory. E.g. ExecutePreprocessor runs |
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82 | 89 | the code in the notebook and updates the output. |
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83 | 90 | 2. An exporter converts the notebook to another file format. Most of the |
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84 | 91 | exporters use templates for this. |
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85 | 92 | 3. Postprocessors work on the file produced by exporting. |
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86 | 93 | |
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87 | 94 | The `nbviewer <http://nbviewer.ipython.org/>`_ website uses nbconvert with the |
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88 | 95 | HTML exporter. When you give it a URL, it fetches the notebook from that URL, |
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89 | 96 | converts it to HTML, and serves that HTML to you. |
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