diff --git a/.travis.yml b/.travis.yml index 3a8d857f1..cd27155d3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -88,7 +88,7 @@ jobs: env: HV_DOC_HTML="true" DESC="docs" OPTS="-o doc" script: - datashader fetch-data --path=examples --force - - nbsite generate-rst --org pyviz --project-name datashader --skip '.*tiling.*,.*streaming-aggregation.*' + - nbsite generate-rst --org holoviz --project-name datashader --skip '.*tiling.*,.*streaming-aggregation.*' - nbsite build --what=html --output=builtdocs deploy: provider: pages diff --git a/README.md b/README.md index 7a9a77ebc..e42a2d8fd 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,11 @@ | | | | --- | --- | -| Build Status | [![Linux/MacOS Build Status](https://travis-ci.org/holoviz/datashader.svg?branch=master)](https://travis-ci.org/holoviz/datashader) [![Windows Build status](https://img.shields.io/appveyor/ci/pyviz/datashader/master.svg?logo=appveyor)](https://ci.appveyor.com/project/pyviz/datashader/branch/master) | +| Build Status | [![Linux/MacOS Build Status](https://travis-ci.org/holoviz/datashader.svg?branch=master)](https://travis-ci.org/holoviz/datashader) [![Windows Build status](https://img.shields.io/appveyor/ci/holoviz-developers/datashader/master.svg?logo=appveyor)](https://ci.appveyor.com/project/holoviz-developers/datashader/branch/master) | | Coverage | [![codecov](https://codecov.io/gh/holoviz/datashader/branch/master/graph/badge.svg)](https://codecov.io/gh/holoviz/datashader) | | Latest dev release | [![Github tag](https://img.shields.io/github/tag/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/tags) | | Latest release | [![Github release](https://img.shields.io/github/release/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/releases) [![PyPI version](https://img.shields.io/pypi/v/datashader.svg?colorB=cc77dd)](https://pypi.python.org/pypi/datashader) [![datashader version](https://img.shields.io/conda/v/pyviz/datashader.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/datashader) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/datashader.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/datashader) [![defaults version](https://img.shields.io/conda/v/anaconda/datashader.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/datashader) | -| Docs | [![site](https://img.shields.io/website-up-down-green-red/http/datashader.org.svg)](http://datashader.org) | +| Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/datashader/gh-pages.svg)](https://github.com/holoviz/datashader/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/datashader.org.svg)](http://datashader.org) | ## What is it? @@ -117,8 +117,10 @@ including API documentation and papers and talks about the approach. ![NYC taxi](examples/assets/images/nyc_pickups_vs_dropoffs.jpg) -## About PyViz +## About HoloViz -Datashader is part of the PyViz initiative for making Python-based visualization tools work well together. -See [pyviz.org](http://pyviz.org) for related packages that you can use with Datashader and -[status.pyviz.org](http://status.pyviz.org) for the current status of each PyViz project. +Datashader is part of the [HoloViz](https://holoviz.org) ecosystem for making +browser-based data visualization in Python easier to use, easier to learn, and more powerful. See [holoviz.org](http://holoviz.org) for related packages that you can use with Datashader and +[status.holoviz.org](http://status.holoviz.org) for the current status of each HoloViz project. + +Datashader is supported and maintained by [Anaconda](https://anaconda.com). diff --git a/ROADMAP.md b/ROADMAP.md index c6eaa27e4..93a745a2c 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -1,8 +1,8 @@ # Datashader Roadmap, as of 4/2018 -Datashader is an open-source project, with contributions from a variety of developers with different priorities, so it is not possible to lay out a fully detailed timeline of upcoming features. That said, there are clear priorities that the current developers have agreed on, which will be described here and updated occasionally. +Datashader is an open-source project, with contributions from a variety of developers with different priorities, so it is not possible to lay out a fully detailed timeline of upcoming features. That said, there are clear priorities that the current developers have agreed on, which will be described here and updated occasionally. -If you need any of the functionality listed below and want to help make it a priority, please respond to the relevant issue listed (preferably with offers of coding, financial, or other assistance!). +If you need any of the functionality listed below and want to help make it a priority, please respond to the relevant issue listed (preferably with offers of coding, financial, or other assistance!). 