Codebase list colorcet / af60908e-b981-440c-8384-0a4e120aa744/main
New upstream snapshot. Debian Janitor 2 years ago
166 changed file(s) with 1178 addition(s) and 30495 deletion(s). Raw diff Collapse all Expand all
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.appveyor.yml less more
0 environment:
1 global:
2 CHANS_DEV: "-c pyviz/label/dev"
3 matrix:
4 - PY: "3.6"
5 PYTHON: "C:\\Python36-x64"
6 CONDA: "C:\\Miniconda36-x64"
7
8 install:
9 - "SET PATH=%CONDA%;%CONDA%\\Scripts;%PATH%"
10 - "conda install -y %CHANS_DEV% pyctdev && doit ecosystem_setup"
11 - "doit env_create --name=colorcet --python=%PY%"
12 - "activate colorcet"
13 - "doit develop_install %CHANS_DEV% -o examples"
14 - "doit env_capture"
15
16 build: off
17
18 test_script:
19 - "doit test_all"
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.gitattributes less more
0 colorcet/__init__.py export-subst
1 setup.cfg export-subst
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.gitignore less more
0 *.py[cod]
1 *~
2 *.egg
3 *.egg-info
4 *.swp
5 *.DS_Store
6 *.so
7 *.o
8 *.out
9 *.lock
10 /doc/_build
11 /doc/Reference_Manual/*
12 !/doc/Reference_Manual/index.rst
13 .ipynb_checkpoints
14 .coverage
15 __pycache__
16 .doit*
17 *.egg-info
18 */.version
19 pip-wheel-metadata
20 **.ipynb_checkpoints
21
22 # nbsite
23 # these files normally shouldn't be checked in as they should be
24 # dynamically built from notebooks
25 doc/**/*.rst
26 doc/**/*.ipynb
27 doc/**/*.json
28 # this dir contains the whole website and should not be checked in on master
29 builtdocs/
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.travis.yml less more
0 language: generic
1
2 env:
3 global:
4 - CHANS_DEV="-c pyviz/label/dev"
5 - CHANS_REL="-c pyviz"
6 - LABELS_DEV="--label dev"
7 - LABELS_REL="--label dev --label main"
8 - PYENV_VERSION=3.7
9 - PKG_TEST_PYTHON="--test-python=py37 --test-python=py27"
10
11 stages:
12 - test
13 - name: conda_dev_package
14 if: tag =~ ^v(\d+|\.)+[a-z]\d+$
15 - name: pip_dev_package
16 if: tag =~ ^v(\d+|\.)+[a-z]\d+$
17 - name: conda_package
18 if: tag =~ ^v(\d+|\.)+[^a-z]\d+$
19 - name: pip_package
20 if: tag =~ ^v(\d+|\.)+[^a-z]\d+$
21 - name: website_dev
22 if: tag =~ ^v(\d+|\.)+[a-z]\d+$ OR tag = website_dev
23 - name: website_release
24 if: tag =~ ^v(\d+|\.)+[^a-z]\d+$ OR tag = website
25
26 jobs:
27 include:
28 - &default
29 stage: test
30 os: linux
31 install:
32 - pip install pyctdev && doit miniconda_install && pip uninstall -y doit pyctdev
33 - export PATH="$HOME/miniconda/bin:$PATH" && hash -r
34 - conda config --set always_yes True
35 - conda install -c pyviz pyctdev && doit ecosystem_setup
36 before_script:
37 - doit env_create --name=colorcet --python=$PYENV_VERSION
38 - source activate colorcet
39 - doit develop_install $CHANS_DEV -o examples
40 - doit env_capture
41 script:
42 - doit test_all
43
44
45 - &website
46 <<: *default
47 stage: website_release
48 script:
49 # TODO: set chans according to dev/rel
50 - doit develop_install $CHANS_DEV -o doc -o examples
51 - doit build_website
52
53 deploy:
54 - provider: pages
55 skip_cleanup: true
56 github_token: $GITHUB_TOKEN
57 local_dir: ./builtdocs
58 fqdn: colorcet.pyviz.org
59 on:
60 tags: true
61 all_branches: true
62
63 - <<: *website
64 stage: website_dev
65 deploy:
66 - provider: pages
67 skip_cleanup: true
68 github_token: $GITHUB_TOKEN
69 local_dir: ./builtdocs
70 repo: pyviz-dev/colorcet
71 on:
72 tags: true
73 all_branches: true
74
75 ## dev packages
76
77 - &pip_pkg
78 env: PYPI=testpypi PYPIUSER=$TPPU PYPIPASS=$TPPP
79 stage: pip_dev_package
80 install: pip install pyctdev && doit ecosystem=pip ecosystem_setup
81 before_script:
82 - doit ecosystem=pip package_build $PKG_TEST_PYTHON --test-group=unit --sdist-install-build-deps
83 script: doit ecosystem=pip package_upload -u $PYPIUSER -p $PYPIPASS --pypi ${PYPI}
84
85 - &conda_pkg
86 <<: *default
87 stage: conda_dev_package
88 env: LABELS=$LABELS_DEV CHANS=$CHANS_DEV
89 before_script:
90 - travis_wait 60 doit package_build $CHANS $PKG_TEST_PYTHON --test-group=unit
91 script: doit package_upload --token=$ANACONDA_TOKEN $LABELS
92
93 ## release packages
94
95 - <<: *pip_pkg
96 env: PYPI=pypi PYPIUSER=$PPU PYPIPASS=$PPP
97 stage: pip_package
98
99 - <<: *conda_pkg
100 stage: conda_package
101 env: LABELS=$LABELS_REL CHANS=$CHANS_REL
102
103 notifications:
104 email: false
0 Metadata-Version: 2.1
1 Name: colorcet
2 Version: None
3 Summary: Collection of perceptually uniform colormaps
4 Home-page: https://colorcet.holoviz.org
5 Author: James A. Bednar
6 Author-email: jbednar@anaconda.com
7 Maintainer: James A. Bednar
8 Maintainer-email: jbednar@anaconda.com
9 License: CC-BY License
10 Project-URL: Bug Tracker, http://github.com/holoviz/colorcet/issues
11 Project-URL: Documentation, https://colorcet.holoviz.org
12 Project-URL: Source Code, http://github.com/holoviz/colorcet
13 Description: README.md
14 Platform: UNKNOWN
15 Classifier: License :: OSI Approved
16 Classifier: Operating System :: OS Independent
17 Classifier: Programming Language :: Python
18 Classifier: Programming Language :: Python :: 2.7
19 Classifier: Programming Language :: Python :: 3.6
20 Classifier: Programming Language :: Python :: 3.7
21 Classifier: Programming Language :: Python :: 3.8
22 Classifier: Development Status :: 5 - Production/Stable
23 Requires-Python: >=2.7
24 Provides-Extra: all
25 Provides-Extra: build
26 Provides-Extra: doc
27 Provides-Extra: examples
28 Provides-Extra: tests
29 Provides-Extra: tests_extra
0 <img src="https://github.com/pyviz/colorcet/blob/master/doc/_static/logo.png" width=200><br>
0 <img src="https://github.com/holoviz/colorcet/blob/master/doc/_static/logo.png" width=200><br>
11
22 -----------------
33
55
66 | | |
77 | --- | --- |
8 | 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) |
9 | Latest dev release | [![Github tag](https://img.shields.io/github/tag/pyviz/colorcet.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/colorcet/tags) |
10 | 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) |
11 | 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) |
8 | Build Status | [![Linux/MacOS Build Status](https://github.com/holoviz/colorcet/workflows/tests/badge.svg?query=branch%3Amaster)](https://github.com/holoviz/colorcet/actions/workflows/tests.yaml?query=branch%3Amaster) |
9 | Latest dev release | [![Github tag](https://img.shields.io/github/tag/holoviz/colorcet.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/colorcet/tags) [![dev-site](https://img.shields.io/website-up-down-green-red/https/pyviz-dev.github.io/colorcet.svg?label=dev%20website)](https://pyviz-dev.github.io/colorcet/) |
10 | Latest release | [![Github release](https://img.shields.io/github/release/holoviz/colorcet.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/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) |
11 | Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/colorcet/gh-pages.svg)](https://github.com/holoviz/colorcet/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/colorcet.holoviz.org.svg)](http://colorcet.holoviz.org) |
1212
1313
1414 ## What is it?
6363
6464 If you want to try out the latest features between releases, you can get the latest dev release by specifying -c pyviz/label/dev in place of -c pyviz.
6565
66 For more information take a look at [Getting Started](http://colorcet.pyviz.org/getting_started).
66 For more information take a look at [Getting Started](http://colorcet.holoviz.org/getting_started).
6767
6868 ## Learning more
6969
7373 background is available in a [1996 paper from
7474 IBM](http://www.research.ibm.com/people/l/lloydt/color/color.HTM).
7575
76 The matplotlib project also has a number of relevant resources,
76 The Matplotlib project also has a number of relevant resources,
7777 including an excellent
7878 [2015 SciPy talk](https://www.youtube.com/watch?v=xAoljeRJ3lU), the
7979 [viscm tool for creating maps like the four in mpl](https://github.com/matplotlib/viscm), the
8383
8484 ## Samples
8585
86 All the colorcet colormaps that have short, memorable names (which are probably
86 All the Colorcet colormaps that have short, memorable names (which are probably
8787 the most useful ones) are visible here:
8888
8989 <img src="./examples/assets/images/named.png" width="800">
9090
91 But the complete set of 50+ is shown in the [User Guide](http://colorcet.pyviz.org/user_guide).
92
93
94 ## About PyViz
95
96 Colorcet is part of the PyViz initiative for making Python-based visualization tools work well together.
97 See [pyviz.org](http://pyviz.org) for related packages that you can use with Colorcet and
98 [status.pyviz.org](http://status.pyviz.org) for the current status of each PyViz project.
91 But the complete set of 50+ is shown in the [User Guide](http://colorcet.holoviz.org/user_guide).
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assets/CET_to_py.py less more
0 """
1 Generate Python versions for each of the colormaps provided in
2 http://peterkovesi.com/projects/colourmaps/CETperceptual_csv_0_1.zip
3
4 Also adds Glasbey colormaps created using: https://github.com/taketwo/glasbey
5 see https://github.com/pyviz/colorcet/issues/11 for more details
6 """
7
8 import os, os.path, csv
9
10 paths = ['CETperceptual_csv_0_1', 'CETperceptual_csv_0_1_v2', 'Glasbey']
11 output_file = '../colorcet/__init__.py'
12 header = '''\
13 """
14 Python versions of the 256-color colormaps provided in
15 http://peterkovesi.com/projects/colourmaps/CETperceptual_csv_0_1.zip
16
17 Each of these colormaps can be accessed as a Bokeh palette or
18 Matplotlib colormap, either by string name:
19
20 palette['name']
21 cm['name']
22
23 or as Python attributes:
24
25 palette.name
26 cm.name
27
28 or as individually importable Python attributes:
29
30 m_name
31 b_name
32
33 All colormaps are named using Peter Kovesi\'s naming scheme:
34
35 <category>_<huesequence>_<lightnessrange>_c<meanchroma>[_s<colorshift>_[r<ifreversed>]]
36
37 but some have shorter, more convenient aliases, some of which are
38 named for the color ranges included and others
39 based on the qualitative appearance. The colormaps with
40 shorter names tend to be the most useful subset, and for
41 cases like automatic population of a GUI widget these
42 colormaps are provided as a separate subset:
43
44 palette_n['name'] or palette_n.name
45 cm_n['name'] or cm_n.name
46
47 Also included are some sets of 256 Glasbey colors. These are available via the
48 same methods described above and are named:
49
50 glasbey_<starting_palette>[_<min|max>c_<chroma_value>][_<min|max>l_<lightness_value>][_hue_<start>_<end>]
