Updated to use pyct, pyctdev, nbsite (#19)

* Updated packaging, testing, docs to match standard pyviz.
* Allows release on tag, website building, etc.
* Expanded the website, with notebooks, favicon, etc.
* Added license
* Added unit tests

.appveyor.yml

@@ -0,0 +1,20 @@
+environment:
+  global:
+    CHANS_DEV: "-c pyviz/label/dev"
+  matrix:
+    - PY: "3.6"
+      PYTHON: "C:\\Python36-x64"
+      CONDA: "C:\\Miniconda36-x64"
+
+install:
+  - "SET PATH=%CONDA%;%CONDA%\\Scripts;%PATH%"
+  - "conda install -y %CHANS_DEV% pyctdev && doit ecosystem_setup"
+  - "doit env_create --name=colorcet --python=%PY%"
+  - "activate colorcet"
+  - "doit develop_install %CHANS_DEV% -o examples"
+  - "doit env_capture"
+
+build: off
+
+test_script:
+  - "doit test_all"

.gitattributes

@@ -0,0 +1,2 @@
+colorcet/__init__.py export-subst
+setup.cfg export-subst

.gitignore

@@ -1,5 +1,4 @@
 *.py[cod]
-#*#
 *~
 *.egg
 *.egg-info
@@ -14,3 +13,18 @@
 !/doc/Reference_Manual/index.rst
 .ipynb_checkpoints
 .coverage
+__pycache__
+.doit*
+*.egg-info
+*/.version
+pip-wheel-metadata
+**.ipynb_checkpoints
+
+# nbsite
+# these files normally shouldn't be checked in as they should be
+# dynamically built from notebooks
+doc/**/*.rst
+doc/**/*.ipynb
+doc/**/*.json
+# this dir contains the whole website and should not be checked in on master
+builtdocs/

.travis.yml

@@ -0,0 +1,105 @@
+language: generic
+
+env:
+  global:
+    - CHANS_DEV="-c pyviz/label/dev"
+    - CHANS_REL="-c pyviz"
+    - LABELS_DEV="--label dev"
+    - LABELS_REL="--label dev --label main"
+    - PYENV_VERSION=3.6
+    - PKG_TEST_PYTHON="--test-python=py36 --test-python=py27"
+
+stages:
+  - test
+  - name: conda_dev_package
+    if: tag =~ ^v(\d+|\.)+[a-z]\d+$
+  - name: pip_dev_package
+    if: tag =~ ^v(\d+|\.)+[a-z]\d+$
+  - name: conda_package
+    if: tag =~ ^v(\d+|\.)+[^a-z]\d+$
+  - name: pip_package
+    if: tag =~ ^v(\d+|\.)+[^a-z]\d+$
+  - name: website_dev
+    if: tag =~ ^v(\d+|\.)+[a-z]\d+$ OR tag = website_dev
+  - name: website_release
+    if: tag =~ ^v(\d+|\.)+[^a-z]\d+$ OR tag = website
+
+jobs:
+  include:
+    - &default
+      stage: test
+      os: linux
+      install:
+        - pip install pyctdev && doit miniconda_install && pip uninstall -y doit pyctdev
+        - export PATH="$HOME/miniconda/bin:$PATH" && hash -r
+        - conda config --set always_yes True
+        - conda install -c pyviz/label/dev pyctdev && doit ecosystem_setup
+      before_script:
+        - doit env_create --name=colorcet --python=$PYENV_VERSION
+        - source activate colorcet
+        - doit develop_install $CHANS_DEV -o examples
+        - doit env_capture
+      script:
+        - doit test_all
+
+
+    - &website
+      <<: *default
+      stage: website_release
+      script:
+        # TODO: set chans according to dev/rel
+        - doit develop_install $CHANS_DEV -o doc -o examples
+        - doit build_website
+
+      deploy:
+        - provider: pages
+          skip_cleanup: true
+          github_token: $GITHUB_TOKEN
+          local_dir: ./builtdocs
+          fqdn: colorcet.pyviz.org
+          on:
+            tags: true
+            all_branches: true
+
+    - <<: *website
+      stage: website_dev
+      deploy:
+        - provider: pages
+          skip_cleanup: true
+          github_token: $GITHUB_TOKEN
+          local_dir: ./builtdocs
+          repo: pyviz-dev/colorcet
+          on:
+            tags: true
+            all_branches: true
+
+    ## dev packages
+
+    - &pip_pkg
+      env: PYPI=testpypi PYPIUSER=$TPPU PYPIPASS=$TPPP
+      stage: pip_dev_package
+      install: pip install pyctdev && doit ecosystem=pip ecosystem_setup
+      before_script:
+        - doit ecosystem=pip package_build $PKG_TEST_PYTHON --test-group=unit --sdist-install-build-deps
+      script: doit ecosystem=pip package_upload -u $PYPIUSER -p $PYPIPASS --pypi ${PYPI}
+
+    - &conda_pkg
+      <<: *default
+      stage: conda_dev_package
+      env: LABELS=$LABELS_DEV CHANS=$CHANS_DEV
+      before_script:
+        - travis_wait 60 doit package_build $CHANS $PKG_TEST_PYTHON --package-name colorcet --test-group=unit
+      script: doit package_upload --token=$ANACONDA_TOKEN $LABELS
+
+    ## release packages
+
+    - <<: *pip_pkg
+      env: PYPI=pypi PYPIUSER=$PPU PYPIPASS=$PPP
+      stage: pip_package
+
+    - <<: *conda_pkg
+      stage: conda_package
+      env: LABELS=$LABELS_REL CHANS=$CHANS_REL
+
+notifications:
+  email: false