1. **Ongoing maintenance, improved documentation and examples** - As always, there are various bugs and usability issues reported on the issue tracker, and we will address these as time permits. @@ -30,6 +30,3 @@ If you need any of the functionality listed below and want to help make it a pri - [#92](../../issues/92) Box select support - [#61](../../issues/61) Add information on requirements for osm example - [#242](../../issues/242) Spatiotemporal data animation - - -Also see the [PyViz.org roadmap](http://pyviz.org/roadmap.html). diff --git a/datashader/tests/test_pandas.py b/datashader/tests/test_pandas.py index 02a0fcc57..85739597c 100644 --- a/datashader/tests/test_pandas.py +++ b/datashader/tests/test_pandas.py @@ -652,7 +652,7 @@ def test_trimesh_agg_api(): def test_bug_570(): - # See https://github.com/pyviz/datashader/issues/570 + # See https://github.com/holoviz/datashader/issues/570 df = pd.DataFrame({ 'Time': [1456353642.2053893, 1456353642.2917893], 'data': [-59.4948743433377, 506.4847376716022], diff --git a/doc/README.md b/doc/README.md deleted file mode 100644 index f039b6d7e..000000000 --- a/doc/README.md +++ /dev/null @@ -1,35 +0,0 @@ -See https://pyviz.github.io/nbsite/Usage.html for more details. - -First, set up environment so you can run the examples and build docs: - -``` -$ conda install -c pyviz/label/dev pyctdev # if you don't already have pyctdev -$ doit env_create -c pyviz/label/dev -c conda-forge --python=3.6 --name=dsdocs -$ conda activate dsdocs -$ doit develop_install -c pyviz/label/dev -c defaults -c conda-forge -o doc -``` - -WARNING: when you run `develop_install` above, which uses python/pip -to do a develop install, if you have data in your examples/data -directory, it will be copied around at least one time :( So -temporarily move it away before doing a develop install, then restore -it afterwards. (This applies to doing a pip editable install any time, -not just for the documentation; see -https://github.com/pyviz/pyct/issues/22) - -(optional) Building the docs does not check the notebooks run without errors (you have to watch out for tracebacks flying by). Building the docs also runs the notebooks with modifications (e.g. setting backend options). If you want to be sure all the notebooks run normally without exception, execute `doit test_examples_extra`. (Requires running the notebooks twice; this is future work for nbsite.) - -**TODO:** At this time (2019-02-05) thumb-nailing is being done by hand. - -Build the docs: - -1. Build site: `doit build_website` - -2. Inspect result: `cd builtdocs && python -m http.server` then `open http://0.0.0.0:8000` - -3. Edit notebooks as desired and repeat steps 1 and 2 as required. Unedited notebooks will not be re-run. - -4. Deploy to S3 bucket: `doit release_website` - note this can also be done using the AWS UI, just be sure to make it public - -**NOTE:** To see what is going on behind the scenes with these doit commands, inspect dodo.py. -It is recommended that if you do something different, you edit the command in that file. diff --git a/doc/about.rst b/doc/about.rst index 1c3f44ef5..4f7c17559 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -1,8 +1,9 @@ About Us ======== -Datashader is part of the PyViz initiative for making Python-based visualization tools work well together. -See `pyviz.org `_ for related packages that you can use with Datashader and -`status.pyviz.org `_ for the current status of each PyViz project. +Datashader is part of the `HoloViz `_ ecosystem for making +browser-based data visualization in Python