51
52 Some of the Glasbey sets are aliased to short names as explained in the User Guide.
53 """
54
55 __version__ = '1.0.0'
56
57 from collections import OrderedDict
58
59 class AttrODict(OrderedDict):
60 """Ordered dictionary with attribute access (e.g. for tab completion)"""
61 def __dir__(self): return self.keys()
62 def __delattr__(self, name): del self[name]
63 def __getattr__(self, name):
64 return self[name] if not name.startswith('_') else super(AttrODict, self).__getattr__(name)
65 def __setattr__(self, name, value):
66 if (name.startswith('_')): return super(AttrODict, self).__setattr__(name, value)
67 self[name] = value
68
69 try:
70 from matplotlib.colors import LinearSegmentedColormap
71 from matplotlib.cm import register_cmap
72 except:
73 def LinearSegmentedColormap(colorlist,name): pass
74 def register_cmap(name,cmap): pass
75 LinearSegmentedColormap.from_list=lambda n,c,N: None
76
77 def rgb_to_hex(r,g,b):
78 return '#%02x%02x%02x' % (r,g,b)
79
80 def bokeh_palette(name,colorlist):
81 palette[name] = [rgb_to_hex(int(r*255),int(g*255),int(b*255)) for r,g,b in colorlist]
82 return palette[name]
83
84 def mpl_cm(name,colorlist):
85 cm[name] = LinearSegmentedColormap.from_list(name, colorlist, N=len(colorlist))
86 register_cmap("cet_"+name, cmap=cm[name])
87 return cm[name]
88
89 def get_aliases(name):
90 """Get the aliases for a given colormap name"""
91 names = [name]
92
93 def check_aliases(names, d, k_position=-1, v_position=0):
94 for name in [n for n in names]:
95 for k, v in d.items():
96 if name == v and k not in names:
97 if k_position == -2:
98 names.append(k)
99 else:
100 names.insert(k_position, k)
101 if name == k and v not in names:
102 if v_position == -2:
103 names.append(v)
104 else:
105 names.insert(v_position, v)
106 return names
107
108 names = check_aliases(names, aliases, k_position=-2, v_position=0)
109 names = check_aliases(names, aliases_v2, k_position=-1, v_position=-2)
110 names = check_aliases(names, mapping_flipped, k_position=-2, v_position=-1)
111 names = check_aliases(names, aliases_v2, k_position=-1, v_position=-2)
112 names = check_aliases(names, aliases, k_position=-2, v_position=0)
113 return ', '.join(names)
114
115 def all_original_names(group=None, not_group=None, only_aliased=False, only_CET=False):
116 """Get all original names - optionally in a particular group - or only those with aliases"""
117 names = palette.keys()
118 if group:
119 names = filter(lambda x: group in x, names)
120 if not_group:
121 names = filter(lambda x: not_group not in x, names)
122 if only_aliased:
123 names = filter(lambda x: x in aliases.keys(), names)
124 else:
125 names = filter(lambda x: x not in aliases.values(), names)
126 if only_CET:
127 names = filter(lambda x: x in mapping_flipped.values(), names)
128 else:
129 names = filter(lambda x: x not in mapping_flipped.values(), names)
130 return sorted(list(names))
131
132 palette = AttrODict()
133 cm = AttrODict()
134 palette_n = AttrODict()
135 cm_n = AttrODict()
136
137 '''
138
139 footer = """
140 palette_n = AttrODict(sorted(palette_n.items()))
141 cm_n = AttrODict(sorted(cm_n.items()))
142 """
143
144 # Here #mpl indicates a colormap name taken from Matplotlib
145 aliases = dict(
146 cyclic_mygbm_30_95_c78_s25 = 'colorwheel',
147 diverging_bkr_55_10_c35 = 'bkr',
148 diverging_bky_60_10_c30 = 'bky',
149 diverging_protanopic_deuteranopic_bwy_60_95_c32 = 'bwy',
150 diverging_tritanopic_cwr_75_98_c20 = 'cwr',
151 diverging_bwr_40_95_c42 = 'coolwarm', #mpl
152 diverging_gwv_55_95_c39 = 'gwv',
153 diverging_linear_bjy_30_90_c45 = 'bjy',
154 isoluminant_cgo_80_c38 = 'isolum',
155 linear_bgy_10_95_c74 = 'bgy',
156 linear_bgyw_15_100_c68 = 'bgyw',
157 linear_blue_5_95_c73 = 'kbc',
158 linear_blue_95_50_c20 = 'blues', #mpl
159 linear_bmw_5_95_c89 = 'bmw',
160 linear_bmy_10_95_c78 = 'bmy',
161 linear_green_5_95_c69 = 'kgy',
162 linear_grey_0_100_c0 = 'gray', #mpl
163 linear_grey_10_95_c0 = 'dimgray',
164 linear_kryw_0_100_c71 = 'fire',
165 linear_ternary_blue_0_44_c57 = 'kb',
166 linear_ternary_green_0_46_c42 = 'kg',
167 linear_ternary_red_0_50_c52 = 'kr',
168 rainbow_bgyr_35_85_c73 = 'rainbow',
169 glasbey_bw_minc_20 = 'glasbey',
170 glasbey_bw_minc_20_minl_30 = 'glasbey_light',
171 glasbey_bw_minc_20_maxl_70 = 'glasbey_dark',
172 glasbey_bw_minc_20_hue_330_100 = 'glasbey_warm',
173 glasbey_bw_minc_20_hue_150_280 = 'glasbey_cool',
174 )
175
176 mapping = {
177 'CET-L1': 'linear_grey_0-100_c0',
178 'CET-L2': 'linear_grey_10-95_c0',
179 'CET-L3': 'linear_kryw_0-100_c71',
180 'CET-L4': 'linear_kry_0-97_c73',
181 'CET-L5': 'linear_kgy_5-95_c69',
182 'CET-L6': 'linear_kbc_5-95_c73',
183 'CET-L7': 'linear_bmw_5-95_c86',
184 'CET-L8': 'linear_bmy_10-95_c71',
185 'CET-L9': 'linear_bgyw_20-98_c66',
186 'CET-L10': 'linear_gow_60-85_c27',
187 'CET-L11': 'linear_gow_65-90_c35',
188 'CET-L12': 'linear_blue_95-50_c20',
189 'CET-L13': 'linear_ternary-red_0-50_c52',
190 'CET-L14': 'linear_ternary-green_0-46_c42',
191 'CET-L15': 'linear_ternary-blue_0-44_c57',
192 'CET-L16': 'linear_kbgyw_5-98_c62',
193 'CET-L17': 'linear_worb_100-25_c53',
194 'CET-L18': 'linear_wyor_100-45_c55',
195 'CET-L19': 'linear_wcmr_100-45_c42',
196 'CET-D1': 'diverging_bwr_40-95_c42',
197 'CET-D1A': 'diverging_bwr_20-95_c54',
198 'CET-D2': 'diverging_gwv_55-95_c39',
199 'CET-D3': 'diverging_gwr_55-95_c38',
200 'CET-D4': 'diverging_bkr_55-10_c35',
201 'CET-D6': 'diverging_bky_60-10_c30',
202 'CET-D7': 'diverging-linear_bjy_30-90_c45',
203 'CET-D8': 'diverging-linear_bjr_30-55_c53',
204 'CET-D9': 'diverging_bwr_55-98_c37',
205 'CET-D10': 'diverging_cwm_80-100_c22',
206 'CET-D13': 'diverging_bwg_20-95_c41',
207 'CET-R3': 'diverging-rainbow_bgymr_45-85_c67',
208 'CET-R1': 'rainbow_bgyrm_35-85_c69',
209 'CET-R2': 'rainbow_bgyr_35-85_c72',
210 'CET-C1': 'cyclic_mrybm_35-75_c68',
211 'CET-C1s': 'cyclic_mrybm_35-75_c68_s25',
212 'CET-C2': 'cyclic_mygbm_30-95_c78',
213 'CET-C2s': 'cyclic_mygbm_30-95_c78_s25',
214 'CET-C4': 'cyclic_wrwbw_40-90_c42',
215 'CET-C4s': 'cyclic_wrwbw_40-90_c42_s25',
216 'CET-C5': 'cyclic_grey_15-85_c0',
217 'CET-C5s': 'cyclic_grey_15-85_c0_s25',
218 'CET-I1': 'isoluminant_cgo_70_c39',
219 'CET-I2': 'isoluminant_cgo_80_c38',
220 'CET-I3': 'isoluminant_cm_70_c39',
221 'CET-D11': 'diverging-isoluminant_cjo_70_c25',
222 'CET-D12': 'diverging-isoluminant_cjm_75_c23',
223 'CET-CBL1': 'linear-protanopic-deuteranopic_kbjyw_5-95_c25',
224 'CET-CBL2': 'linear-protanopic-deuteranopic_kbw_5-98_c40',
225 'CET-CBD1': 'diverging-protanopic-deuteranopic_bwy_60-95_c32',
226 'CET-CBC1': 'cyclic-protanopic-deuteranopic_bwyk_16-96_c31',
227 'CET-CBC2': 'cyclic-protanopic-deuteranopic_wywb_55-96_c33',
228 'CET-CBTL1': 'linear-tritanopic_krjcw_5-98_c46',
229 'CET-CBTL2': 'linear-tritanopic_krjcw_5-95_c24',
230 'CET-CBTD1': 'diverging-tritanopic_cwr_75-98_c20',
231 'CET-CBTC1': 'cyclic-tritanopic_cwrk_40-100_c20',
232 'CET-CBTC2': 'cyclic-tritanopic_wrwc_70-100_c20',
233 }
234
235 mapping_flipped = {v.replace('-', '_'): k.replace('-', '_') for
236 k, v in mapping.items()}
237
238 aliases_v2 = {
239 'linear_green_5_95_c69': 'linear_kgy_5_95_c69',
240 'linear_blue_5_95_c73': 'linear_kbc_5_95_c73',
241 }
242
243 def create_alias(alias, base, output, is_name=True):
244 output.write("{0} = b_{1}\n".format(alias,base))
245 output.write("m_{0} = m_{1}\n".format(alias,base))
246 output.write("m_{0}_r = m_{1}_r\n".format(alias,base))
247 output.write("palette['{0}'] = b_{1}\n".format(alias,base))
248 if is_name:
249 output.write("palette_n['{0}'] = b_{1}\n".format(alias,base))
250 output.write("cm['{0}'] = m_{1}\n".format(alias,base))
251 output.write("cm['{0}_r'] = m_{1}_r\n".format(alias,base))
252 if is_name:
253 output.write("cm_n['{0}'] = mpl_cm('{0}',{1})\n".format(alias,base))
254 output.write("cm_n['{0}_r'] = mpl_cm('{0}_r',list(reversed({1})))\n".format(alias,base))
255 else:
256 output.write("register_cmap('cet_{0}',m_{1})\n".format(alias,base))
257 output.write("register_cmap('cet_{0}_r',m_{1}_r)\n".format(alias,base))
258
259 cmaps = []
260 with open(output_file, "w") as output:
261 output.write(header)
262 output.write("aliases = {}\n".format(aliases))
263 output.write("aliases_v2 = {}\n".format(aliases_v2))
264 output.write("mapping_flipped = {}\n".format(mapping_flipped))
265 for path in paths:
266 for filename in os.listdir(path):
267 if filename.endswith(".csv"):
268 base = mapping.get(filename[:-4], filename[:-4])
269 base = base.replace("-","_").replace("_n256","")
270 if base in cmaps:
271 continue
272 output.write("\n\n"+base+" = [\\\n")
273 with open(os.path.join(path,filename),'r') as csvfile:
274 reader = csv.reader(csvfile)
275 for row in reader:
276 output.write("["+', '.join(row)+"],\n")
277 output.write("]\n")
278 output.write("b_{0} = bokeh_palette('{0}',{0})\n".format(base))
279 output.write("m_{0} = mpl_cm('{0}',{0})\n".format(base))
280 output.write("m_{0}_r = mpl_cm('{0}_r',list(reversed({0})))\n".format(base))
281 if base in aliases:
282 alias = aliases[base]
283 create_alias(alias, base, output)
284 if base in mapping_flipped:
285 alias = mapping_flipped[base]
286 create_alias(alias, base, output, is_name=False)
287 output.write("\n\n")
288 cmaps.append(base)
289 output.write(footer)
+0
-256
assets/CETperceptual_csv_0_1/cyclic_grey_15-85_c0_n256.csv less more
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+0
-256
assets/CETperceptual_csv_0_1/cyclic_grey_15-85_c0_n256_s25.csv less more
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assets/CETperceptual_csv_0_1/linear_gow_65-90_c35_n256.csv less more
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assets/CETperceptual_csv_0_1_v2/CET-CBC2.csv less more
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assets/CETperceptual_csv_0_1_v2/CET-CBL2.csv less more
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assets/CETperceptual_csv_0_1_v2/CET-CBTC1.csv less more
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assets/CETperceptual_csv_0_1_v2/CET-I2.csv less more
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assets/Glasbey/glasbey_bw_minc_20_n256.csv less more
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assets/Glasbey/glasbey_bw_n256.csv less more
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assets/Glasbey/glasbey_category10_n256.csv less more
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+0
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assets/Glasbey/glasbey_hv_n256.csv less more
0 0.188235,0.635294,0.854902
1 0.988235,0.309804,0.188235
2 0.898039,0.682353,0.219608
3 0.427451,0.564706,0.309804
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0 {"version_string": "None"}
126126 mapping_flipped = {'linear_grey_0_100_c0': 'CET_L1', 'linear_grey_10_95_c0': 'CET_L2', 'linear_kryw_0_100_c71': 'CET_L3', 'linear_kry_0_97_c73': 'CET_L4', 'linear_kgy_5_95_c69': 'CET_L5', 'linear_kbc_5_95_c73': 'CET_L6', 'linear_bmw_5_95_c86': 'CET_L7', 'linear_bmy_10_95_c71': 'CET_L8', 'linear_bgyw_20_98_c66': 'CET_L9', 'linear_gow_60_85_c27': 'CET_L10', 'linear_gow_65_90_c35': 'CET_L11', 'linear_blue_95_50_c20': 'CET_L12', 'linear_ternary_red_0_50_c52': 'CET_L13', 'linear_ternary_green_0_46_c42': 'CET_L14', 'linear_ternary_blue_0_44_c57': 'CET_L15', 'linear_kbgyw_5_98_c62': 'CET_L16', 'linear_worb_100_25_c53': 'CET_L17', 'linear_wyor_100_45_c55': 'CET_L18', 'linear_wcmr_100_45_c42': 'CET_L19', 'diverging_bwr_40_95_c42': 'CET_D1', 'diverging_bwr_20_95_c54': 'CET_D1A', 'diverging_gwv_55_95_c39': 'CET_D2', 'diverging_gwr_55_95_c38': 'CET_D3', 'diverging_bkr_55_10_c35': 'CET_D4', 'diverging_bky_60_10_c30': 'CET_D6', 'diverging_linear_bjy_30_90_c45': 'CET_D7', 'diverging_linear_bjr_30_55_c53': 'CET_D8', 'diverging_bwr_55_98_c37': 'CET_D9', 'diverging_cwm_80_100_c22': 'CET_D10', 'diverging_bwg_20_95_c41': 'CET_D13', 'diverging_rainbow_bgymr_45_85_c67': 'CET_R3', 'rainbow_bgyrm_35_85_c69': 'CET_R1', 'rainbow_bgyr_35_85_c72': 'CET_R2', 'cyclic_mrybm_35_75_c68': 'CET_C1', 'cyclic_mrybm_35_75_c68_s25': 'CET_C1s', 'cyclic_mygbm_30_95_c78': 'CET_C2', 'cyclic_mygbm_30_95_c78_s25': 'CET_C2s', 'cyclic_wrwbw_40_90_c42': 'CET_C4', 'cyclic_wrwbw_40_90_c42_s25': 'CET_C4s', 'cyclic_grey_15_85_c0': 'CET_C5', 'cyclic_grey_15_85_c0_s25': 'CET_C5s', 'isoluminant_cgo_70_c39': 'CET_I1', 'isoluminant_cgo_80_c38': 'CET_I2', 'isoluminant_cm_70_c39': 'CET_I3', 'diverging_isoluminant_cjo_70_c25': 'CET_D11', 'diverging_isoluminant_cjm_75_c23': 'CET_D12', 'linear_protanopic_deuteranopic_kbjyw_5_95_c25': 'CET_CBL1', 'linear_protanopic_deuteranopic_kbw_5_98_c40': 'CET_CBL2', 'diverging_protanopic_deuteranopic_bwy_60_95_c32': 'CET_CBD1', 'cyclic_protanopic_deuteranopic_bwyk_16_96_c31': 'CET_CBC1', 'cyclic_protanopic_deuteranopic_wywb_55_96_c33': 'CET_CBC2', 'linear_tritanopic_krjcw_5_98_c46': 'CET_CBTL1', 'linear_tritanopic_krjcw_5_95_c24': 'CET_CBTL2', 'diverging_tritanopic_cwr_75_98_c20': 'CET_CBTD1', 'cyclic_tritanopic_cwrk_40_100_c20': 'CET_CBTC1', 'cyclic_tritanopic_wrwc_70_100_c20': 'CET_CBTC2'}
127127
128128
129 diverging_isoluminant_cjm_75_c23 = [\
129 diverging_isoluminant_cjm_75_c23 = [ # cmap_def
130130 [0, 0.78663, 1],
131131 [0, 0.78616, 0.99976],
132132 [0, 0.78569, 0.99755],
399399
400400
401401
402 linear_grey_10_95_c0 = [\
402 linear_grey_10_95_c0 = [ # cmap_def
403403 [0.10767, 0.1077, 0.1077],