MANIFEST.in

@@ -1 +1,8 @@
 include LICENSE.txt
+include README.md
+include colorcet/.version
+graft examples
+graft colorcet/examples
+global-exclude *.py[co]
+global-exclude *~
+global-exclude *.ipynb_checkpoints/*

README.md

@@ -1,109 +1,45 @@
-colorcet
---------
+<img src="https://github.com/pyviz/colorcet/blob/master/doc/_static/logo.png" width=200><br>
 
-[colorcet](https://github.com/bokeh/colorcet) is a collection of
+-----------------
+
+# Colorcet: Collection of perceptually uniform colormaps
+
+|    |    |
+| --- | --- |
+| Build Status | [![Linux/MacOS Build Status](https://travis-ci.org/pyviz/colorcet.svg?branch=master)](https://travis-ci.org/pyviz/colorcet) [![Windows Build status](https://img.shields.io/appveyor/ci/pyviz/colorcet/master.svg?logo=appveyor)](https://ci.appveyor.com/project/pyviz/colorcet/branch/master) |
+| Latest dev release | [![Github tag](https://img.shields.io/github/tag/pyviz/colorcet.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/colorcet/tags) |
+| Latest release | [![Github release](https://img.shields.io/github/release/pyviz/colorcet.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/colorcet/releases) [![PyPI version](https://img.shields.io/pypi/v/colorcet.svg?colorB=cc77dd)](https://pypi.python.org/pypi/colorcet) [![colorcet version](https://img.shields.io/conda/v/pyviz/colorcet.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/colorcet) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/colorcet.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/colorcet) [![defaults version](https://img.shields.io/conda/v/anaconda/colorcet.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/colorcet) |
+| Docs | [![gh-pages](https://img.shields.io/github/last-commit/pyviz/colorcet/gh-pages.svg)](https://github.com/pyviz/colorcet/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/colorcet.pyviz.org.svg)](http://colorcet.pyviz.org) |
+
+
+## What is it?
+
+Colorcet is a collection of
 perceptually uniform colormaps for use with Python plotting programs like
 [bokeh](http://bokeh.pydata.org),
 [matplotlib](http://matplotlib.org),
 [holoviews](http://holoviews.org), and
-[datashader](https://github.com/bokeh/datashader).
-
-Apart from the four [uniform colormaps now provided with
-matplotlib](http://matplotlib.org/users/colormaps.html), most
-colormaps shipping with Python plotting Python programs are highly
-perceptually nonuniform.  That is, small changes in data values
-result in large changes in the perceptual appearance of the
-corresponding colors, or vice versa.  For instance, the popular matplotlib
-"hot" and "jet" colormaps have long stretches where the apparent
-colors change imperceptibly, such as the yellow region in "hot" and
-the cyan/green region in "jet":
-
-![hot/jet](doc/images/hot_jet.png)     
-
-When colormaps are used for visualizing scientific datasets, these
-perceptual nonlinearities can make interpretation of this data very
-difficult, because false boundaries appear in the data, and genuine
-boundaries and changes can be obscured.
-
-Matplotlib now offers a [tool to construct uniform
-colormaps](https://github.com/matplotlib/viscm), but it doesn't yet
-allow construction of colormaps like those above.  To address this
-need, Peter Kovesi at the Center for Exploration
-Targeting created a set of colormaps that are sampled uniformly in a
-perceptual color space, using methods he describes in a [paper on