easier to use, easier to learn, and more powerful. +See `holoviz.org `_ for related packages that you can use with Datashader and +`status.holoviz.org `_ for the current status of each HoloViz project. -Datashader is supported and mantained by `Anaconda `_. \ No newline at end of file +Datashader is supported and maintained by `Anaconda `_. diff --git a/doc/conf.py b/doc/conf.py index d5b1d4908..15fb70eee 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -11,9 +11,9 @@ version = release = __version__ html_static_path += ['_static'] -html_theme = 'sphinx_pyviz_theme' +html_theme = 'sphinx_holoviz_theme' html_theme_options = { - 'logo': 'logo_horizontal.png', + 'logo': 'logo_horizontal.svg', 'include_logo_text': False, 'favicon': 'favicon.ico', 'primary_color': '#774c9a', @@ -35,15 +35,15 @@ 'PROJECT': project, 'DESCRIPTION': description, 'AUTHOR': authors, - 'WEBSITE_SERVER': 'http://datashader.org', + 'WEBSITE_SERVER': 'https://datashader.org', 'VERSION': version, 'NAV': _NAV, 'LINKS': _NAV, 'SOCIAL': ( - ('Github', 'https://github.com/bokeh/datashader/'), + ('Github', 'https://github.com/holoviz/datashader/'), ('Twitter', 'https://twitter.com/datashader/'), ('Gitter', 'https://gitter.im/pyviz/pyviz'), - ('PyViz', 'http://pyviz.org'), + ('HoloViz', 'https://holoviz.org'), ) }) diff --git a/doc/getting_started/index.rst b/doc/getting_started/index.rst index cfaa7c4bd..6cd78f473 100644 --- a/doc/getting_started/index.rst +++ b/doc/getting_started/index.rst @@ -1,8 +1,3 @@ -.. - Originally generated by nbsite (0.5.2): - /Users/jsignell/conda/envs/earthml/envs/dsdocs/bin/nbsite generate-rst --org pyviz --project-name datashader --skip .*tiling.* - Will not subsequently be overwritten by nbsite, so can be edited. - *************** Getting Started *************** @@ -71,7 +66,7 @@ Developer Instructions 2. Clone the datashader git repository if you do not already have it:: - git clone git://github.com/pyviz/datashader.git + git clone git://github.com/holoviz/datashader.git 3. Set up a new conda environment with all of the dependencies needed to run the examples:: diff --git a/examples/getting_started/2_Pipeline.ipynb b/examples/getting_started/2_Pipeline.ipynb index e39da20e1..e07d6bde7 100644 --- a/examples/getting_started/2_Pipeline.ipynb +++ b/examples/getting_started/2_Pipeline.ipynb @@ -264,7 +264,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Currently only counts are supported for categories, but other reduction operators can be implemented as well (a [to-do item](https://github.com/pyviz/datashader/issues/140)).\n", + "Currently only counts are supported for categories, but other reduction operators can be implemented as well (a [to-do item](https://github.com/holoviz/datashader/issues/140)).\n", "\n", "You can then select a specific category or subset of them for further processing, where `.sum(dim='cat')` will collapse across such a subset to give a single aggregate array:" ] @@ -513,7 +513,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See [the API docs](http://datashader.readthedocs.org/en/latest/api.html#transfer-functions) for more details. Image composition operators to provide for the `how` argument of `tf.stack` (e.g. `over` (default), `source`, `add`, and `saturate`) are listed in [composite.py](https://raw.githubusercontent.com/pyviz/datashader/master/datashader/composite.py) and illustrated [here](http://cairographics.org/operators).\n", + "See [the API docs](https://datashader.org/api.html#transfer-functions) for more details. Image composition operators to provide for the `how` argument of `tf.stack` (e.g. `over` (default), `source`, `add`, and `saturate`) are listed in [composite.py](https://raw.githubusercontent.com/holoviz/datashader/master/datashader/composite.py) and illustrated [here](http://cairographics.org/operators).