404404 [0.11032, 0.11035, 0.11035],
405405 [0.11295, 0.11298, 0.11297],
681681
682682
683683
684 isoluminant_cgo_70_c39 = [\
684 isoluminant_cgo_70_c39 = [ # cmap_def
685685 [0.21566, 0.71777, 0.92594],
686686 [0.21805, 0.71808, 0.92254],
687687 [0.2204, 0.71839, 0.91913],
954954
955955
956956
957 diverging_bky_60_10_c30 = [\
957 diverging_bky_60_10_c30 = [ # cmap_def
958958 [0.056674, 0.57959, 0.98121],
959959 [0.066107, 0.57547, 0.97345],
960960 [0.074463, 0.57135, 0.9657],
12361236
12371237
12381238
1239 diverging_bwr_40_95_c42 = [\
1239 diverging_bwr_40_95_c42 = [ # cmap_def
12401240 [0.12757, 0.31619, 0.85836],
12411241 [0.14675, 0.32048, 0.85923],
12421242 [0.16386, 0.32476, 0.8601],
15181518
15191519
15201520
1521 cyclic_mrybm_35_75_c68 = [\
1521 cyclic_mrybm_35_75_c68 = [ # cmap_def
15221522 [0.97561, 0.52012, 0.97096],
15231523 [0.98033, 0.51722, 0.96319],
15241524 [0.9842, 0.51357, 0.95448],
17911791
17921792
17931793
1794 linear_blue_5_95_c73 = [\
1794 linear_blue_5_95_c73 = [ # cmap_def
17951795 [0, 0.0024382, 0.30687],
17961796 [0, 0.0044692, 0.31437],
17971797 [0, 0.0062324, 0.32191],
20642064
20652065
20662066
2067 diverging_gkr_60_10_c40 = [\
2067
2068 diverging_gkr_60_10_c40 = [ # cmap_def
20682069 [0.2109, 0.65114, 0.08795],
20692070 [0.21113, 0.64645, 0.089674],
20702071 [0.21134, 0.64175, 0.091371],
23292330
23302331
23312332
2332 diverging_gwv_55_95_c39 = [\
2333 diverging_gwv_55_95_c39 = [ # cmap_def
23332334 [0.22157, 0.59107, 0.055073],
23342335 [0.22965, 0.59389, 0.068993],
23352336 [0.2376, 0.59671, 0.081291],
26112612
26122613
26132614
2614 rainbow_bgyrm_35_85_c71 = [\
2615 rainbow_bgyrm_35_85_c71 = [ # cmap_def
26152616 [0, 0.20755, 0.97632],
26162617 [0, 0.22113, 0.96201],
26172618 [0, 0.23397, 0.94773],
28762877
28772878
28782879
2879 linear_ternary_green_0_46_c42 = [\
2880 linear_ternary_green_0_46_c42 = [ # cmap_def
28802881 [0, 0, 0],
28812882 [0, 0.0035927, 0],
28822883 [0, 0.0071431, 1.452e-17],
31583159
31593160
31603161
3161 linear_kryw_5_100_c64 = [\
3162 linear_kryw_5_100_c64 = [ # cmap_def
31623163 [0.066001, 0.066024, 0.066019],
31633164 [0.080369, 0.066784, 0.064674],
31643165 [0.092822, 0.067552, 0.063341],
34233424
34243425
34253426
3426 diverging_bwr_55_98_c37 = [\
3427 diverging_bwr_55_98_c37 = [ # cmap_def
34273428 [0.14124, 0.50092, 0.99811],
34283429 [0.16238, 0.50425, 0.99811],
34293430 [0.18106, 0.50758, 0.99811],
36963697
36973698
36983699
3699 cyclic_mygbm_30_95_c78_s25 = [\
3700 cyclic_mygbm_30_95_c78_s25 = [ # cmap_def
37003701 [0.18062, 0.13244, 0.91856],
37013702 [0.19173, 0.12446, 0.92396],
37023703 [0.20389, 0.11765, 0.92889],
39783979
39793980
39803981
3981 diverging_rainbow_bgymr_45_85_c67 = [\
3982 diverging_rainbow_bgymr_45_85_c67 = [ # cmap_def
39823983 [0.032081, 0.36182, 0.97348],
39833984 [0.05836, 0.37092, 0.95845],
39843985 [0.075961, 0.37984, 0.94345],
42514252
42524253
42534254
4254 linear_bgyw_15_100_c68 = [\
4255 linear_bgyw_15_100_c68 = [ # cmap_def
42554256 [0.10261, 0, 0.52493],
42564257 [0.10283, 0, 0.53197],
42574258 [0.10301, 0, 0.53902],
45254526
45264527
45274528
4528 diverging_isoluminant_cjo_70_c25 = [\
4529 diverging_isoluminant_cjo_70_c25 = [ # cmap_def
45294530 [0, 0.71527, 1],
45304531 [0, 0.7149, 1],
45314532 [0, 0.71453, 1],
47984799
47994800
48004801
4801 cyclic_mygbm_30_95_c78 = [\
4802 cyclic_mygbm_30_95_c78 = [ # cmap_def
48024803 [0.93769, 0.33352, 0.94809],
48034804 [0.94383, 0.34283, 0.94239],
48044805 [0.94939, 0.35275, 0.93613],
50715072
50725073
50735074
5074 cyclic_mrybm_35_75_c68_s25 = [\
5075 cyclic_mrybm_35_75_c68_s25 = [ # cmap_def
50755076 [0.24409, 0.24544, 0.94131],
50765077 [0.25448, 0.24134, 0.94753],
50775078 [0.26529, 0.23866, 0.95301],
53445345
53455346
53465347
5347 linear_bmw_5_95_c86 = [\
5348 linear_bmw_5_95_c86 = [ # cmap_def
53485349 [0, 0.0078689, 0.29563],
53495350 [0, 0.01018, 0.30347],
53505351 [0, 0.01249, 0.31136],
56175618
56185619
56195620
5620 linear_grey_0_100_c0 = [\
5621 linear_grey_0_100_c0 = [ # cmap_def
56215622 [0, 0, 0],
56225623 [0.0055606, 0.0055631, 0.0055625],
56235624 [0.011212, 0.011219, 0.011217],
58995900
59005901
59015902
5902 linear_bmy_10_95_c71 = [\
5903 linear_bmy_10_95_c71 = [ # cmap_def
59035904 [0.0018068, 0.058001, 0.36399],
59045905 [0.0024107, 0.059964, 0.37048],
59055906 [0.0031987, 0.061692, 0.37697],
61726173
61736174
61746175
6175 diverging_isoluminant_cjm_75_c24 = [\
6176 diverging_isoluminant_cjm_75_c24 = [ # cmap_def
61766177 [0, 0.79435, 0.99671],
61776178 [0, 0.79384, 0.99455],
61786179 [0, 0.79333, 0.99239],
64376438
64386439
64396440
6440 rainbow_bgyr_35_85_c73 = [\
6441 rainbow_bgyr_35_85_c73 = [ # cmap_def
64416442 [0, 0.20755, 0.97632],
64426443 [0, 0.21857, 0.96476],
64436444 [0, 0.2291, 0.95322],
67116712
67126713
67136714
6714 rainbow_bgyr_35_85_c72 = [\
6715 rainbow_bgyr_35_85_c72 = [ # cmap_def
67156716 [0, 0.20387, 0.96251],
67166717 [0, 0.21524, 0.9514],
67176718 [0, 0.22613, 0.94031],
69846985
69856986
69866987
6987 linear_kry_5_98_c75 = [\
6988 linear_kry_5_98_c75 = [ # cmap_def
69886989 [0.066001, 0.066024, 0.066019],
69896990 [0.080538, 0.066611, 0.064712],
69906991 [0.093113, 0.067208, 0.063412],
72497250
72507251
72517252
7252 linear_blue_95_50_c20 = [\
7253 linear_blue_95_50_c20 = [ # cmap_def
72537254 [0.94334, 0.94353, 0.94348],
72547255 [0.94036, 0.94166, 0.94325],
72557256 [0.93738, 0.9398, 0.94302],
75317532
75327533
75337534
7534 linear_ternary_red_0_50_c52 = [\
7535 linear_ternary_red_0_50_c52 = [ # cmap_def
75357536 [0, 0, 0],
75367537 [0.0080248, 0.0015152, 4.5084e-17],
75377538 [0.016166, 0.0030536, 0],
78137814
78147815
78157816
7816 linear_bgyw_15_100_c67 = [\
7817 linear_bgyw_15_100_c67 = [ # cmap_def
78177818 [0.10477, 0.0011197, 0.51875],
78187819 [0.105, 0.0031206, 0.52587],
78197820 [0.10521, 0.005201, 0.53301],
80788079
80798080
80808081
8081 linear_kry_5_95_c72 = [\
8082 linear_kry_5_95_c72 = [ # cmap_def
80828083 [0.066001, 0.066024, 0.066019],
80838084 [0.07965, 0.066744, 0.064745],
80848085 [0.091664, 0.06747, 0.063475],
83438344
83448345
83458346
8346 rainbow_bgyrm_35_85_c69 = [\
8347 rainbow_bgyrm_35_85_c69 = [ # cmap_def
83478348 [0, 0.18656, 0.96265],
83488349 [0, 0.20247, 0.94889],
83498350 [0, 0.21731, 0.93516],
86168617
86178618
86188619
8619 linear_kryw_0_100_c71 = [\
8620 linear_kryw_0_100_c71 = [ # cmap_def
86208621 [0, 0, 0],
86218622 [0.027065, 2.143e-05, 0],
86228623 [0.052054, 7.4728e-05, 0],
88988899
88998900
89008901
8901 diverging_bkr_55_10_c35 = [\
8902 diverging_bkr_55_10_c35 = [ # cmap_def
89028903 [0.097481, 0.50736, 0.98205],
89038904 [0.1033, 0.50389, 0.97428],
89048905 [0.10863, 0.50041, 0.96653],
91809181
91819182
91829183
9183 linear_green_5_95_c69 = [\
9184 linear_green_5_95_c69 = [ # cmap_def
91849185 [0.0020082, 0.082527, 0.022201],
91859186 [0.004381, 0.08615, 0.022118],
91869187 [0.0067598, 0.089747, 0.022041],
94549455
94559456
94569457
9457
9458 linear_bmw_5_95_c89 = [\
9458 linear_bmw_5_95_c89 = [ # cmap_def
94599459 [0, 0.017709, 0.34641],
94609460 [0, 0.020672, 0.3555],
94619461 [0, 0.023402, 0.3646],
97299729
97309730
97319731
9732 cyclic_wrwbw_40_90_c42_s25 = [\
9732 cyclic_wrwbw_40_90_c42_s25 = [ # cmap_def
97339733 [0.10246, 0.39014, 0.89689],
97349734 [0.11205, 0.39152, 0.89693],
97359735 [0.12596, 0.39371, 0.897],
1000210002
1000310003
1000410004
10005 isoluminant_cgo_80_c38 = [\
10005 isoluminant_cgo_80_c38 = [ # cmap_def
1000610006 [0.43773, 0.82141, 1],
1000710007 [0.438, 0.82178, 1],
1000810008 [0.43825, 0.82216, 1],
1028410284
1028510285
1028610286
10287 cyclic_grey_15_85_c0_s25 = [\
10287 cyclic_grey_15_85_c0_s25 = [ # cmap_def
1028810288 [0.17676, 0.17681, 0.1768],
1028910289 [0.1775, 0.17755, 0.17754],
1029010290 [0.17871, 0.17875, 0.17874],
1055710557
1055810558
1055910559
10560 diverging_cwm_80_100_c22 = [\
10560 diverging_cwm_80_100_c22 = [ # cmap_def
1056110561 [0, 0.85071, 1],
1056210562 [0, 0.85194, 1],
1056310563 [0.056211, 0.85316, 1],
1083010830
1083110831
1083210832
10833 linear_gow_60_85_c27 = [\
10833 linear_gow_60_85_c27 = [ # cmap_def
1083410834 [0.39872, 0.60624, 0.56451],
1083510835 [0.40156, 0.60721, 0.56266],
1083610836 [0.4044, 0.60816, 0.5608],
1110311103
1110411104
1110511105
11106 linear_bmy_10_95_c78 = [\
11106 linear_bmy_10_95_c78 = [ # cmap_def
1110711107 [0, 0.047803, 0.4883],
1110811108 [0, 0.049705, 0.49553],
1110911109 [0, 0.051451, 0.50272],
1137711377
1137811378
1137911379
11380 linear_gow_65_90_c35 = [\
11380 linear_gow_65_90_c35 = [ # cmap_def
1138111381 [0.43765, 0.67814, 0.36054],
1138211382 [0.44314, 0.67844, 0.361],
1138311383 [0.4486, 0.67874, 0.36146],
1165011650
1165111651
1165211652
11653 linear_ternary_blue_0_44_c57 = [\
11653 linear_ternary_blue_0_44_c57 = [ # cmap_def
1165411654 [0, 0, 0],
1165511655 [0.00074336, 0.0024531, 0.0074342],
1165611656 [0.0014922, 0.0049243, 0.014921],
1193211932
1193311933
1193411934
11935 isoluminant_cm_70_c39 = [\
11935 isoluminant_cm_70_c39 = [ # cmap_def
1193611936 [0.078311, 0.7275, 0.90055],
1193711937 [0.096546, 0.72692, 0.9011],
1193811938 [0.11226, 0.72635, 0.90165],
1220512205
1220612206
1220712207
12208 diverging_linear_bjr_30_55_c53 = [\
12208 diverging_linear_bjr_30_55_c53 = [ # cmap_def
1220912209 [0, 0.1654, 0.84384],
1221012210 [0, 0.16745, 0.84024],
1221112211 [0, 0.16948, 0.83663],
1247812478
1247912479
1248012480
12481 diverging_gwr_55_95_c38 = [\
12481 diverging_gwr_55_95_c38 = [ # cmap_def
1248212482 [0.22157, 0.59107, 0.055073],
1248312483 [0.22965, 0.59389, 0.068993],
1248412484 [0.2376, 0.59671, 0.081291],
1275112751
1275212752
1275312753
12754 linear_bgy_10_95_c74 = [\
12754 linear_bgy_10_95_c74 = [ # cmap_def
1275512755 [0, 0.047803, 0.4883],
1275612756 [0, 0.049756, 0.49556],
1275712757 [0, 0.051605, 0.50281],
1302513025
1302613026
1302713027
13028 cyclic_grey_15_85_c0 = [\
13028 cyclic_grey_15_85_c0 = [ # cmap_def
1302913029 [0.46877, 0.46888, 0.46885],
1303013030 [0.47381, 0.47391, 0.47389],
1303113031 [0.47887, 0.47897, 0.47895],
1329813298
1329913299
1330013300
13301 cyclic_wrwbw_40_90_c42 = [\
13301 cyclic_wrwbw_40_90_c42 = [ # cmap_def
1330213302 [0.873, 0.83593, 0.84864],
1330313303 [0.8777, 0.8339, 0.84071],
1330413304 [0.88194, 0.83075, 0.832],
1357113571
1357213572
1357313573
13574 linear_kryw_5_100_c67 = [\
13574 linear_kryw_5_100_c67 = [ # cmap_def
1357513575 [0.066001, 0.066024, 0.066019],
1357613576 [0.080835, 0.066624, 0.064683],
1357713577 [0.093644, 0.067235, 0.063358],
1383613836
1383713837
1383813838
13839 diverging_linear_bjy_30_90_c45 = [\
13839 diverging_linear_bjy_30_90_c45 = [ # cmap_def
1384013840 [0.077854, 0.19343, 0.75827],
1384113841 [0.095213, 0.19629, 0.7569],
1384213842 [0.11007, 0.1991, 0.75553],
1411814118
1411914119
1412014120
14121 linear_bgyw_20_98_c66 = [\
14121 linear_bgyw_20_98_c66 = [ # cmap_def
1412214122 [0.019721, 0, 0.67312],
1412314123 [0.021792, 0.0093198, 0.67457],
1412414124 [0.023764, 0.020375, 0.67602],
1439114391
1439214392
1439314393
14394 linear_tritanopic_krjcw_5_95_c24 = [\
14394 linear_tritanopic_krjcw_5_95_c24 = [ # cmap_def
1439514395 [0.066001, 0.066024, 0.066019],
1439614396 [0.076759, 0.067596, 0.06602],
1439714397 [0.086416, 0.069198, 0.066054],
1466414664
1466514665
1466614666
14667 diverging_bwr_20_95_c54 = [\
14667 diverging_bwr_20_95_c54 = [ # cmap_def
1466814668 [0.091874, 0.16093, 0.4469],
1466914669 [0.09332, 0.16518, 0.45817],
1467014670 [0.094888, 0.16952, 0.4695],
1493714937
1493814938
1493914939
14940 linear_tritanopic_krjcw_5_98_c46 = [\
14940 linear_tritanopic_krjcw_5_98_c46 = [ # cmap_def
1494114941 [0.066001, 0.066024, 0.066019],
1494214942 [0.078225, 0.067325, 0.065441],
1494314943 [0.089145, 0.068628, 0.064864],
1521015210
1521115211
1521215212
15213 cyclic_protanopic_deuteranopic_wywb_55_96_c33 = [\
15213 cyclic_protanopic_deuteranopic_wywb_55_96_c33 = [ # cmap_def
1521415214 [0.93344, 0.92965, 0.9265],
1521515215 [0.93483, 0.9285, 0.91829],
1521615216 [0.93521, 0.92627, 0.90898],
1548315483
1548415484
1548515485
15486 diverging_bwg_20_95_c41 = [\
15486 diverging_bwg_20_95_c41 = [ # cmap_def
1548715487 [0.065559, 0.17633, 0.40763],
1548815488 [0.069881, 0.18111, 0.41595],
1548915489 [0.074129, 0.18595, 0.42428],
1575615756
1575715757
1575815758
15759 cyclic_protanopic_deuteranopic_bwyk_16_96_c31 = [\
15759 cyclic_protanopic_deuteranopic_bwyk_16_96_c31 = [ # cmap_def
1576015760 [0.24364, 0.52802, 0.91769],
1576115761 [0.2544, 0.53367, 0.92309],
1576215762 [0.26662, 0.53935, 0.92754],
1602916029
1603016030
1603116031
16032 linear_wyor_100_45_c55 = [\
16032 linear_wyor_100_45_c55 = [ # cmap_def
1603316033 [0.99986, 1, 1],
1603416034 [0.99765, 0.99831, 0.98983],
1603516035 [0.99541, 0.99656, 0.97966],
1630216302
1630316303
1630416304
16305 linear_wcmr_100_45_c42 = [\
16305 linear_wcmr_100_45_c42 = [ # cmap_def
1630616306 [0.99986, 1, 1],
1630716307 [0.99494, 0.99813, 0.99962],
1630816308 [0.99002, 0.9962, 0.99922],
1657516575
1657616576
1657716577
16578 diverging_protanopic_deuteranopic_bwy_60_95_c32 = [\
16578 diverging_protanopic_deuteranopic_bwy_60_95_c32 = [ # cmap_def
1657916579 [0.22906, 0.56603, 0.9967],
1658016580 [0.24157, 0.56872, 0.99641],
1658116581 [0.25345, 0.57142, 0.99612],
1685716857
1685816858
1685916859
16860 diverging_tritanopic_cwr_75_98_c20 = [\
