Hello community,
here is the log from the commit of package python-colorcet for openSUSE:Factory
checked in at 2019-09-26 20:46:37
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-colorcet (Old)
and /work/SRC/openSUSE:Factory/.python-colorcet.new.2352 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-colorcet"
Thu Sep 26 20:46:37 2019 rev:3 rq:733457 version:2.0.2
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-colorcet/python-colorcet.changes
2019-07-26 17:33:32.660105785 +0200
+++
/work/SRC/openSUSE:Factory/.python-colorcet.new.2352/python-colorcet.changes
2019-09-26 20:46:39.869671904 +0200
@@ -1,0 +2,6 @@
+Thu Sep 26 13:49:09 UTC 2019 - Tomáš Chvátal <[email protected]>
+
+- Update to 2.0.2:
+ * Typo fixes in metadata and small fixes only
+
+-------------------------------------------------------------------
Old:
----
colorcet-2.0.1.tar.gz
New:
----
colorcet-2.0.2.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-colorcet.spec ++++++
--- /var/tmp/diff_new_pack.oqXu3N/_old 2019-09-26 20:46:41.069668698 +0200
+++ /var/tmp/diff_new_pack.oqXu3N/_new 2019-09-26 20:46:41.085668655 +0200
@@ -12,31 +12,30 @@
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.
-# Please submit bugfixes or comments via http://bugs.opensuse.org/
+# Please submit bugfixes or comments via https://bugs.opensuse.org/
#
%{?!python_module:%define python_module() python-%{**} python3-%{**}}
Name: python-colorcet
-Version: 2.0.1
+Version: 2.0.2
Release: 0
Summary: Collection of perceptually uniform colormaps
License: CC-BY-4.0
Group: Development/Languages/Python
-Url: http://github.com/bokeh/colorcet
+URL: https://github.com/bokeh/colorcet
Source:
https://files.pythonhosted.org/packages/source/c/colorcet/colorcet-%{version}.tar.gz
BuildRequires: %{python_module param >= 1.7.0}
BuildRequires: %{python_module pyct >= 0.4.4}
BuildRequires: %{python_module setuptools >= 30.3.0}
BuildRequires: fdupes
BuildRequires: python-rpm-macros
-# SECTION test requirements
-BuildRequires: %{python_module pytest}
-# /SECTION
Requires: python-param >= 1.7.0
Requires: python-pyct >= 0.4.4
BuildArch: noarch
-
+# SECTION test requirements
+BuildRequires: %{python_module pytest}
+# /SECTION
%python_subpackages
%description
++++++ colorcet-2.0.1.tar.gz -> colorcet-2.0.2.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/PKG-INFO new/colorcet-2.0.2/PKG-INFO
--- old/colorcet-2.0.1/PKG-INFO 2019-04-03 19:15:57.000000000 +0200
+++ new/colorcet-2.0.2/PKG-INFO 2019-08-28 18:06:52.000000000 +0200
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: colorcet
-Version: 2.0.1
+Version: 2.0.2
Summary: Collection of perceptually uniform colormaps
Home-page: https://colorcet.pyviz.org
Author: James A. Bednar
@@ -21,9 +21,9 @@
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=2.7
-Provides-Extra: tests
-Provides-Extra: build
Provides-Extra: all
-Provides-Extra: examples
-Provides-Extra: tests_extra