\n", "\n", "## Moving on\n", "\n", diff --git a/examples/getting_started/3_Interactivity.ipynb b/examples/getting_started/3_Interactivity.ipynb index 908521c54..2717d6588 100644 --- a/examples/getting_started/3_Interactivity.ipynb +++ b/examples/getting_started/3_Interactivity.ipynb @@ -6,7 +6,7 @@ "source": [ "The [previous notebook](2-Pipeline.ipynb) showed all the steps required to get a Datashader rendering of your dataset, yielding raster images displayed using [Jupyter](http://jupyter.org)'s \"rich display\" support. However, these bare images do not show the data ranges or axis labels, making them difficult to interpret. Moreover, they are only static images, and datasets often need to be explored at multiple scales, which is much easier to do in an interactive program. \n", "\n", - "To get axes and interactivity, the images generated by Datashader need to be embedded into a plot using an external library like [Matplotlib](http://matplotlib.org) or [Bokeh](http://bokeh.pydata.org). As we illustrate below, the most convenient way to make Datashader plots using these libraries is via the [HoloViews](http://holoviews.org) high-level data-science API. Plotly also includes Datashader support for Plotly, and native Datashader support for Matplotlib has been [sketched](https://github.com/pyviz/datashader/pull/200) but is not yet released.\n" + "To get axes and interactivity, the images generated by Datashader need to be embedded into a plot using an external library like [Matplotlib](http://matplotlib.org) or [Bokeh](http://bokeh.pydata.org). As we illustrate below, the most convenient way to make Datashader plots using these libraries is via the [HoloViews](http://holoviews.org) high-level data-science API. Plotly also includes Datashader support for Plotly, and native Datashader support for Matplotlib has been [sketched](https://github.com/holoviz/datashader/pull/200) but is not yet released.\n" ] }, { @@ -15,7 +15,7 @@ "source": [ "# Embedding Datashader with HoloViews\n", "\n", - "[HoloViews](http://holoviews.org) (1.7 and later) is a high-level data analysis and visualization library that makes it simple to generate interactive [Datashader](https://github.com/pyviz/datashader)-based plots. Here's an illustration of how this all fits together when using HoloViews+[Bokeh](http://bokeh.pydata.org):\n", + "[HoloViews](http://holoviews.org) (1.7 and later) is a high-level data analysis and visualization library that makes it simple to generate interactive [Datashader](https://github.com/holoviz/datashader)-based plots. Here's an illustration of how this all fits together when using HoloViews+[Bokeh](http://bokeh.pydata.org):\n", "\n", " ![Datashader+Holoviews+Bokeh](../assets/images/ds_hv_bokeh.png)\n", "\n", diff --git a/examples/index.ipynb b/examples/index.ipynb index 804618254..86c97cb97 100644 --- a/examples/index.ipynb +++ b/examples/index.ipynb @@ -14,8 +14,8 @@ "relationships of interest in a principled way.\n", "\n", "The computation-intensive steps in this process are written in Python\n", - "but transparently compiled to machine code using [Numba](http://numba.pydata.org) and flexibly\n", - "distributed across cores and processors using [Dask](http://dask.pydata.org), providing a\n", + "but transparently compiled to machine code using [Numba](https://numba.pydata.org) and flexibly\n", + "distributed across cores and processors using [Dask](https://dask.org), providing a\n", "highly optimized rendering pipeline that makes it practical to work\n", "with extremely large datasets even on standard hardware.\n", "\n", @@ -58,8 +58,8 @@ "\n", "## Installation\n", "\n", - "Please follow the instructions on [Getting Started](http://datashader.org/getting_started)\n", - "if you want to reproduce the specific examples on this website, or follow the instructions at [PyViz.org](http://pyviz.org) if you want to try out Datashader together with related plotting tools.\n", + "Please follow the instructions on [Getting Started](https://datashader.org/getting_started)\n", + "if you want to reproduce the specific examples on this website, or follow the instructions at [HoloViz.org](https://holoviz.org) if you want to try out Datashader together with related plotting tools.\n", "\n", "\n", "\n", @@ -73,7 +73,7 @@ "Some of the original ideas for datashader were developed under the\n", "name Abstract Rendering, which is described in a [2014 SPIE VDA paper](http://spie.org/Publications/Proceedings/Paper/10.1117/12.2041200).