16860 diverging_tritanopic_cwr_75_98_c20 = [ # cmap_def
1686116861 [0.16144, 0.79052, 0.90539],
1686216862 [0.17912, 0.79202, 0.90596],
1686316863 [0.19524, 0.79352, 0.90654],
1713917139
1714017140
1714117141
17142 linear_protanopic_deuteranopic_kbw_5_98_c40 = [\
17142 linear_protanopic_deuteranopic_kbw_5_98_c40 = [ # cmap_def
1714317143 [0.066001, 0.066024, 0.066019],
1714417144 [0.068392, 0.069594, 0.07418],
1714517145 [0.070639, 0.072929, 0.081677],
1741217412
1741317413
1741417414
17415 linear_protanopic_deuteranopic_kbjyw_5_95_c25 = [\
17415 linear_protanopic_deuteranopic_kbjyw_5_95_c25 = [ # cmap_def
1741617416 [0.066001, 0.066024, 0.066019],
1741717417 [0.067911, 0.069463, 0.075246],
1741817418 [0.069781, 0.072628, 0.083652],
1768517685
1768617686
1768717687
17688 linear_worb_100_25_c53 = [\
17688 linear_worb_100_25_c53 = [ # cmap_def
1768917689 [0.99986, 1, 1],
1769017690 [0.99813, 0.99701, 0.98897],
1769117691 [0.99635, 0.99397, 0.97793],
1795817958
1795917959
1796017960
17961 linear_kbgyw_5_98_c62 = [\
17961 linear_kbgyw_5_98_c62 = [ # cmap_def
1796217962 [0.066001, 0.066024, 0.066019],
1796317963 [0.073531, 0.067157, 0.081516],
1796417964 [0.080354, 0.068289, 0.094843],
1823118231
1823218232
1823318233
18234 linear_kbc_5_95_c73 = [\
18234 linear_kbc_5_95_c73 = [ # cmap_def
1823518235 [0, 0.0024382, 0.30687],
1823618236 [0, 0.0044692, 0.31437],
1823718237 [0, 0.0062324, 0.32191],
1850418504
1850518505
1850618506
18507 cyclic_tritanopic_wrwc_70_100_c20 = [\
18507 cyclic_tritanopic_wrwc_70_100_c20 = [ # cmap_def
1850818508 [0.98457, 0.98115, 0.98169],
1850918509 [0.98868, 0.9791, 0.97837],
1851018510 [0.99201, 0.97625, 0.97432],
1877718777
1877818778
1877918779
18780 linear_kgy_5_95_c69 = [\
18780 linear_kgy_5_95_c69 = [ # cmap_def
1878118781 [0.0020082, 0.082527, 0.022201],
1878218782 [0.004381, 0.08615, 0.022118],
1878318783 [0.0067598, 0.089747, 0.022041],
1905019050
1905119051
1905219052
19053 cyclic_tritanopic_cwrk_40_100_c20 = [\
19053 cyclic_tritanopic_cwrk_40_100_c20 = [ # cmap_def
1905419054 [0.1493, 0.73523, 0.84245],
1905519055 [0.1629, 0.73969, 0.8467],
1905619056 [0.17926, 0.74403, 0.85055],
1932319323
1932419324
1932519325
19326 linear_kry_0_97_c73 = [\
19326 linear_kry_0_97_c73 = [ # cmap_def
1932719327 [0, 0, 0],
1932819328 [0.025875, 1.8579e-05, 0],
1932919329 [0.050536, 6.891e-05, 2.2895e-17],
1959619596
1959719597
1959819598
19599 glasbey_category10 = [\
19599 glasbey_category10 = [ # cmap_def
1960019600 [0.121569, 0.466667, 0.705882],
1960119601 [1.000000, 0.498039, 0.054902],
1960219602 [0.172549, 0.627451, 0.172549],
1986119861
1986219862
1986319863
19864 glasbey_bw_minc_20_minl_30 = [\
19864 glasbey_bw_minc_20_minl_30 = [ # cmap_def
1986519865 [0.843137, 0.000000, 0.000000],
1986619866 [0.007843, 0.533333, 0.000000],
1986719867 [0.713725, 0.000000, 1.000000],
2013520135
2013620136
2013720137
20138 glasbey_bw_minc_20_hue_330_100 = [\
20138 glasbey_bw_minc_20_hue_330_100 = [ # cmap_def
2013920139 [0.843137, 0.000000, 0.000000],
2014020140 [1.000000, 0.384314, 0.972549],
2014120141 [0.674510, 0.615686, 0.000000],
2040920409
2041020410
2041120411
20412 glasbey_bw_minc_20 = [\
20412 glasbey_bw_minc_20 = [ # cmap_def
2041320413 [0.843137, 0.000000, 0.000000],
2041420414 [0.549020, 0.235294, 1.000000],
2041520415 [0.007843, 0.533333, 0.000000],
2068320683
2068420684
2068520685
20686 glasbey_bw = [\
20686 glasbey_bw = [ # cmap_def
2068720687 [0.843137, 0.000000, 0.000000],
2068820688 [0.549020, 0.235294, 1.000000],
2068920689 [0.007843, 0.533333, 0.000000],
2094820948
2094920949
2095020950
20951 glasbey_bw_minc_20_maxl_70 = [\
20951 glasbey_bw_minc_20_maxl_70 = [ # cmap_def
2095220952 [0.843137, 0.000000, 0.000000],
2095320953 [0.549020, 0.235294, 1.000000],
2095420954 [0.007843, 0.533333, 0.000000],
2122221222
2122321223
2122421224
21225 glasbey_hv = [\
21225 glasbey_hv = [ # cmap_def
2122621226 [0.188235, 0.635294, 0.854902],
2122721227 [0.988235, 0.309804, 0.188235],
2122821228 [0.898039, 0.682353, 0.219608],
2148721487
2148821488
2148921489
21490 glasbey_bw_minc_20_hue_150_280 = [\
21490 glasbey_bw_minc_20_hue_150_280 = [ # cmap_def
2149121491 [0.050980, 0.415686, 1.000000],
2149221492 [0.035294, 0.584314, 0.301961],
2149321493 [0.000000, 0.792157, 0.898039],
0 import colorcet as cc
1 from colorcet.plotting import swatches, sine_combs
2
3 import holoviews as hv
4 import panel as pn
5
6 hv.extension('bokeh')
7
8 diverging_n = cc.all_original_names(group='diverging', only_aliased=True)
9 linear_n = cc.all_original_names(group='linear', not_group='diverging', only_aliased=True)
10 cat_n = cc.all_original_names(group='glasbey', only_aliased=True)
11 misc_n = sorted([k for k in cc.aliases if k not in cat_n + diverging_n + linear_n])
12
13 diverging_col = pn.Column('#Diverging', sine_combs(*diverging_n, width=400, height=150).opts(toolbar=None))
14 linear_col = pn.Column('#Linear', sine_combs(*linear_n, width=400, height=150).opts(toolbar=None))
15 cat_col = pn.Column('#Categorical', swatches(*cat_n, width=400, height=150, cols=1).opts(toolbar=None))
16 misc_col = pn.Column('#Misc', sine_combs(*misc_n, width=400, height=150).opts(toolbar=None))
17
18 all_named = pn.Row(
19 linear_col, pn.Spacer(width=100),
20 pn.Column(
21 diverging_col, pn.Spacer(height=102),
22 cat_col, pn.Spacer(height=102),
23 misc_col))
24
25 all_named.save('./images/named.png')
0 {
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "## Collection of perceptually accurate colormaps\n",
7 "\n",
8 "[Colorcet](https://github.com/pyviz/colorcet) is a collection of \n",
9 "perceptually acccurate 256-color colormaps for use with Python plotting programs like\n",
10 "[Bokeh](http://bokeh.pydata.org),\n",
11 "[Matplotlib](http://matplotlib.org),\n",
12 "[HoloViews](http://holoviews.org), and\n",
13 "[Datashader](https://github.com/pyviz/datashader). \n",
14 "\n",
15 "There are two types of colormaps currently included: [continuous](./user_guide/Continuous.ipynb) and [categorical](./user_guide/Categorical.ipynb). The continuous colormaps are perceptually uniform, with each new color equally perceptually distinct from the previous and following colors. The continuous maps were constructed by [Peter Kovesi](http://peterkovesi.com/projects/colourmaps) at the Center for Exploration Targeting using the methods described in [Kovesi (2015)](https://arxiv.org/abs/1509.03700).\n",
16 "\n",
17 "The [categorical](user_guide/Categorical.ipynb) colormaps are perceptually distinct, but not uniform; each color is meant for a separate category and not as a position on a numerical scale. Here, categorical colormaps of arbitrary length are generated to cover a perceptual space with well-separated samples, using the method from \n",
18 "[Glasbey, van der Heijden, Toh, & Gray (2007)](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf).\n",
19 "\n",
20 "See [Getting Started](http://colorcet.holoviz.org/getting_started) for installation info, and the [User Guide](./user_guide/index.ipynb) for more info on what colormaps are available and how to use them.\n",
21 "\n",
22 "\n",
23 "## Background\n",
24 "\n",
25 "Apart from the four [uniform colormaps now provided with\n",
26 "matplotlib](http://matplotlib.org/users/colormaps.html), most\n",
27 "continuous colormaps shipping with Python plotting programs are highly\n",
28 "perceptually nonuniform. That is, small changes in data values\n",
29 "result in large changes in the perceptual appearance of the\n",
30 "corresponding colors, or vice versa. For instance, the popular matplotlib\n",
31 "\"hot\" and \"jet\" colormaps have long stretches where the apparent\n",
32 "colors change imperceptibly, such as the yellow region in \"hot\" and\n",
33 "the cyan/green region in \"jet\":\n",
34 "\n",
35 "![hot](assets/images/hot.png) ![jet](assets/images/jet.png)\n",
36 "\n",
37 "When colormaps are used for visualizing scientific datasets, these\n",
38 "perceptual nonlinearities can make interpretation of this data very\n",
39 "difficult, because false boundaries appear in the data, and genuine\n",
40 "boundaries and changes can be obscured.\n",
41 "\n",
42 "Matplotlib now offers a [tool to construct uniform\n",
43 "colormaps](https://github.com/matplotlib/viscm), but it doesn't yet\n",
44 "allow construction of colormaps like those above. To address this\n",
45 "need, Peter Kovesi at the Center for Exploration\n",
46 "Targeting created a set of colormaps that are sampled uniformly in a\n",
47 "perceptual color space, using methods he describes in a [paper on\n",
48 "arXiv](https://arxiv.org/abs/1509.03700). For instance, the\n",
49 "perceptually uniform versions of the above colormaps are called \"fire\"\n",
50 "and \"rainbow\" in this package:\n",
51 "\n",
52 "![fire](assets/images/fire.png) ![rainbow](assets/images/rainbow.png)\n",
53 "\n",
54 "You should be able to see the differences right away, revealing more\n",
55 "detail, more faithfully than if you use non-uniform maps. For\n",
56 "instance, here is a population-density dataset (from\n",
57 "[datashader](https://github.com/bokeh/datashader)) rendered with the\n",
58 "original matplotlib \"hot\" colormap:\n",
59 "\n",
60 "![census_hot](assets/images/census_hot.png)\n",
61 "\n",
62 "and the same dataset rendered with colorcet's \"fire\" colormap:\n",
63 "\n",
64 "![census_fire](assets/images/census_fire.png)\n",
65 "\n",
66 "It should be obvious that the \"hot\" version completely washes out\n",
67 "detail at the high end, as if the image is overexposed, while \"fire\"\n",
68 "makes detail visible throughout the data range. Yet the qualitative\n",
69 "effect is still roughly similar, allowing \"fire\" to be used in nearly\n",
70 "any situation where the original \"hot\" was useful.\n",
71 "\n",
72 "Peter provides [versions of 50 perceptually uniform colormaps for a\n",
73 "variety of different plotting programs](http://peterkovesi.com/projects/colourmaps), \n",
74 "and this package provides those colormaps ready to use from within Python\n",
75 "programs. The colormaps are all illustrated in the \n",
76 "[user guide](./user_guide/index.ipynb) that describes the \n",
77 "different types available and allows you to test how perceptually\n",
78 "uniform they are on your particular display device.\n",
79 "\n",
80 "Peter's methods differ from those used in Matplotlib's\n",
81 "uniform colormaps (as implemented in their [viscm\n",
82 "tool](https://github.com/matplotlib/viscm)), which (apart from using a\n",
83 "different color model) are designed to satisfy different constraints.\n",
84 "For instance, mpl's colormaps are always perceptually uniform in their\n",
85 "monochrome representation, not just their original color\n",
86 "representation, and are safe for colorblind viewers, neither of which\n",
87 "are necessarily true of these colormaps. On the other hand, colormaps\n",
88 "like \"fire\" above, i.e., a usable perceptually equivalent of\n",
89 "matplotlib/matlab's \"hot\", are not obtainable using viscm in any\n",
90 "straightforward way, limiting the range of useful colormaps that can be created.\n",
91 "In any case, this package focuses on making a set of useful colormaps\n",
92 "readily available from within Python programs, rather than providing\n",
93 "tools for building novel colormaps, for which see the \n",
94 "[viscm tool](https://github.com/matplotlib/viscm) and\n",
95 "[Peter's original site](http://peterkovesi.com/projects/colourmaps). \n",
96 "\n",
97 "Colorcet also includes [categorical colormaps](user_guide/Categorical.ipynb)\n",
98 "that provide large numbers of distinct colors sampled systematically from a\n",
99 "perceptually uniform space. These maps are made with similar methods, but \n",
100 "with a goal of having recognizable different colors rather than showing \n",
101 "an ordering like the perceptually uniform maps above.\n",
102 "\n",
103 "\n",
104 "## Learning more\n",
105 "\n",
106 "You can see all the details about the methods used to create these\n",
107 "colormaps in [Peter Kovesi (2015)](https://arxiv.org/pdf/1509.03700v1.pdf) and [Glasbey et al. (2007)](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf). Other useful\n",
108 "background is available in a research paper from IBM\n",
109 "[(Rogowitz & Treinish 1996)](https://github.com/ResearchComputing/USGS_2015_06_23-25/raw/master/25_June/ColorTheory_References/Why%20Should%20Engineers%20and%20Scientists%20Be%20Worried%20About%20Color.pdf).\n",
110 "\n",
111 "The Matplotlib project also has a number of relevant resources, including an excellent \n",
112 "[2015 SciPy talk](https://www.youtube.com/watch?v=xAoljeRJ3lU), the\n",
113 "[viscm tool for creating maps like the four in mpl](https://github.com/matplotlib/viscm), the\n",
114 "[cmocean site](http://matplotlib.org/cmocean/) collecting a set of maps created by viscm, \n",
115 "and the [discussion of how the mpl maps were created](https://bids.github.io/colormap/).\n",
116 "\n",
117 "## Samples\n",
118 "\n",
119 "All the colorcet colormaps that have short, memorable names (which are probably\n",
120 "the most useful ones) are visible here:\n",
121 "\n",
122 "<img src=\"assets/images/named.png\" width=\"800\">\n",
123 "\n",
124 "But the complete set of 60+ is shown in the [User Guide](./user_guide/index.ipynb)."