-arXiv](https://arxiv.org/abs/1509.03700).  For instance, the
-perceptually uniform versions of the above colormaps are called "fire"
-and "rainbow" in this package:
-
-![fire/rainbow](doc/images/fire_rainbow.png)
-
-You should be able to see the differences right away, revealing more
-detail, more faithfully than if you use non-uniform maps.  For
-instance, here is a population-density dataset (from
-[datashader](https://github.com/bokeh/datashader)) rendered with the
-original matplotlib "hot" colormap:
-
-![census_hot](doc/images/census_hot.png)
-
-and the same dataset rendered with colorcet's "fire" colormap:
-
-![census_fire](doc/images/census_fire.png)
-
-It should be obvious that the "hot" version completely washes out
-detail at the high end, as if the image is overexposed, while "fire"
-makes detail visible throughout the data range.  Yet the qualitative
-effect is still roughly similar, allowing "fire" to be used in nearly
-any situation where the original "hot" was useful.
-
-Peter provides [versions of 50 perceptually uniform colormaps for a
-variety of different plotting programs](http://peterkovesi.com/projects/colourmaps), 
-and this package provides those colormaps ready to use from within Python
-programs.  The colormaps are all illustrated in an 
-[example notebook](https://colorcet.pyviz.org/) that describes the 
-different types available and allows you to test how perceptually
-uniform they are on your particular display device.
-
-Peter's methods differ from those used in Matplotlib's
-uniform colormaps (as implemented in their [viscm
-tool](https://github.com/matplotlib/viscm)), which (apart from using a
-different color model) are designed to satisfy different constraints.
-For instance, mpl's colormaps are always perceptually uniform in their
-monochrome representation, not just their original color
-representation, and are safe for colorblind viewers, neither of which
-are necessarily true of these colormaps.  On the other hand, colormaps
-like "fire" above, i.e., a usable perceptually equivalent of
-matplotlib/matlab's "hot", are not obtainable using viscm in any
-straightforward way, limiting the range of useful colormaps that can be created.
-In any case, this package focuses on making a set of useful colormaps
-readily available from within Python programs, rather than providing
-tools for building novel colormaps, for which see the 
-[viscm tool](https://github.com/matplotlib/viscm) and
-[Peter's original site](http://peterkovesi.com/projects/colourmaps). 
+[datashader](https://github.com/bokeh/datashader) based on the
+set of [perceptually uniform colormaps](https://arxiv.org/abs/1509.03700) created
+by Peter Kovesi at the Center for Exploration Targeting.
 
 
 ## Installation
 
-colorcet is available on most platforms using the `conda` package manager,
-from the `bokeh` channel:
+Colorcet supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows, or Mac
+and can be installed with conda:
 
 ```
-conda install -c bokeh colorcet
+    conda install colorcet
 ```
 
-or by using pip:
+or with pip:
 
 ```
-pip install colorcet
+    pip install colorcet
 ```
 
-Alternatively, you can manually install from the repository if you
-wish to be able to modify the code over time:
-
-```
-git clone https://github.com/bokeh/colorcet.git
-cd colorcet
-python setup.py develop
-```
+For more information take a look at [Getting Started](http://colorcet.pyviz.org/getting_started).
 