\n", "\n", - "The source code for datashader is maintained at our [Github site,](https://github.com/pyviz/datashader) and\n", + "The source code for datashader is maintained on [Github](https://github.com/holoviz/datashader), and\n", "is documented using the API link on this page.\n", "\n", "We recommend the [Getting Started Guide](getting_started) to learn\n", @@ -85,7 +85,7 @@ "Datashader, but the same information is available more conveniently via\n", "the `help()` command as needed when using each component.\n", "\n", - "Please feel free to report [issues](https://github.com/pyviz/datashader/issues) or [contribute code](https://help.github.com/articles/about-pull-requests). You are also welcome to chat with the developers on [gitter](https://gitter.im/pyviz/pyviz).\n" + "Please feel free to report [issues](https://github.com/holoviz/datashader/issues) or [contribute code](https://help.github.com/articles/about-pull-requests). You are also welcome to chat with the developers on [gitter](https://gitter.im/pyviz/pyviz).\n" ] } ], diff --git a/examples/user_guide/10_Performance.ipynb b/examples/user_guide/10_Performance.ipynb index 782780e87..9d5155c7b 100644 --- a/examples/user_guide/10_Performance.ipynb +++ b/examples/user_guide/10_Performance.ipynb @@ -72,8 +72,8 @@ ">>> agg = cvs.points(dask_df, ...)\n", "```\n", "\n", - " [Benchmarking]: https://github.com/pyviz/datashader/issues/313\n", - " [testing with various file formats]: https://github.com/pyviz/datashader/issues/129\n", + " [Benchmarking]: https://github.com/holoviz/datashader/issues/313\n", + " [testing with various file formats]: https://github.com/holoviz/datashader/issues/129\n", " [Apache Parquet]: https://parquet.apache.org/\n", " [fastparquet]: https://github.com/dask/fastparquet" ] diff --git a/examples/user_guide/1_Plotting_Pitfalls.ipynb b/examples/user_guide/1_Plotting_Pitfalls.ipynb index 2184eafad..956179e59 100644 --- a/examples/user_guide/1_Plotting_Pitfalls.ipynb +++ b/examples/user_guide/1_Plotting_Pitfalls.ipynb @@ -19,7 +19,7 @@ "\n", "You can [skip to the end](#Summary) if you just want to see an illustration of these problems.\n", "\n", - "This notebook requires [HoloViews](http://holoviews.org), [colorcet](https://github.com/pyviz/colorcet), and matplotlib, and optionally scikit-image, which can be installed with:\n", + "This notebook requires [HoloViews](https://holoviews.org), [colorcet](https://colorcet.pyviz.org), and matplotlib, and optionally scikit-image, which can be installed with:\n", "\n", "```\n", "conda install holoviews colorcet matplotlib scikit-image\n", @@ -380,7 +380,7 @@ "\n", "![hot](../assets/images/hot.png) ![jet](../assets/images/jet.png)\n", "\n", - "In this image, a good colormap would have \"teeth\" equally visible at all data values, as for the perceptually uniform equivalents from the [colorcet](https://github.com/pyviz/colorcet) package:\n", + "In this image, a good colormap would have \"teeth\" equally visible at all data values, as for the perceptually uniform equivalents from the [colorcet](https://colorcet.pyviz.org) package:\n", "\n", "![fire](../assets/images/fire.png) ![rainbow](../assets/images/rainbow.png)\n", "\n", @@ -400,7 +400,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Comparing **A ** to **B **it should be clear that the \"fire\" colormap is revealing much more of the data, accurately rendering the density differences between each of the different blobs. The unsuitable \"hot\" colormap is mapping all of the high density regions to perceptually indistinguishable shades of bright yellow/white, giving an \"oversaturated\" appearance even though we know the underlying heatmap array is *not* oversaturated (by construction). Luckily it is easy to avoid this problem; just use a perceptually uniform colormap like one of the 60 available in the [colorcet](https://github.com/pyviz/colorcet) package, one of the four shipped with matplotlib [(viridis, plasma, inferno, or magma)](https://bids.github.io/colormap), or the Parula colormap shipped with Matlab.