125 ]
126 }
127 ],
128 "metadata": {
129 "language_info": {
130 "name": "python",
131 "pygments_lexer": "ipython3"
132 }
133 },
134 "nbformat": 4,
135 "nbformat_minor": 2
136 }
0 {
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "## Glasbey colormaps for categorical data\n",
7 "\n",
8 "Colorcet primarily includes continuous colormaps, where each color is meant to be equally spaced in perceptual color space from the preceding and following colors. The resulting colors then convey numerical magnitude to the viewer. Categorical data can also be represented as numbers, but each number is then distinct, with the numerical value important only to distinguish from other values. When categorical data is plotted as colors, each category should have a color visibly distinct from all the other colors, _not_ nearby in color space, to make each category separately visible. \n",
9 "\n",
10 "There are [many sets of categorical colors available](https://graphicdesign.stackexchange.com/questions/3682), but these tend to be relatively small numbers of hand-chosen colors, typically under 10 and nearly always under 25. To support visualizing larger numbers of categories, it would be good if an arbitrarily large number of colors could be chosen in a principled way from an available color space. Glasbey et al. presented such a method in:\n",
11 "\n",
12 "> [Glasbey, Chris; van der Heijden, Gerie & Toh, Vivian F. K. et al. (2007)](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf), \"Colour displays for categorical images\", Color Research & Application 32.4: 304-309.\n",
13 "\n",
14 "Given a starting palette (e.g. white, black), this approach selects each new color to be maximally perceptually dissimilar from all the preceding colors, out of an allowed color space. Glasbey colors are available in [ImageJ](http://imagej.net/Glasbey) and for [R](https://github.com/btupper/catecolors), and there is a [Python implementation](https://github.com/taketwo/glasbey) of the method. \n",
15 "\n",
16 "Generating the colors from Python is time consuming, so we have generated some specific large sets of Glasbey colors to distribute in colorcet, as outlined below. We'll use [Bokeh](http://bokeh.pydata.org) with [HoloViews](http://holoviews.org) for displaying these colormaps so that you can zoom in for a closer look."
17 ]
18 },
19 {
20 "cell_type": "code",
21 "execution_count": null,
22 "metadata": {},
23 "outputs": [],
24 "source": [
25 "from colorcet.plotting import swatch, swatches, candy_buttons\n",
26 "\n",
27 "import holoviews as hv\n",
28 "hv.extension('bokeh')"
29 ]
30 },
31 {
32 "cell_type": "markdown",
33 "metadata": {},
34 "source": [
35 "## Color space\n",
36 "\n",
37 "The results from the Glasbey algorithm depend on two factors: the initial starting set of colors, and the color space used. The default color space is the full [CIELAB](https://en.wikipedia.org/wiki/CIELAB_color_space) gamut, which results in colors of all the different available saturations, brightnesses, and hues:"
38 ]
39 },
40 {
41 "cell_type": "code",
42 "execution_count": null,
43 "metadata": {},
44 "outputs": [],
45 "source": [
46 "swatch(\"glasbey_bw\")"
47 ]
48 },
49 {
50 "cell_type": "markdown",
51 "metadata": {},
52 "source": [
53 "If you need *n&nbsp;*colors that are distinct from each other and you have no other criteria to apply, just take the first *n&nbsp;*colors from this palette. Note that these colors won't necessarily be the _most_ distinct that are possible in a set of that size, because they are chosen iteratively (each compared to those that come before it) using a greedy algorithm rather than being globally optimized for that specific size. The Glasbey algorithm can also be used to create such fully optimal sets, but it would not be practical to distribute all those sets and the color assignments would then vary each time a new data point was added, so this single set represents a good compromise.\n",
54 "\n",
55 "### Light/Dark\n",
56 "What if you have additional constraints to consider? For instance, if you are making a scatter or line plot on a white or black background, some of the colors above will show up vividly in contrast, and others will fade into the background because they are very dark or very light:"
57 ]
58 },
59 {
60 "cell_type": "code",
61 "execution_count": null,
62 "metadata": {},
63 "outputs": [],
64 "source": [
65 "candy_buttons('glasbey_bw') + candy_buttons('glasbey_bw', bgcolor='black')"
66 ]
67 },
68 {
69 "cell_type": "markdown",
70 "metadata": {},
71 "source": [
72 "For this reason, the basic **glasbey_bw** set is generally useful only when the page background isn't visible between data points, such as for heatmaps with no missing values. To work well in the more common case when the background is visible, we also provide Glasbey colors selected from a subset of the full CIELAB space. First, let's consider sets created for light or dark backgrounds:"
73 ]
74 },
75 {
76 "cell_type": "code",
77 "execution_count": null,
78 "metadata": {},
79 "outputs": [],
80 "source": [
81 "swatches(\"glasbey_bw_minc_20_maxl_70\", \"glasbey_bw_minc_20_minl_30\")"
82 ]
83 },
84 {
85 "cell_type": "markdown",
86 "metadata": {},
87 "source": [
88 "Here we are using a naming scheme indicating the starting palette and any restrictions on the color space:\n",
89 " \n",
90 " glasbey_<starting_palette>[_<min|max>c_<chroma_value>][_<min|max>l_<lightness_value>][_hue_<start>_<end>]\n",
91 " \n",
92 "The **glasbey_dark** colors were generated by eliminating all colors with lightness above 70 (out of 100) in the [CIECAM02-JCh](https://colorspacious.readthedocs.io/en/latest/tutorial.html#tutorial-perception) color space, and similarly the **glasbey_light** colors eliminated those with lightness below 30. In both cases, shades of gray were also eliminated by removing colors with chromaticity below 20. After trimming the space, the same Glasbey procedure was followed as usual, making each subsequent color maximally distinct (from the remaining possible colors) from all those that precede it. These sets are intended to be useful on light and dark backgrounds, respectively, providing good contrast in each case. \n",
93 "\n",
94 "In order to test the viability of categorical color sets, we should display them on the color of background where they will be used. This helps us discern whether the colors are distinguishable between themselves and against the background. We'll make candy button style plot to capture this property:"
95 ]
96 },
97 {
98 "cell_type": "code",
99 "execution_count": null,
100 "metadata": {},
101 "outputs": [],
102 "source": [
103 "candy_buttons('glasbey_dark') + candy_buttons('glasbey_light', bgcolor='black')"
104 ]
105 },
106 {
107 "cell_type": "markdown",
108 "metadata": {},
109 "source": [
110 "E.g., notice how the dots on the black background above are all clearly visible, unlike the **glasbey_bw** example from the start. "
111 ]
112 },
113 {
114 "cell_type": "markdown",
115 "metadata": {},
116 "source": [
117 "### Cool/Warm\n",
118 "What if you are working within a particular color palette or want to combine two relatively large sets of categories? For this case we created sets of warm and cool colors from a subset of the full CIELAB hue space:"
119 ]
120 },
121 {
122 "cell_type": "code",
123 "execution_count": null,
124 "metadata": {},
125 "outputs": [],
126 "source": [
127 "swatches(\"glasbey_bw_minc_20_hue_330_100\", \"glasbey_bw_minc_20_hue_150_280\")"
128 ]
129 },
130 {
131 "cell_type": "markdown",
132 "metadata": {},
133 "source": [
134 "Similar to how the dark and light sets were created, these warm and cool sets were generated by first trimming the colorspace and then applying the Glasbey procedure to make the colors maximally distinct. The \"warm\" colors were generated by only including colors with hues between 330° and 100°, and similarly the \"cool\" colors only include those between 150° and 280°. These sets can be used by themselves or in conjunction. It is important to note that by reducing the color space, we are decreasing the number of meaningfully distinct colors. So these sets should only be used when there is a strong reason to do so. E.g. in the blue plot, you can probably notice colors that _nearly_ reappear in other locations, even though each of the 100 dots is in a separate category:"
135 ]
136 },
137 {
138 "cell_type": "code",
139 "execution_count": null,
140 "metadata": {},
141 "outputs": [],
142 "source": [
143 "candy_buttons('glasbey_warm') + candy_buttons('glasbey_cool')"
144 ]
145 },
146 {
147 "cell_type": "markdown",
148 "metadata": {},
149 "source": [
150 "### No gray\n",
151 "\n",
152 "We also generated a set where we eliminated gray values, i.e. those with chromaticity in the JCh color space below 20. These are useful if you truly need \"colors\", i.e. not grays: "
153 ]
154 },
155 {
156 "cell_type": "code",
157 "execution_count": null,
158 "metadata": {},
159 "outputs": [],
160 "source": [
161 "swatch(\"glasbey_bw_minc_20\")"
162 ]
163 },
164 {
165 "cell_type": "markdown",
166 "metadata": {},
167 "source": [
168 "You might have noticed that most of the colormaps mentioned above have more than one name. The short name is provided as an easy to remember alias for accessing the colormaps, while the long names indicate precisely how that set was created. In the case of the alias **glasbey**, we decided that the version with no gray would provide a better default than the regular glasbey set, called **glasbey_bw**. In the plot below you can see that the less-constrained **glasbey_bw** has several colors that are very dark and hard to distinguish, compared to the more distinguishable **glasbey** set on the right. "
169 ]
170 },
171 {
172 "cell_type": "code",
173 "execution_count": null,
174 "metadata": {},
175 "outputs": [],
176 "source": [
177 "candy_buttons('glasbey_bw') + candy_buttons('glasbey_bw_minc_20')"
178 ]
179 },
180 {
181 "cell_type": "markdown",
182 "metadata": {},
183 "source": [
184 "## Starting colors\n",
185 "\n",
186 "For a given color space, the results also depend on the set of starting colors, which above was always [white, black]. We have removed those two colors from the final palettes, because white and black are generally already in use for plotting as the page background or for plot annotations. (That said, if you do need the colormap to include white and black, just add `[\"white\",\"black\"]` to the front of one of the Bokeh palettes, as Bokeh palettes are simply Python lists of colors.)\n",
187 "\n",
188 "In some cases, you may already have an initial set of colors that you use regularly, and you just want to extend that set to allow additional colors that are distinct from the original set. In that case, the Glasbey algorithm can be initialized with the starting set, and then extended using a selected color space.\n",
189 "\n",
190 "Here, we've extended the Category10 colormap used in [bokeh](https://bokeh.pydata.org/en/latest/docs/reference/palettes.html#bokeh.palettes.d3) and originally from [D3](https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#categorical-colors) to make a 256-color drop-in replacement that provides more colors for when they are needed, but the same first 10 colors:"
191 ]
192 },
193 {
194 "cell_type": "code",
195 "execution_count": null,
196 "metadata": {},
197 "outputs": [],
198 "source": [
199 "swatch(\"glasbey_category10\")"
200 ]
201 },
202 {
203 "cell_type": "markdown",
204 "metadata": {},
205 "source": [
206 "To check how these compare with the original sets in Bokeh, we can render them side by side. Drag to the right or scroll out to see more of the Glasbey versions:"
207 ]
208 },
209 {
210 "cell_type": "code",
211 "execution_count": null,
212 "metadata": {},
213 "outputs": [],
214 "source": [
215 "from bokeh.palettes import Category10_10\n",
216 "\n",
217 "(swatch(name='bokeh: Category10', cmap=Category10_10, bounds=(0, 0, 10, 1)) +\n",
218 " swatch('glasbey_category10')).cols(1).redim.range(x=(0, 50))"
219 ]
220 },
221 {
222 "cell_type": "markdown",
223 "metadata": {},
224 "source": [
225 "We did the same for the default holoviews values:"
226 ]
227 },
228 {
229 "cell_type": "code",
230 "execution_count": null,
231 "metadata": {},
232 "outputs": [],
233 "source": [
234 "(swatch(name='holoviews default', cmap=hv.Cycle().values, bounds=(0, 0, 12, 1)) +\n",
235 " swatch('glasbey_hv')).cols(1).redim.range(x=(0, 50))"
236 ]
237 },
238 {
239 "cell_type": "markdown",
240 "metadata": {},
241 "source": [
242 "Note that as you include more and more colors, it will be more and more difficult for people to perceive their differences, but this approach is still better than the highly misleading alternative of having to cycle through the same colors again as one would do when only 10 colors are available:"
243 ]
244 },
245 {
246 "cell_type": "code",
247 "execution_count": null,
248 "metadata": {},
249 "outputs": [],
250 "source": [
251 "candy_buttons('bokeh: Category10', cmap=Category10_10) + candy_buttons('glasbey_category10')"
252 ]
253 },
254 {
255 "cell_type": "markdown",
256 "metadata": {},
257 "source": [
258 "## Complete list, for reference\n",
259 "\n",
260 "The colormaps with relatively short names above are expected to be the most useful in practice. The complete list of categorical colormaps provided is:"
261 ]
262 },
263 {
264 "cell_type": "code",
265 "execution_count": null,
266 "metadata": {},
267 "outputs": [],
268 "source": [
269 "swatches(group='glasbey', cols=1)"
270 ]
271 },
272 {
273 "cell_type": "code",
274 "execution_count": null,
275 "metadata": {},
276 "outputs": [],
277 "source": []
278 }
279 ],
280 "metadata": {
281 "language_info": {
282 "name": "python",
283 "pygments_lexer": "ipython3"
284 }
285 },
286 "nbformat": 4,
287 "nbformat_minor": 2
288 }
0 {
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "## Perceptually uniform continuous colormaps from CET\n",
7 "\n",
8 "Peter Kovesi at the Center for Exploration Targeting created a very useful set of [perceptually uniform continuous colormaps](https://arxiv.org/abs/1509.03700), many of which can replace the highly non-uniform colormaps provided with Python plotting programs. Here we will show how to use them via a Python package named [colorcet](https://github.com/pyviz/colorcet), listing all the ones available and allowing you to evaluate how perceptually uniform they are for you, your particular monitor, etc. Download and installation instructions are at the [github site](https://github.com/pyviz/colorcet).\n",
9 "\n",
10 "We will plot them using [Matplotlib](http://matplotlib.org) via [Holoviews](http://holoviews.org), but identical [Bokeh](http://bokeh.pydata.org) palettes are also provided, and both Bokeh palettes and Matplotlib colormaps are usable in [Datashader](https://github.com/pyviz/datashader). Thus these colormaps can be used in any of those Python packages, as well as any other package that accepts a Python list of normalized RGB tuples or hex colors. See \n",
11 "[Peter's site](http://peterkovesi.com/projects/colourmaps/) to download versions for other non-Python packages."
