Metadata-Version: 1.1
Name: tensorflow-plot
Version: 0.2.0
Summary: TensorFlow Plot
Home-page: https://github.com/wookayin/tensorflow-plot
Author: Jongwook Choi
Author-email: wookayin@gmail.com
License: MIT License
Description: TensorFlow Plot
        ===============
        
        [![pypi](https://img.shields.io/pypi/v/tfplot.svg?maxAge=86400)][pypi_tfplot]
        [![Documentation Status](https://readthedocs.org/projects/tensorflow-plot/badge/?version=latest)][documentation]
        [![Build Status](https://travis-ci.org/wookayin/tensorflow-plot.svg?branch=master)](https://travis-ci.org/wookayin/tensorflow-plot)
        
        A [TensorFlow][tensorflow] utility for providing matplotlib-based **plot** operations
        — [TensorBoard][tensorboard] ❤️ [Matplotlib][matplotlib].
        
        <p align="center">
        <i> 🚧 Under Construction —  API might change!</i>
        </p>
        
        It allows us to draw **_any_** [matplotlib][matplotlib] plots or figures into images,
        as a part of TensorFlow computation graph.
        Especially, we can easily any plot and see the result image
        as an image summary in [TensorBoard][tensorboard].
        
        <p align="center">
        <img src="./assets/tensorboard-plot-summary.png" width="70%" />
        </p>
        
        Quick Overview
        --------------
        
        We can wrap *any* pre-existing functions for plotting, e.g.,
        [`seaborn.heatmap`](http://seaborn.pydata.org/generated/seaborn.heatmap.html) or [matplotlib `Axes`](https://matplotlib.org/api/axes_api.html),
        as a Tensorflow op:
        
        ```python tfplot
        import tfplot
        import seaborn.apionly as sns
        
        tf_heatmap = tfplot.wrap_axesplot(sns.heatmap, figsize=(4, 4), batch=True)
        tf.summary.image("attention_maps", tf_heatmap(attention_maps))
        ```
        
        Alternatively, if you need more flexibility on plots,
        just define a python function that takes `numpy.ndarray` values as input,
        draw a plot, and return it as a `matplotlib.figure.Figure` object.
        Then, `tfplot.plot()` will wrap this function as a TensorFlow operation,
        which will produce a RGB-A image tensor `[height, width, 4]` containing the resulting plot.
        
        ```python
        def figure_heatmap(heatmap, cmap='jet'):
            # draw a heatmap with a colorbar
            fig, ax = tfplot.subplots(figsize=(4, 3))
            im = ax.imshow(heatmap, cmap=cmap)
            fig.colorbar(im)
            return fig
        
        # heatmap_tensor : a float32 Tensor of shape [16, 16], for example
        plot_op = tfplot.plot(figure_heatmap, [heatmap_tensor], cmap='jet')
        
        # Or just directly add an image summary with the plot
        tfplot.summary.plot("heatmap_summary", figure_heatmap, [heatmap_tensor])
        ```
        
        Please take a look at the
        [the showcase][examples-showcase] or [examples directory][examples-dir]
        for more examples and use cases.
        
        [The full documentation][documentation] including API docs, can be found at [readthedocs][documentation].
        
        
        
        Installation
        ------------
        
        ```
        pip install tensorflow-plot
        ```
        
        To grab the latest development version:
        
        ```
        pip install git+https://github.com/wookayin/tensorflow-plot.git@master
        ```
        
        Note
        ----
        
        ### Some comments
        
        Matplotlib operations can be *very* slow as Matplotlib runs in python, so please be aware of runtime performance.
        There is still a room for improvement, which will be added sometimes later.
        
        Moreover, it might be also a good idea to draw plots from the main code (rather than having a TF op) and add them as image summaries.
        Please use this library with your best discernment.
        
        ### Thread-safety issue
        
        Please use **object-oriented** matplotlib APIs (e.g. `Figure`, `AxesSubplot`)
        instead of [pyplot] APIs (i.e. `matplotlib.pyplot` or `plt.XXX()`)
        when creating and drawing plots.
        This is because [pyplot] APIs are not *thread-safe*,
        while the TensorFlow plot operations are usually executed in multi-threaded manners.
        
        For example, avoid any use of `pyplot` (or `plt`):
        
        ```python
        # DON'T DO LIKE THIS !!!
        def figure_heatmap(heatmap):
            fig = plt.figure()
            plt.imshow(heatmap)
            return fig
        ```
        
        and do it like:
        
        ```python
        def figure_heatmap(heatmap):
            fig = matplotlib.figure.Figure()   # or just `fig = tfplot.Figure()`
            ax = fig.add_subplot(1, 1, 1)      # ax: AxesSubplot
            # or, just `fig, ax = tfplot.subplots()`
            ax.imshow(heatmap)
            return fig                         # fig: Figure
        ```
        
        For example, `tfplot.subplots()` is a good replacement for `plt.subplots()`
        to use inside plot functions.
        
        
        [pypi_tfplot]: https://pypi.python.org/pypi/tfplot
        [matplotlib]: http://matplotlib.org/
        [tensorflow]: https://www.tensorflow.org/
        [tensorboard]: https://www.tensorflow.org/get_started/summaries_and_tensorboard
        [pyplot]: http://matplotlib.org/api/pyplot_api.html
        [examples-dir]: https://github.com/wookayin/tensorflow-plot/blob/master/examples/
        [examples-showcase]: https://github.com/wookayin/tensorflow-plot/blob/master/examples/showcases.ipynb
        [documentation]: http://tensorflow-plot.readthedocs.io/en/latest/
        
        
        License
        -------
        
        [MIT License](LICENSE) © Jongwook Choi
        
Keywords: tensorflow matplotlib tensorboard plot tfplot
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