\n", + "Comparing **A ** to **B **it should be clear that the \"fire\" colormap is revealing much more of the data, accurately rendering the density differences between each of the different blobs. The unsuitable \"hot\" colormap is mapping all of the high density regions to perceptually indistinguishable shades of bright yellow/white, giving an \"oversaturated\" appearance even though we know the underlying heatmap array is *not* oversaturated (by construction). Luckily it is easy to avoid this problem; just use a perceptually uniform colormap like one of the 60 available in the [colorcet](https://colorcet.pyviz.org) package, one of the four shipped with matplotlib [(viridis, plasma, inferno, or magma)](https://bids.github.io/colormap), or the Parula colormap shipped with Matlab.\n", "\n", "\n", "## Summary\n", @@ -452,9 +452,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### [Datashader](https://github.com/pyviz/datashader)\n", + "### [Datashader](https://github.com/holoviz/datashader)\n", "\n", - "The steps above show how to avoid the six main plotting pitfalls by hand, but it can be awkward and relatively slow to do so. Luckily there is a new Python library available to automate and optimize these steps, named [Datashader](https://github.com/bokeh/datashader). Datashader avoids users having to make dataset-dependent decisions and parameter settings when visualizing a new dataset. Datashader makes it practical to create accurate visualizations of datasets too large to understand directly, up to a billion points on a normal laptop and larger datasets on a compute cluster. As a simple teaser, the above steps can be expressed very concisely using the Datashader interface provided by [HoloViews](http://holoviews.org):" + "The steps above show how to avoid the six main plotting pitfalls by hand, but it can be awkward and relatively slow to do so. Luckily there is a new Python library available to automate and optimize these steps, named [Datashader](https://github.com/bokeh/datashader). Datashader avoids users having to make dataset-dependent decisions and parameter settings when visualizing a new dataset. Datashader makes it practical to create accurate visualizations of datasets too large to understand directly, up to a billion points on a normal laptop and larger datasets on a compute cluster. As a simple teaser, the above steps can be expressed very concisely using the Datashader interface provided by [HoloViews](https://holoviews.org):" ] }, { @@ -488,7 +488,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See the [Datashader web site](https://raw.githubusercontent.com/pyviz/datashader/master/examples/README.md) for details and examples to help you get started." + "See the [Datashader web site](https://datashader.org) for details and examples to help you get started." ] } ], diff --git a/examples/user_guide/9_Extending.ipynb b/examples/user_guide/9_Extending.ipynb index 4b6109de6..35c61125a 100644 --- a/examples/user_guide/9_Extending.ipynb +++ b/examples/user_guide/9_Extending.ipynb @@ -8,7 +8,7 @@ "\n", "![pipeline diagram](../assets/images/pipeline2.png)\n", "\n", - "This document contains brief notes on how to extend or replace each of these stages, following the organization of the [Pipeline section](../getting_started/02_Pipeline.ipynb) of the documentation. For the full details, you can study the [Datashader source code](https://github.com/pyviz/datashader).\n", + "This document contains brief notes on how to extend or replace each of these stages, following the organization of the [Pipeline section](../getting_started/02_Pipeline.ipynb) of the documentation. For the full details, you can study the [Datashader source code](https://github.com/holoviz/datashader).\n", "\n", "## Data\n", "\n", diff --git a/setup.py b/setup.py index ee6fd8e0a..3d539e02e 100644 --- a/setup.py +++ b/setup.py @@ -65,7 +65,7 @@ extras_require['doc'] = extras_require['examples_extra'] + [ 'nbsite >=0.5.2', - 'sphinx_pyviz_theme', + 'sphinx_holoviz_theme', 'tornado <6.0', 'numpydoc' ] diff --git a/tox.ini b/tox.ini index 972095b42..95f3cd958 100644 --- a/tox.ini +++ b/tox.ini @@ -1,4 +1,4 @@ -# For use with pyct (https://github.com/pyviz/pyct), but just standard +# For use with pyct (https://github.com/pyviz-dev/pyct), but just standard # tox config (works with tox alone). [tox]