Full Code of jktr/matplotlib-backend-kitty for AI

Repository: jktr/matplotlib-backend-kitty
Branch: master
Commit: cbe4fe231d75
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Directory structure:
gitextract_81kwwsd_/

├── .gitignore
├── LICENSE
├── README.md
├── matplotlib-backend-kitty/
│   └── __init__.py
└── pyproject.toml

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.pyc
__pycache__
dist/
*.egg-info/


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FILE: LICENSE
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FILE: README.md
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# matplotlib-backend-kitty

This python module allows you to use your
[kitty terminal](https://github.com/kovidgoyal/kitty)
to show the plots generated by python's
[matplotlib](https://github.com/matplotlib/matplotlib).

For other terminals, the similar [notcurses backend](https://github.com/jktr/matplotlib-backend-notcurses)
may also be of interest.

To install this one, do one of the following

 - `$ pip install --user matplotlib-backend-kitty`
 - clone this repo into your python's `site-packages` directory
 - clone this repo and add the parent directory to `sys.path` or `$PYTHONPATH`

Then, configure matplotlib to use the module by either setting the
environment variable `MPLBACKEND` to `module://matplotlib-backend-kitty`
or by initializing matplotlib as follows.

```python
import matplotlib
matplotlib.use('module://matplotlib-backend-kitty')
import matplotlib.pyplot as plt
```

If you've installed this module correctly, you can now use
the following sample code to draw a plot in your terminal.

```
$ export MPLBACKEND='module://matplotlib-backend-kitty'
$ python
>>> n = 10000
>>> df = pd.DataFrame({'x': np.random.randn(n),
                       'y': np.random.randn(n)})
>>> df.plot.hexbin(x='x', y='y', gridsize=20)
<plot is shown>
```

If you set your matplotlib to interactive mode via
`matplotlib.pyplot.ion()` or by running an interactive python
shell like `python` or `ipython`, non-empty figures are drawn on
construction where possible. This allows you to use pandas'
`plot()` calls directly, without calling `plt.show()`, and still
enables you to manually construct and `plt.show()`.

If your matplotlib is in non-interactive mode,
you can construct your figures as usual, and then call
`plt.show()` to render them to your terminal. This
works both from scripts and from a repl.

Figures are resized to the size of your terminal by default.
If you'd rather control the sizing of figures manually,
set the `MPLBACKEND_KITTY_SIZING` environment variable to `manual`.

Internally, this backend is somewhat based on matplotlib's
IPython support: it's a hybrid of image and GUI backend types.
It works by using matplotlib's `Agg` backend to render the
plot, and then calls kitty's `icat` to place the rendered
image on your terminal. This means that plotting works as
expected, but the image drawn to your terminal isn't
interactive and animations aren't supported.


================================================
FILE: matplotlib-backend-kitty/__init__.py
================================================
# SPDX-License-Identifier: CC0-1.0

import os
import sys

from io import BytesIO
from subprocess import run

from matplotlib import interactive, is_interactive
from matplotlib._pylab_helpers import Gcf
from matplotlib.backend_bases import (_Backend, FigureManagerBase)
from matplotlib.backends.backend_agg import FigureCanvasAgg


# XXX heuristic for interactive repl
if hasattr(sys, 'ps1') or sys.flags.interactive:
    interactive(True)


class FigureManagerICat(FigureManagerBase):

    @classmethod
    def _run(cls, *cmd):
        def f(*args, output=True, **kwargs):
            if output:
                kwargs['capture_output'] = True
                kwargs['text'] = True
            r = run(cmd + args, **kwargs)
            if output:
                return r.stdout.rstrip()
        return f

    def show(self):

        icat = __class__._run('kitty', '+kitten', 'icat')

        if os.environ.get('MPLBACKEND_KITTY_SIZING', 'automatic') != 'manual':

            tput = __class__._run('tput')

            # gather terminal dimensions
            rows = int(tput('lines'))
            px = icat('--print-window-size')
            px = list(map(int, px.split('x')))

            # account for post-display prompt scrolling
            # 3 line shift for [\n, <matplotlib.axes…, >>>] after the figure
            px[1] -= int(3*(px[1]/rows))

            # resize figure to terminal size & aspect ratio
            dpi = self.canvas.figure.dpi
            self.canvas.figure.set_size_inches((px[0] / dpi, px[1] / dpi))

        with BytesIO() as buf:
            self.canvas.figure.savefig(buf, format='png')
            icat('--align', 'left', output=False, input=buf.getbuffer())


class FigureCanvasICat(FigureCanvasAgg):
    manager_class = FigureManagerICat


@_Backend.export
class _BackendICatAgg(_Backend):

    FigureCanvas = FigureCanvasICat
    FigureManager = FigureManagerICat

    # Noop function instead of None signals that
    # this is an "interactive" backend
    mainloop = lambda: None

    # XXX: `draw_if_interactive` isn't really intended for
    # on-shot rendering. We run the risk of being called
    # on a figure that isn't completely rendered yet, so
    # we skip draw calls for figures that we detect as
    # not being fully initialized yet. Our heuristic for
    # that is the presence of axes on the figure.
    @classmethod
    def draw_if_interactive(cls):
        manager = Gcf.get_active()
        if is_interactive() and manager.canvas.figure.get_axes():
            cls.show()

    @classmethod
    def show(cls, *args, **kwargs):
        _Backend.show(*args, **kwargs)
        Gcf.destroy_all()


================================================
FILE: pyproject.toml
================================================
[project]
name = "matplotlib-backend-kitty"
version = "2.1.2"
description = "show matplotlib plots directly in your kitty terminal"
readme = "README.md"
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
    "Framework :: Matplotlib",
    "Topic :: Terminals",
]
dependencies = [ "matplotlib" ]

[project.urls]
"Homepage" = "https://github.com/jktr/matplotlib-backend-kitty"
"Bug Tracker" = "https://github.com/jktr/matplotlib-backend-kitty/issues"

[build-system]
requires = [ "setuptools>=61.0" ]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
where = [ "." ]