12 ]
13 },
14 {
15 "cell_type": "code",
16 "execution_count": null,
17 "metadata": {},
18 "outputs": [],
19 "source": [
20 "import holoviews as hv\n",
21 "import colorcet as cc\n",
22 "from colorcet.plotting import swatches, sine_combs\n",
23 "\n",
24 "hv.notebook_extension(\"matplotlib\")"
25 ]
26 },
27 {
28 "cell_type": "markdown",
29 "metadata": {},
30 "source": [
31 "## Named colormaps\n",
32 "\n",
33 "The full list of colormaps included will be shown below, but a useful subset of these maps that cover the various types have been given more convenient names, and we will focus on those first."
34 ]
35 },
36 {
37 "cell_type": "code",
38 "execution_count": null,
39 "metadata": {},
40 "outputs": [],
41 "source": [
42 "swatches(only_aliased=True, not_group='glasbey')"
43 ]
44 },
45 {
46 "cell_type": "markdown",
47 "metadata": {},
48 "source": [
49 "## All colormaps\n",
50 "\n",
51 "Each colormap has a name in the form:\n",
52 "\n",
53 "`{category}_{huesequence}_{lightnessrange}_c{meanchroma}[_s{colorshift}[_r]]`\n",
54 "\n",
55 "It is helpful to think of them by which category best suits your use case. \n",
56 "\n",
57 "### Linear (sequential) colormaps, for plotting magnitudes:"
58 ]
59 },
60 {
61 "cell_type": "code",
62 "execution_count": null,
63 "metadata": {},
64 "outputs": [],
65 "source": [
66 "swatches(group=\"linear\")"
67 ]
68 },
69 {
70 "cell_type": "markdown",
71 "metadata": {},
72 "source": [
73 "### Diverging colormaps, for plotting magnitudes increasing or decreasing from a central point:"
74 ]
75 },
76 {
77 "cell_type": "code",
78 "execution_count": null,
79 "metadata": {},
80 "outputs": [],
81 "source": [
82 "swatches(group='diverging')"
83 ]
84 },
85 {
86 "cell_type": "markdown",
87 "metadata": {},
88 "source": [
89 "### Colorblind-safe colormaps (usable by people with various types of colorblindness)"
90 ]
91 },
92 {
93 "cell_type": "code",
94 "execution_count": null,
95 "metadata": {},
96 "outputs": [],
97 "source": [
98 "swatches(group='nopic')"
99 ]
100 },
101 {
102 "cell_type": "markdown",
103 "metadata": {},
104 "source": [
105 "### Misc colormaps:\n",
106 "\n",
107 "* cyclic: for cyclic quantities like orientation or phase (where the highest and lowest values need the same color)\n",
108 "* isoluminant: to highlight low spatial-frequency information\n",
109 "* rainbow: to highlight local differences in sequential data"
110 ]
111 },
112 {
113 "cell_type": "code",
114 "execution_count": null,
115 "metadata": {},
116 "outputs": [],
117 "source": [
118 "misc = [name for name in cc.all_original_names() if \"cyclic\" in name or \"isoluminant\" in name or \"rainbow\" in name]\n",
119 "swatches(*misc)"
120 ]
121 },
122 {
123 "cell_type": "markdown",
124 "metadata": {},
125 "source": [
126 "## Testing perceptual uniformity\n",
127 "\n",
128 "Peter Kovesi created a [test image with a sine grating modulation of intensity](http://peterkovesi.com/projects/colourmaps/colourmaptestimage.html), where modulation gain decreases from top to bottom, which helps evaluate perceptual uniformity of a colormap at a glance. The matplotlib maintainers use different definitions of perceptually uniform (uniformity in a different color space), but the new matplotlib perceptually uniform colormaps do well at Peter's test image:"
129 ]
130 },
131 {
132 "cell_type": "code",
133 "execution_count": null,
134 "metadata": {},
135 "outputs": [],
136 "source": [
137 "sine_combs((\"viridis\", \"viridis\"), (\"inferno\", \"inferno\"))"
138 ]
139 },
140 {
141 "cell_type": "markdown",
142 "metadata": {},
143 "source": [
144 "Here the sine grating for a uniform colormap should be visible as a fine-toothed comb with teeth that gradually become less visible from top to bottom. The further down the comb these teeth are visible, the higher the discriminability of magnitudes at that location in the colormap. Thus a perceptually uniform colormap, like the two above, should have teeth that visible at the same length for all colors. \n",
145 "\n",
146 "You can also use these images to evaluate the overall level of discriminability provided by a given colormap -- the longer the visible area of teeth, the better this colormap allows you to discriminate fine differences in magnitude. Here the `inferno` map seems to have better discriminability than the `viridis` map, despite both being perceptually uniform.\n",
147 "\n",
148 "The default colormaps that have traditionally been used with Matlab, Matplotlib, and HoloViews are clearly not perceptually uniform -- all the green and yellow areas are nearly indistinguishable:"
149 ]
150 },
151 {
152 "cell_type": "code",
153 "execution_count": null,
154 "metadata": {},
155 "outputs": [],
156 "source": [
157 "sine_combs((\"hot\", \"hot\"),(\"jet\", \"jet\"))"
158 ]
159 },
160 {
161 "cell_type": "markdown",
162 "metadata": {},
163 "source": [
164 "Thus those colormaps should be avoided if at all possible, to avoid generating misleading visualizations. Compare these two to the perceptually uniform versions provided by this package: "
165 ]
166 },
167 {
168 "cell_type": "code",
169 "execution_count": null,
170 "metadata": {},
171 "outputs": [],
172 "source": [
173 "sine_combs(\"fire\", \"rainbow\")"
174 ]
175 },
176 {
177 "cell_type": "markdown",
178 "metadata": {},
179 "source": [
180 "We can see the results for all the `colorcet` colormaps below, which can be summarized as:\n",
181 "\n",
182 "* \"linear\" colormaps all work well by this criterion\n",
183 "* \"diverging\" colormaps typically have discontinuities in perceptual discriminability around the central value\n",
184 "* \"cyclic\" colormaps with repeating colors tend to have discontinuities at 1/4 and 3/4 of the way through the cycle, or at other locations if shifted.\n",
185 "* \"isoluminant\" colormaps typically show no visible modulation, because without luminance cues humans can only discriminate low spatial-frequency modulations (i.e., much wider teeth would be needed for evaluating such colormaps)\n",
186 "* Some of the \"rainbow\" colormaps appear to have a perceptual discontinuity around the color red, the reasons for which are not yet clear."
187 ]
188 },
189 {
190 "cell_type": "code",
191 "execution_count": null,
192 "metadata": {},
193 "outputs": [],
194 "source": [
195 "sine_combs(not_group='glasbey', cols=2)"
196 ]
197 }
198 ],
199 "metadata": {
200 "language_info": {
201 "name": "python",
202 "pygments_lexer": "ipython3"
203 }
204 },
205 "nbformat": 4,
206 "nbformat_minor": 2
207 }
0 {
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "source": [
5 "## Accessing the colormaps\n",
6 "\n",
7 "After importing `colorcet` as `cc`, all the colormaps shown in this notebook will be available for use in different forms. It's a bit difficult to describe, but the idea is that colorcet should have at least one such form convenient for any particular application. There are two different basic versions for each colormap, each of which is fundamentally a list of 256 distinct colors: \n",
8 "\n",
9 "1. A Bokeh-style palette, i.e., a Python list of RGB colors as hex strings, like ``['#000000', ..., '#ffffff']``\n",
10 "2. If matplotlib is installed and importable, a Matplotlib ``LinearSegmentedColormap`` using normalized magnitudes, like ``LinearSegmentedColormap.from_list(\"fire\",[ [0.0,0.0,0.0], ..., [1.0,1.0,1.0] ], 256)``\n",
11 "\n",
12 "The Bokeh-compatible palettes are provided as attributes in the ``colorcet`` namespace, with long names prefixed with ``b_`` or a short name (if any) with no prefix. E.g. ``linear_kryw_0_100_c71`` can be accessed as ``cc.b_linear_kryw_0_100_c71`` or by the short name ``cc.fire``, which both return the same Bokeh palette. These names should tab complete once ``cc`` has been imported. Because Bokeh palettes are just Python lists, you can always reverse them using normal Python syntax, e.g. ``list(reversed(cc.fire))``, or use subsets of them with slice notation, e.g. ``cc.fire[25:]``. If you want to access the palettes by string name, they are also collected into a dictionary named ``palette``, so you can use ``cc.palette[\"linear_kryw_0_100_c71\"]`` or ``cc.palette[\"fire\"]`` or ``cc.palette.fire``; whichever is more convenient. Finally, the subset of colormaps that have short, readable names are available separately, accessible as ``cc.palette_n.fire`` or ``cc.palette_n[\"fire\"]``, e.g. for use in GUI widgets selecting a colormap by readable name.\n",
13 "\n",
14 "The Matplotlib colormaps are also provided as tab-completable attributes, but consistently with a prefix ``m_``, e.g. ``cc.m_linear_kryw_0_100_c71`` or ``cc.m_fire``. Already reversed versions are also available, as ``cc.m_linear_kryw_0_100_c71_r`` or ``cc.m_fire_r``. The same colormaps are also registered with matplotlib's string-based dictionary with the prefix ``cet_``, making them available by name within various matplotlib functions (e.g. ``cet_linear_kryw_0_100_c71``, ``cet_linear_kryw_0_100_c71_r``, ``cet_fire``, or ``cet_fire_r``). Finally, if you want to access the colormaps by string name without using Matplotlib's registry, they are also stored in the ``cc.cm`` dictionary, e.g. ``cc.cm[\"linear_kryw_0_100_c71\"]``, ``cc.cm[\"linear_kryw_0_100_c71_r\"]``, ``cc.cm[\"fire\"]``, ``cc.cm.fire``, ``cc.cm[\"fire_r\"]``, or ``cc.cm.fire_r``.\n",
15 "\n",
16 "In each case, the colormap names used are the shortest ones that are expected to be unique in that context, and in practice you are only likely to need one of these forms for any particular application."
17 ],
18 "metadata": {}
19 },
20 {
21 "cell_type": "markdown",
22 "source": [
23 "#### Example\n",
24 "\n",
25 "Here we show importing fire and printing the first 5 colors in the set. "
26 ],
27 "metadata": {}
28 },
29 {
30 "cell_type": "code",
31 "execution_count": null,
32 "source": [
33 "import colorcet as cc\n",
34 "\n",
35 "cc.fire[:5]"
36 ],
37 "outputs": [],
38 "metadata": {}
39 },
40 {
41 "cell_type": "markdown",
42 "source": [
43 "## Plotting\n",
44 "\n",
45 "For ease of use, we also provide minimal plotting commands for use with colorcet. These depend on holoviews, which needs to be installed before they can be used. Once set up, these commands provide easy viewing capability of the colormaps."
46 ],
47 "metadata": {}
48 },
49 {
50 "cell_type": "markdown",
51 "source": [
52 "#### Example\n",
53 "\n",
54 "Import `swatch` or `swatches` from `colorcet.plotting` and load your desired backend into holoviews. Then call `swatch` with the name of a colormap. "
55 ],
56 "metadata": {}
57 },
58 {
59 "cell_type": "code",
60 "execution_count": null,
61 "source": [
62 "from colorcet.plotting import swatch, swatches\n",
63 "import holoviews as hv\n",
64 "hv.extension('matplotlib')"
65 ],
66 "outputs": [],
67 "metadata": {}
68 },
69 {
70 "cell_type": "code",
71 "execution_count": null,
72 "source": [
73 "swatch('fire')"
74 ],
75 "outputs": [],
76 "metadata": {}
77 },
78 {
79 "cell_type": "markdown",
80 "source": [
81 "#### Using `colorcet` via `matplotlib.cm.get_cmap`\n",
82 "\n",
83 "The `colorcet` colormaps are all available through `matplotlip.cm.get_cmap` by prepending `cet_`to the colormap name. "
84 ],
85 "metadata": {}
86 },
87 {
88 "cell_type": "code",
89 "execution_count": null,
90 "source": [
91 "from matplotlib.cm import get_cmap\n",
92 "\n",
93 "get_cmap(\"cet_fire\")"
94 ],
95 "outputs": [],
96 "metadata": {}
97 },
98 {
99 "cell_type": "markdown",
100 "source": [
101 "#### Using `colorcet` to visualize custom colormaps using `swatch`"
102 ],
103 "metadata": {}
104 },
105 {
106 "cell_type": "code",
107 "execution_count": null,
108 "source": [
109 "color_list = ['#000000', '#380000', '#560000', '#760100', '#980300', '#bb0600', '#df0d00', '#f93500', '#fe6800', '#ff9100', '#ffb402', '#ffd407', '#fff324']\n",
110 "\n",
111 "swatch(name='custom', cmap=color_list)"
112 ],
113 "outputs": [],
114 "metadata": {}
115 },
116 {
117 "cell_type": "markdown",
118 "source": [
119 "## Complete list\n",
120 "\n",
121 "The `swatches` command accepts the optional key word argument `group` to show just the colormaps that fall under a particular group - try 'glasbey', 'circular', or 'diverging'. When no arguments are provided, `swatches` returns all of the colorcet colormaps."
122 ],
123 "metadata": {}
124 },
125 {
126 "cell_type": "code",
127 "execution_count": null,
128 "source": [
129 "swatches()"
130 ],
131 "outputs": [],
132 "metadata": {}
133 },
134 {
135 "cell_type": "markdown",
136 "source": [
137 "For more explanation of the various options see [categorical](Categorical.ipynb) and [continuous](Continuous.ipynb)."
138 ],
139 "metadata": {}
140 }
141 ],
142 "metadata": {
143 "language_info": {
144 "name": "python",
145 "pygments_lexer": "ipython3",
146 "version": "3.7.11"
147 },
148 "kernelspec": {
149 "name": "python3",
150 "display_name": "Python 3.7.11 64-bit ('colorcet-dev37': conda)"
151 },
152 "interpreter": {
153 "hash": "4f854a078010334f958b1feab68ee1edfeebf6c4ffa4549f8460648dec38ebd8"
154 }
155 },
156 "nbformat": 4,
157 "nbformat_minor": 2
158 }
1313 array = np.meshgrid(np.linspace(0, 1, 256), np.linspace(0, 1, 10))[0]
1414
1515 def swatch(name, cmap=None, bounds=None, array=array, **kwargs):
16 """Show swatch using matplotlib or bokeh via holoviews"""
16 """Show a color swatch for a colormap using matplotlib or bokeh via holoviews.
17 Colormaps can be selected by `name`, including those in Colorcet
18 along with any standard Bokeh palette or named Matplotlib colormap.
19
20 Custom colormaps can be visualized by passing an explicit
21 list of colors (for Bokeh) or the colormap object (for Matplotlib) to `cmap`.