 ## Learning more
 
@@ -114,19 +50,25 @@ background is available in a [1996 paper from
 IBM](http://www.research.ibm.com/people/l/lloydt/color/color.HTM).
 
 The matplotlib project also has a number of relevant resources,
-including an excellent 
+including an excellent
 [2015 SciPy talk](https://www.youtube.com/watch?v=xAoljeRJ3lU), the
 [viscm tool for creating maps like the four in mpl](https://github.com/matplotlib/viscm), the
-[cmocean site](http://matplotlib.org/cmocean/) collecting a set of maps created by viscm, 
+[cmocean site](http://matplotlib.org/cmocean/) collecting a set of maps created by viscm,
 and the [discussion of how the mpl maps were created](https://bids.github.io/colormap/).
 
 
-# Samples
+## Samples
 
 All the colorcet colormaps that have short, memorable names (which are probably
 the most useful ones) are visible here:
 
-<img src="doc/images/named.png" width="800">
+<img src="./examples/assets/images/named.png" width="800">
+
+But the complete set of 50+ is shown in the [User Guide](http://colorcet.pyviz.org/user_guide).
+
+
+## About PyViz
 
-But the complete set of 50+ is shown in the
-[example notebook](https://bokeh.github.io/colorcet).
+Colorcet 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 Colorcet and
+[status.pyviz.org](http://status.pyviz.org) for the current status of each PyViz project.

colorcet/__init__.py

@@ -22,9 +22,9 @@
 
 <category>_<huesequence>_<lightnessrange>_c<meanchroma>[_s<colorshift>_[r<ifreversed>]]
 
-but some have shorter, more convenient aliases, some of which are 
+but some have shorter, more convenient aliases, some of which are
 inspired by Matplotlib colormaps of the same name and others
-based on the qualitative appearance.  The colormaps with 
+based on the qualitative appearance.  The colormaps with
 shorter names tend to be the most useful subset, and for
 cases like automatic population of a GUI widget these
 colormaps are provided as a separate subset:
@@ -33,7 +33,8 @@
   cm_n['name'] or cm_n.name
 """
 
-__version__ = '1.0.0'
+import param
+__version__ = str(param.Version(fpath=__file__,archive_commit="$Format:%h$",reponame="colorcet"))
 
 from collections import OrderedDict
 
@@ -56,7 +57,7 @@ def register_cmap(name,cmap): pass
     LinearSegmentedColormap.from_list=lambda n,c,N: None
 
 def rgb_to_hex(r,g,b):
-    return '#%02x%02x%02x' % (r,g,b) 
+    return '#%02x%02x%02x' % (r,g,b)
 
 def bokeh_palette(name,colorlist):
     palette[name] = [rgb_to_hex(int(r*255),int(g*255),int(b*255)) for r,g,b in colorlist]

colorcet/tests/README.md

@@ -0,0 +1,20 @@
+## Matplotlib tests
+
+These tests contain baseline images generated using [pytest-mpl](https://github.com/matplotlib/pytest-mpl).
+To run these tests with the fig checking enabled first install pytest-mpl:
+
+```bash
+pip install pytest-mpl
+```
+
+To regenerate these figures from within this dir run:
+
+```bash
+pytest --mpl-generate-path=baseline
+```
+
+To run the tests checking that the output is as expected run:
+
+```bash
+pytest --mpl
+```

colorcet/tests/test_bokeh.py

@@ -0,0 +1,12 @@
+import pytest  # noqa
+import colorcet as cc
+
+def test_bokeh_palettes_available():
+    assert len(cc.palette.items()) == 72
+    assert len(cc.palette_n.items()) == 21
+
+def test_bokeh_palette_is_a_list():
+    assert isinstance(cc.blues, list)
+    assert len(cc.blues) == 256
+    assert cc.blues[0] == '#f0f0f0'
+    assert cc.blues[-1] == '#3a7bb1'