22
23 HoloViews options for either backend can be passed in as kwargs,
24 so that you can customize the width, height, etc. of the swatch.
25
26 The `bounds` and `array` arguments allow you to customize the
27 portion of the colormap to show and how many samples to take
28 from it; see the source code and hv.Image documentation for
29 details.
30 """
1731 title = name if cmap else get_aliases(name)
1832 if bounds is None:
1933 bounds = (0, 0, 256, 1)
34
35 if type(cmap) is tuple:
36 cmap = list(cmap)
2037
2138 plot = hv.Image(array, bounds=bounds, group=title)
2239 backends = hv.Store.loaded_backends()
+0
-20
colorcet/tests/README.md less more
0 ## Matplotlib tests
1
2 These tests contain baseline images generated using [pytest-mpl](https://github.com/matplotlib/pytest-mpl).
3 To run these tests with the fig checking enabled first install pytest-mpl:
4
5 ```bash
6 pip install pytest-mpl
7 ```
8
9 To regenerate these figures from within this dir run:
10
11 ```bash
12 pytest --mpl-generate-path=baseline
13 ```
14
15 To run the tests checking that the output is as expected run:
16
17 ```bash
18 pytest --mpl
19 ```
colorcet/tests/baseline/test_matplotlib.png less more
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colorcet/tests/baseline/test_matplotlib_default_colormap_plot_blues.png less more
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colorcet/tests/baseline/test_matplotlib_default_colormap_plot_kbc.png less more
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colorcet/tests/baseline/test_matplotlib_glasbey.png less more
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0 Metadata-Version: 2.1
1 Name: colorcet
2 Version: None
3 Summary: Collection of perceptually uniform colormaps
4 Home-page: https://colorcet.holoviz.org
5 Author: James A. Bednar
6 Author-email: jbednar@anaconda.com
7 Maintainer: James A. Bednar
8 Maintainer-email: jbednar@anaconda.com
9 License: CC-BY License
10 Project-URL: Bug Tracker, http://github.com/holoviz/colorcet/issues
11 Project-URL: Documentation, https://colorcet.holoviz.org
12 Project-URL: Source Code, http://github.com/holoviz/colorcet
13 Description: README.md
14 Platform: UNKNOWN
15 Classifier: License :: OSI Approved
16 Classifier: Operating System :: OS Independent
17 Classifier: Programming Language :: Python
18 Classifier: Programming Language :: Python :: 2.7
19 Classifier: Programming Language :: Python :: 3.6
20 Classifier: Programming Language :: Python :: 3.7
21 Classifier: Programming Language :: Python :: 3.8
22 Classifier: Development Status :: 5 - Production/Stable
23 Requires-Python: >=2.7
24 Provides-Extra: all
25 Provides-Extra: build
26 Provides-Extra: doc
27 Provides-Extra: examples
28 Provides-Extra: tests
29 Provides-Extra: tests_extra
0 LICENSE.txt
1 MANIFEST.in
2 README.md
3 pyproject.toml
4 setup.cfg
5 setup.py
6 colorcet/.version
7 colorcet/__init__.py
8 colorcet/__main__.py
9 colorcet/plotting.py
10 colorcet/sineramp.py
11 colorcet.egg-info/PKG-INFO
12 colorcet.egg-info/SOURCES.txt
13 colorcet.egg-info/dependency_links.txt
14 colorcet.egg-info/entry_points.txt
15 colorcet.egg-info/requires.txt
16 colorcet.egg-info/top_level.txt
17 colorcet/examples/index.ipynb
18 colorcet/examples/assets/write_named.py
19 colorcet/examples/assets/images/census_fire.png
20 colorcet/examples/assets/images/census_hot.png
21 colorcet/examples/assets/images/fire.png
22 colorcet/examples/assets/images/hot.png
23 colorcet/examples/assets/images/jet.png
24 colorcet/examples/assets/images/named.png
25 colorcet/examples/assets/images/rainbow.png
26 colorcet/examples/user_guide/Categorical.ipynb
27 colorcet/examples/user_guide/Continuous.ipynb
28 colorcet/examples/user_guide/index.ipynb
29 colorcet/tests/__init__.py
30 colorcet/tests/test_aliases.py
31 colorcet/tests/test_bokeh.py
32 colorcet/tests/test_matplotlib.py
33 examples/index.ipynb
34 examples/assets/write_named.py
35 examples/assets/images/census_fire.png
36 examples/assets/images/census_hot.png
37 examples/assets/images/fire.png
38 examples/assets/images/hot.png
39 examples/assets/images/jet.png
40 examples/assets/images/named.png
41 examples/assets/images/rainbow.png
42 examples/user_guide/Categorical.ipynb
43 examples/user_guide/Continuous.ipynb
44 examples/user_guide/index.ipynb
0 [console_scripts]
1 colorcet = colorcet.__main__:main
2
0 param>=1.7.0
1 pyct>=0.4.4
2
3 [all]
4 bokeh
5 flake8
6 holoviews
7 matplotlib
8 nbsite>=0.6.1
9 nbsmoke>=0.2.6
10 numpy
11 param>=1.7.0
12 pyct>=0.4.4
13 pytest-mpl
14 pytest>=2.8.5
15 setuptools>=30.3.0
16 sphinx_holoviz_theme
17 wheel
18
19 [build]
20 param>=1.7.0
21 pyct>=0.4.4
22 setuptools>=30.3.0
23 wheel
24
25 [doc]
26 bokeh
27 holoviews
28 matplotlib
29 nbsite>=0.6.1
30 numpy
31 sphinx_holoviz_theme
32
33 [examples]
34 bokeh
35 holoviews
36 matplotlib
37 numpy
38
39 [tests]
40 flake8
41 nbsmoke>=0.2.6
42 pytest>=2.8.5
43
44 [tests_extra]
45 flake8
46 nbsmoke>=0.2.6
47 pytest-mpl
48 pytest>=2.8.5
+0
-43
conda.recipe/meta.yaml less more
0 {% set sdata = load_setup_py_data() %}
1
2 package:
3 name: colorcet
4 version: {{ sdata['version'] }}
5
6 source:
7 path: ..
8
9 build:
10 noarch: python
11 script: python setup.py install --single-version-externally-managed --record=record.txt
12 entry_points:
13 {% for group,epoints in sdata.get("entry_points",{}).items() %}
14 {% for entry_point in epoints %}
15 - {{ entry_point }}
16 {% endfor %}
17 {% endfor %}
18
19 requirements:
20 build:
21 - python {{ sdata['python_requires'] }}
22 {% for dep in sdata['extras_require']['build'] %}
23 - {{ dep }}
24 {% endfor %}
25 run:
26 - python {{ sdata['python_requires'] }}
27 {% for dep in sdata.get('install_requires',{}) %}
28 - {{ dep }}
29 {% endfor %}
30
31 test:
32 imports:
33 - colorcet
34 requires:
35 {% for dep in sdata['extras_require']['tests'] %}
36 - "{{ dep }}"
37 {% endfor %}
38
39 about:
40 home: {{ sdata['url'] }}
41 summary: {{ sdata['description'] }}
42 license: {{ sdata['license'] }}
0 colorcet (2.0.6+git20210916.1.6228465-1) UNRELEASED; urgency=low
1
2 * New upstream snapshot.
3
4 -- Debian Janitor <janitor@jelmer.uk> Fri, 17 Sep 2021 11:42:08 -0000
5
06 colorcet (2.0.2-2) unstable; urgency=medium
17
28 * Fix d/copyright
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.gsc-tabHeader.gsc-tabhActive{border-color:#4c4c4c #4c4c4c #fff;height:29px}@media screen and (min-width: 640px){display:inline-block;position:relative;width:16em;input{box-sizing:border-box;display:block}}.expander .expander-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;color:#266498;cursor:pointer;display:block;font-size:1em;margin-bottom:1em;padding-bottom:.25em;text-decoration:none}.expander .expander-trigger:before{font-size:.7em;content:"\25BC";margin-right:0.5em}.expander .expander-hidden:before{font-size:.7em;content:"\25BA"}.expander .expander-hidden+.expander-content{display:none}
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doc/_templates/layout.html less more
0 <!DOCTYPE html>
1 <html lang="en">
2 <head>
3
4 <!-- Basic Page Needs
5 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
6 <meta charset="utf-8">
7 {% if title == "&lt;no title&gt;" %}
8 <title>{{ shorttitle|striptags|e }}</title>
9 {% else %}
10 <title>{{ title|striptags|e }} &mdash; {{ shorttitle|striptags|e }}</title>
11 {% endif %}
12 <meta name="description" content="{{ DESCRIPTION }}">
13 <meta name="author" content="{{ AUTHOR }}">
14
15 <!-- Mobile Specific Metas
16 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
17 <meta name="viewport" content="width=device-width, initial-scale=1">
18
19 <!-- FONT
20 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
21 <script src="https://ajax.googleapis.com/ajax/libs/webfont/1.6.16/webfont.js"></script>
22 <script>
23 WebFont.load({
24 google: {
25 families: ['Source Sans Pro']
26 }
27 });
28 </script>
29
30 <!-- CSS
31 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
32
33 <link rel="stylesheet" href="{{ pathto('_static/' + theme_css, 1) }}">
34 {% for link in css_includes %}
35 <link rel="stylesheet" href="{{ pathto('_static/' + link, 1) }}">
36 {% endfor %}
37 {% if theme_custom_css %}
38 <link rel="stylesheet" href="{{ pathto('_static/' + theme_custom_css, 1) }}">
39 {% endif %}
40
41 <!-- Scripts
42 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
43
44 <script src="//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
45 <script type="text/javascript" async
46 src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-MML-AM_CHTML">
47 </script>
48 <script src="{{ pathto('_static/' + theme_js, 1) }}"></script>
49 {% for link in js_includes %}
50 <script src="{{ pathto('_static/' + link, 1) }}"></script>
51 {% endfor %}
52
53 <!-- Favicon
54 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
55 <link rel="icon" type="image/png" href="{{ pathto('_static/' + theme_favicon, 1) }}">
56
57 <!-- Canonical
58 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
59 <link rel="canonical" href="{{ WEBSITE_URL }}/{{ pagename }}{{ file_suffix }}" />
60
61 </head>
62 <body class="">
63
64 <header class="navigation">
65 <div class="wrapper">
66 <a href="{{ pathto('index') }}" class="logo">
67 <img src="{{ pathto('_static/' + theme_logo, 1) }}" alt="Logo">
68 </a>
69 <a href="{{ pathto('index') }}" class="navigation-menu logo-text">{{ PROJECT }}</a>
70 <a href="javascript:void(0)" class="navigation-menu-button" id="js-mobile-menu">Menu</a>
71 <nav>
72 <ul id="js-navigation-menu" class="navigation-menu show">
73 {%- for name, link in SOCIAL %}
74 <li class="nav-link doc-head"><a href="{{ link }}">{{ name }}</a></li>
75 {%- endfor %}
76 <li class="nav-link">
77 <div style="display:inline-block;vertical-align: middle;">
78 <div class="search-bar">
79 {% include 'includes/searchbox.html' %}
80 </div>
81 </div>
82 </li>
83 </ul>
84 </nav>
85 </div>
86 </header>
87 <div class="second-nav">
88 <nav>
89 <ul class="navigation-menu show">
90 {%- for name, link in NAV %}
91 <li class="nav-link doc-head"><a href="{{ pathto(link) }}">{{ name }}</a></li>
92 {%- endfor %}
93 </ul>
94 </nav>
95 </div>
96
97 <!-- MAIN BODY OF DOCS –––––––––––––––––– -->
98 <div class="docs section">
99 <div id="hacketyhackhack"> <!-- style="width:20%;margin-right: 100px;"> --> <!--style="display: none;"> style="display:none;"> -->
100 <div class="toc" style="width:15%; margin-right:20px;">
101 {{ toctree(maxdepth=4, collapse=True, includehidden=True) }}
102 </div>
103 </div>
104 <div class="content"> <!-- style="max-width:80%;margin-left:auto;margin-right: auto;">-->
105 {% block body %}{% endblock %}
106 </div>
107 </div>
108
109 <!-- END MAIN BODY OF DOCS ––––––––––––– -->
110
111 <footer class="footer">
112 <div class="footer-links">
113 <ul>
114 <li><span class="footer-title">Links</span></li>
115 {% for name, link in LINKS %}
116 <li><a href="{{ pathto(link) }}">{{ name }}</a></li>
117 {% endfor %}
118 </ul>
119 <ul>
120 <li><span class="footer-title">Join Us</span></li>
121 {% for name, link in SOCIAL %}
122 <li><a href="{{ link }}">{{ name }}</a></li>
123 {% endfor %}
124 </ul>
125 <ul class="copyright">
126 <li>{{ copyright }}</li>
127 </ul>
128 </div>
129 </footer>
130
131 <!-- Google Analytics -->
132 {% include 'includes/ga.html' %}
133
134 <!-- MathJax Config -->
135 <script type="text/x-mathjax-config">
136 MathJax.Hub.Config({
137 tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}
138 });
139 </script>
140
141 <!-- End Document
142 –––––––––––––––––––––––––––––––––––––––––––––––––– -->
143 </body>
144
145 </html>
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doc/about.rst less more
0 About Us
1 ========
2
3 Colorcet is part of the PyViz initiative for making Python-based visualization tools work well together.
4 See `pyviz.org <http://pyviz.org>`_ for related packages that you can use with Colorcet and
5 `status.pyviz.org <http://status.pyviz.org>`_ for the current status of each PyViz project.
6
7 Colorcet is supported and mantained by `Anaconda <https://anaconda.com>`_.
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doc/conf.py less more
0 # -*- coding: utf-8 -*-
1
2 from nbsite.shared_conf import *
3
4 project = 'colorcet'
5 authors = u'Anaconda'
6 copyright = u'2017-2019 ' + authors
7 description = 'Collection of perceptually uniform colormaps'
8
9 import colorcet
10 version = release = colorcet.__version__
11
12 nbbuild_cell_timeout = 10000
13
14 html_static_path += ['_static']
15 html_theme = 'sphinx_ioam_theme'
16 # logo file etc should be in html_static_path, e.g. _static
17
18 templates_path = ['_templates']
19
20 html_theme_options = {
21 'logo':'logo.png',
22 'favicon':'favicon.ico',
23 'custom_css':'site.css'
24 }
25
26 _NAV = (
27 ('Introduction', 'index'),
28 ('Getting Started', 'getting_started/index'),
29 ('User Guide', 'user_guide/index'),
30 ('About', 'about'),
31 )
32
33 _SOCIAL = (
34 ('Github', 'https://github.com/pyviz/' + project),
35 ('Gitter', 'https://gitter.im/pyviz/pyviz'),
36 ('PyViz', 'http://pyviz.org')
37 )
38
39 html_context.update({
40 'PROJECT': project,
41 'DESCRIPTION': description,
42 'AUTHOR': authors,
43 'WEBSITE_SERVER': 'http://{}.pyviz.org'.format(project),
44 'VERSION': version,
45 'NAV': _NAV ,
46 # by default, footer links are same as those in header
47 'LINKS': _NAV,
48 'SOCIAL': _SOCIAL
49 })
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doc/getting_started/index.rst less more
0 ***************
1 Getting started
2 ***************
3
4 Installation
5 ------------
6
7 Colorcet supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows, or Mac
8 and can be installed with conda::
9
10 conda install colorcet
11
12 or with pip::
13
14 pip install colorcet
15
16 Usage
17 -----
18
19 Once you've installed colorcet the colormaps will be available
20 in two formats:
21
22 1. A Bokeh-style palette, i.e., a Python list of RGB colors as hex
23 strings, like \['\#000000', ..., '\#ffffff'\]
24 2. A Matplotlib LinearSegmentedColormap using normalized magnitudes,
25 like LinearSegmentedColormap.from\_list("fire",\[ \[0.0,0.0,0.0\],
26 ..., \[1.0,1.0,1.0\] \], 256)
27
28 Import colorcet and use the new colormaps anywhere you would use a
29 regular colormap.
30
31 **Matplotlib**::
32
33 import numpy as np
34 import colorcet as cc
35 import matplotlib.pyplot as plt
36
37 xs, _ = np.meshgrid(np.linspace(0, 1, 80), np.linspace(0, 1, 10))
38 plt.imshow(xs, cmap=cc.cm.colorwheel); # use tab completion to choose
39
40 **Bokeh**::
41
42 import numpy as np
43 import colorcet as cc
44 from bokeh.plotting import figure, show
45
46 xs, _ = np.meshgrid(np.linspace(0, 1, 80), np.linspace(0, 1, 10))
47 p = figure(x_range=(0, 80), y_range=(0, 10), height=100, width=400)
48
49 p.image(image=[xs], x=0, y=0, dw=80, dh=10, palette=cc.fire) # use tab completion to choose
50 show(p)
51
52 If you have any questions, please refer to the `User Guide <../user_guide/index>`_
53 and if that doesn't help, feel free to post an issue on GitHub, question on stackoverflow,
54 or discuss on Gitter.
55
56 Developer Instructions
57 ----------------------
58
59 1. Install Python 3 `miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ or `anaconda <https://www.anaconda.com/distribution/>`_, if you don't already have it on your system.
60
61 2. Clone the colorcet git repository if you do not already have it::
62
63 git clone git://github.com/pyviz/colorcet.git
64
65 3. Set up a new conda environment with optional plotting tools::
66
67 cd colorcet
68 conda env create -n colorcet matplotlib bokeh holoviews
69 conda activate colorcet
70
71 4. Put the colorcet directory into the Python path in this environment::
72
73 pip install -e .
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doc/index.rst less more
0 ..
1 Originally generated by nbsite (0.6.0a1):
2 /Users/jsignell/conda/envs/colorcet/bin/nbsite generate-rst --org pyviz --project-name colorcet --offset 0
3 Will not subsequently be overwritten by nbsite, so can be edited.
4
5 .. raw:: html
6
7 <h1><img src="_static/logo_stacked.png" width="200"></h1>
8
9 .. notebook:: colorcet ../examples/index.ipynb
10 :offset: 0
11
12 .. toctree::
13 :hidden:
14 :maxdepth: 2
15
16 Introduction <self>
17 Getting Started <getting_started/index>
18 User Guide <user_guide/index>
19 About <about>
20
21
22 -------
23
24 `Right click to download this notebook from GitHub. <https://raw.githubusercontent.com/pyviz/colorcet/master/examples/index.ipynb>`_
+0
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doc/user_guide/index.rst less more
0 ..
1 Originally generated by nbsite (0.6.1a4):
2 /Users/jsignell/conda/envs/colorcet/bin/nbsite generate-rst --org pyviz --project-name colorcet --offset 0
3 Will not subsequently be overwritten by nbsite, so can be edited.
4 2019-04-03 jsignell: reordered categorical and continuous to not be alphabetical
5
6 **********
7 User Guide
8 **********
9
10 .. notebook:: colorcet ../../examples/user_guide/index.ipynb
11 :offset: 0
12
13 .. toctree::
14 :titlesonly:
15 :maxdepth: 2
16
17 Continuous <Continuous>
18 Categorical <Categorical>
19
20
21 -------
22
23 `Right click to download this notebook from GitHub. <https://raw.githubusercontent.com/pyviz/colorcet/master/examples/user_guide/index.ipynb>`_
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dodo.py less more
0 """
1 This file provides a mechanism to map between the semantic commands that any
2 project has (build docs, run tests, copy examples ... ) and the specific
3 command that should be run.
4
5 Most of these commands are stored in pyctdev which is essentially a collection
6 of the pyviz way of doing these actions. Commands that are newer, or specific
7 to a particular project, will live in this file instead. To see a list of
8 all the available commands - after installing pyctdev - run:
9
10 $ doit list
11
12 To run one command, for instance building the website, run:
13
14 $ doit build_website
15 """
16
17 import os
18 if "PYCTDEV_ECOSYSTEM" not in os.environ:
19 os.environ["PYCTDEV_ECOSYSTEM"] = "conda"
20 from pyctdev import * # noqa: api
21
22
23 def task_build_website():
24 return {'actions': [
25 "nbsite generate-rst --org pyviz --project-name colorcet --offset 0",
26 "nbsite build --what=html --output=builtdocs",
27 ]}
1717 "The [categorical](user_guide/Categorical.ipynb) colormaps are perceptually distinct, but not uniform; each color is meant for a separate category and not as a position on a numerical scale. Here, categorical colormaps of arbitrary length are generated to cover a perceptual space with well-separated samples, using the method from \n",
1818 "[Glasbey, van der Heijden, Toh, & Gray (2007)](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf).\n",
1919 "\n",
20 "See [Getting Started](http://colorcet.pyviz.org/getting_started) for installation info, and the [User Guide](./user_guide/index.ipynb) for more info on what colormaps are available and how to use them.\n",
20 "See [Getting Started](http://colorcet.holoviz.org/getting_started) for installation info, and the [User Guide](./user_guide/index.ipynb) for more info on what colormaps are available and how to use them.\n",
2121 "\n",
2222 "\n",
2323 "## Background\n",
11 "cells": [
22 {
33 "cell_type": "markdown",
4 "metadata": {},
54 "source": [
65 "## Accessing the colormaps\n",
76 "\n",
1514 "The Matplotlib colormaps are also provided as tab-completable attributes, but consistently with a prefix ``m_``, e.g. ``cc.m_linear_kryw_0_100_c71`` or ``cc.m_fire``. Already reversed versions are also available, as ``cc.m_linear_kryw_0_100_c71_r`` or ``cc.m_fire_r``. The same colormaps are also registered with matplotlib's string-based dictionary with the prefix ``cet_``, making them available by name within various matplotlib functions (e.g. ``cet_linear_kryw_0_100_c71``, ``cet_linear_kryw_0_100_c71_r``, ``cet_fire``, or ``cet_fire_r``). Finally, if you want to access the colormaps by string name without using Matplotlib's registry, they are also stored in the ``cc.cm`` dictionary, e.g. ``cc.cm[\"linear_kryw_0_100_c71\"]``, ``cc.cm[\"linear_kryw_0_100_c71_r\"]``, ``cc.cm[\"fire\"]``, ``cc.cm.fire``, ``cc.cm[\"fire_r\"]``, or ``cc.cm.fire_r``.\n",
1615 "\n",
1716 "In each case, the colormap names used are the shortest ones that are expected to be unique in that context, and in practice you are only likely to need one of these forms for any particular application."
18 ]
17 ],
18 "metadata": {}
1919 },
2020 {
2121 "cell_type": "markdown",
22 "metadata": {},
2322 "source": [
2423 "#### Example\n",
2524 "\n",
2625 "Here we show importing fire and printing the first 5 colors in the set. "
27 ]
26 ],
27 "metadata": {}
2828 },
2929 {
3030 "cell_type": "code",
3131 "execution_count": null,
32 "metadata": {},
33 "outputs": [],
3432 "source": [
3533 "import colorcet as cc\n",
3634 "\n",
3735 "cc.fire[:5]"
38 ]
36 ],
37 "outputs": [],
38 "metadata": {}
3939 },
4040 {
4141 "cell_type": "markdown",
42 "metadata": {},
4342 "source": [
4443 "## Plotting\n",
4544 "\n",
4645 "For ease of use, we also provide minimal plotting commands for use with colorcet. These depend on holoviews, which needs to be installed before they can be used. Once set up, these commands provide easy viewing capability of the colormaps."
47 ]
46 ],
47 "metadata": {}
4848 },
4949 {
5050 "cell_type": "markdown",
51 "metadata": {},
5251 "source": [
5352 "#### Example\n",
5453 "\n",
5554 "Import `swatch` or `swatches` from `colorcet.plotting` and load your desired backend into holoviews. Then call `swatch` with the name of a colormap. "
56 ]
55 ],
56 "metadata": {}
5757 },
5858 {
5959 "cell_type": "code",
6060 "execution_count": null,
61 "metadata": {},
62 "outputs": [],
6361 "source": [
6462 "from colorcet.plotting import swatch, swatches\n",
6563 "import holoviews as hv\n",
6664 "hv.extension('matplotlib')"
67 ]
65 ],
66 "outputs": [],
67 "metadata": {}
6868 },
6969 {
7070 "cell_type": "code",
7171 "execution_count": null,
72 "metadata": {},
73 "outputs": [],
7472 "source": [
7573 "swatch('fire')"
76 ]
74 ],
75 "outputs": [],
76 "metadata": {}
7777 },
7878 {
7979 "cell_type": "markdown",
80 "metadata": {},
80 "source": [
81 "#### Using `colorcet` via `matplotlib.cm.get_cmap`\n",
82 "\n",
83 "The `colorcet` colormaps are all available through `matplotlip.cm.get_cmap` by prepending `cet_`to the colormap name. "
84 ],
85 "metadata": {}
86 },
87 {
88 "cell_type": "code",
89 "execution_count": null,
90 "source": [
91 "from matplotlib.cm import get_cmap\n",
92 "\n",
93 "get_cmap(\"cet_fire\")"
94 ],
95 "outputs": [],
96 "metadata": {}
97 },
98 {
99 "cell_type": "markdown",
100 "source": [
101 "#### Using `colorcet` to visualize custom colormaps using `swatch`"
102 ],
103 "metadata": {}
104 },
105 {
106 "cell_type": "code",
107 "execution_count": null,
108 "source": [
109 "color_list = ['#000000', '#380000', '#560000', '#760100', '#980300', '#bb0600', '#df0d00', '#f93500', '#fe6800', '#ff9100', '#ffb402', '#ffd407', '#fff324']\n",
110 "\n",
111 "swatch(name='custom', cmap=color_list)"
112 ],
113 "outputs": [],
114 "metadata": {}
115 },
116 {
117 "cell_type": "markdown",
81118 "source": [
82119 "## Complete list\n",
83120 "\n",
84121 "The `swatches` command accepts the optional key word argument `group` to show just the colormaps that fall under a particular group - try 'glasbey', 'circular', or 'diverging'. When no arguments are provided, `swatches` returns all of the colorcet colormaps."
85 ]
122 ],
123 "metadata": {}
86124 },
87125 {
88126 "cell_type": "code",
89127 "execution_count": null,
90 "metadata": {},
91 "outputs": [],
92128 "source": [
93129 "swatches()"
94 ]
130 ],
131 "outputs": [],
132 "metadata": {}
95133 },
96134 {
97135 "cell_type": "markdown",
98 "metadata": {},
99136 "source": [
100137 "For more explanation of the various options see [categorical](Categorical.ipynb) and [continuous](Continuous.ipynb)."
101 ]
138 ],
139 "metadata": {}
102140 }
103141 ],
104142 "metadata": {
105143 "language_info": {
106144 "name": "python",
107 "pygments_lexer": "ipython3"
145 "pygments_lexer": "ipython3",
146 "version": "3.7.11"
147 },
148 "kernelspec": {
149 "name": "python3",
150 "display_name": "Python 3.7.11 64-bit ('colorcet-dev37': conda)"
151 },
152 "interpreter": {
153 "hash": "4f854a078010334f958b1feab68ee1edfeebf6c4ffa4549f8460648dec38ebd8"
108154 }
109155 },
110156 "nbformat": 4,
111157 "nbformat_minor": 2
112 }
158 }
22
33 [wheel]
44 universal = 1
5
6 [egg_info]
7 tag_build =
8 tag_date = 0
9
4747 'tests': tests,
4848 'examples': examples,
4949 'doc': examples + [
50 'nbsite',
51 'sphinx_ioam_theme',
50 'nbsite >=0.6.1',
51 'sphinx_holoviz_theme',
5252 ],
5353 'tests_extra': tests + [
5454 'pytest-mpl' # only available on pip and conda-forge
7474 license_file='LICENSE.txt',
7575 license='CC-BY License',
7676 classifiers=[
77 "License :: Other/Proprietary License",
77 "License :: OSI Approved",
7878 "Operating System :: OS Independent",
7979 "Programming Language :: Python",
8080 "Programming Language :: Python :: 2.7",
8181 "Programming Language :: Python :: 3.6",
8282 "Programming Language :: Python :: 3.7",
83 "Programming Language :: Python :: 3.8",
8384 "Development Status :: 5 - Production/Stable",
8485 ],
8586 author="James A. Bednar",
8687 author_email="jbednar@anaconda.com",
8788 maintainer="James A. Bednar",
8889 maintainer_email="jbednar@anaconda.com",
89 url="https://colorcet.pyviz.org",
90 url="https://colorcet.holoviz.org",
9091 project_urls = {
91 "Bug Tracker": "http://github.com/pyviz/colorcet/issues",
92 "Documentation": "https://colorcet.pyviz.org",
93 "Source Code": "http://github.com/pyviz/colorcet",
92 "Bug Tracker": "http://github.com/holoviz/colorcet/issues",
93 "Documentation": "https://colorcet.holoviz.org",
94 "Source Code": "http://github.com/holoviz/colorcet",
9495 },
9596 include_package_data=True,
9697 packages=find_packages(),
+0
-58
tox.ini less more
0 # For use with pyctdev (but should work with tox alone)
1
2 [tox]
3 envlist = {py27,py36,py37}-{lint,unit,unit_extra,examples,all}-{default}-{dev,pkg}
4 build = wheel
5
6 [_lint]
7 description = Lint check python code and notebooks
8 deps = .[tests]
9 commands = flake8
10 pytest --nbsmoke-lint -k ".ipynb"
11
12 [_unit]
13 description = There are just some basic unit tests so far
14 deps = .[tests]
15 commands = pytest colorcet
16
17 [_unit_extra]
18 description = Run the unit tests with pytest-mpl and matplotlib
19 deps = .[examples,tests_extra]
20 commands = pytest colorcet --mpl
21
22 [_examples]
23 description = Test that the examples run without exceptions
24 deps = .[examples,tests]
25 commands = pytest --nbsmoke-run -k ".ipynb"
26
27 [_all]
28 description = Run all the tests
29 deps = .[examples, tests]
30 commands = {[_lint]commands}
31 {[_unit]commands}
32 {[_examples]commands}
33
34 [testenv]
35 changedir = {envtmpdir}
36
37 commands = unit: {[_unit]commands}
38 unit_extra: {[_unit_extra]commands}
39 lint: {[_lint]commands}
40 examples: {[_examples]commands}
41 all: {[_all]commands}
42
43 deps = unit: {[_unit]deps}
44 unit_extra: {[_unit_extra]deps}
45 lint: {[_lint]deps}
46 examples: {[_examples]deps}
47 all: {[_all]deps}
48
49 [pytest]
50 addopts = -v --pyargs
51 norecursedirs = doc .git dist build _build .ipynb_checkpoints
52
53 [flake8]
54 include = *.py
55 exclude = .git,__pycache__,.tox,.eggs,*.egg,doc,dist,build,_build,.ipynb_checkpoints,run_test.py
56 ignore = E,
57 W