Provides-Extra: doc
+Provides-Extra: build
+Provides-Extra: tests_extra
+Provides-Extra: examples
+Provides-Extra: tests
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/colorcet/.version
new/colorcet-2.0.2/colorcet/.version
--- old/colorcet-2.0.1/colorcet/.version 2019-04-03 19:15:57.000000000
+0200
+++ new/colorcet-2.0.2/colorcet/.version 2019-08-28 18:06:51.000000000
+0200
@@ -1 +1 @@
-{"git_describe": "v2.0.1-0-g8bcb27f", "version_string": "2.0.1"}
\ No newline at end of file
+{"git_describe": "v2.0.2-0-g77d8d77", "version_string": "2.0.2"}
\ No newline at end of file
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/colorcet/__init__.py
new/colorcet-2.0.2/colorcet/__init__.py
--- old/colorcet-2.0.1/colorcet/__init__.py 2019-04-03 19:15:16.000000000
+0200
+++ new/colorcet-2.0.2/colorcet/__init__.py 2019-08-28 18:06:12.000000000
+0200
@@ -670,8 +670,6 @@
cm['dimgray_r'] = m_linear_grey_10_95_c0_r
cm_n['dimgray'] = mpl_cm('dimgray',linear_grey_10_95_c0)
cm_n['dimgray_r'] = mpl_cm('dimgray_r',list(reversed(linear_grey_10_95_c0)))
-register_cmap('cet_dimgray',m_linear_grey_10_95_c0)
-register_cmap('cet_dimgray_r',m_linear_grey_10_95_c0_r)
CET_L2 = b_linear_grey_10_95_c0
m_CET_L2 = m_linear_grey_10_95_c0
m_CET_L2_r = m_linear_grey_10_95_c0_r
@@ -1227,8 +1225,6 @@
cm['bky_r'] = m_diverging_bky_60_10_c30_r
cm_n['bky'] = mpl_cm('bky',diverging_bky_60_10_c30)
cm_n['bky_r'] = mpl_cm('bky_r',list(reversed(diverging_bky_60_10_c30)))
-register_cmap('cet_bky',m_diverging_bky_60_10_c30)
-register_cmap('cet_bky_r',m_diverging_bky_60_10_c30_r)
CET_D6 = b_diverging_bky_60_10_c30
m_CET_D6 = m_diverging_bky_60_10_c30
m_CET_D6_r = m_diverging_bky_60_10_c30_r
@@ -1511,8 +1507,6 @@
cm['coolwarm_r'] = m_diverging_bwr_40_95_c42_r
cm_n['coolwarm'] = mpl_cm('coolwarm',diverging_bwr_40_95_c42)
cm_n['coolwarm_r'] =
mpl_cm('coolwarm_r',list(reversed(diverging_bwr_40_95_c42)))
-register_cmap('cet_coolwarm',m_diverging_bwr_40_95_c42)
-register_cmap('cet_coolwarm_r',m_diverging_bwr_40_95_c42_r)
CET_D1 = b_diverging_bwr_40_95_c42
m_CET_D1 = m_diverging_bwr_40_95_c42
m_CET_D1_r = m_diverging_bwr_40_95_c42_r
@@ -2068,9 +2062,6 @@
cm['kbc_r'] = m_linear_blue_5_95_c73_r
cm_n['kbc'] = mpl_cm('kbc',linear_blue_5_95_c73)
cm_n['kbc_r'] = mpl_cm('kbc_r',list(reversed(linear_blue_5_95_c73)))
-register_cmap('cet_kbc',m_linear_blue_5_95_c73)
-register_cmap('cet_kbc_r',m_linear_blue_5_95_c73_r)
-
@@ -2609,8 +2600,6 @@
cm['gwv_r'] = m_diverging_gwv_55_95_c39_r
cm_n['gwv'] = mpl_cm('gwv',diverging_gwv_55_95_c39)
cm_n['gwv_r'] = mpl_cm('gwv_r',list(reversed(diverging_gwv_55_95_c39)))
-register_cmap('cet_gwv',m_diverging_gwv_55_95_c39)
-register_cmap('cet_gwv_r',m_diverging_gwv_55_95_c39_r)
CET_D2 = b_diverging_gwv_55_95_c39
m_CET_D2 = m_diverging_gwv_55_95_c39
m_CET_D2_r = m_diverging_gwv_55_95_c39_r
@@ -3158,8 +3147,6 @@
cm['kg_r'] = m_linear_ternary_green_0_46_c42_r
cm_n['kg'] = mpl_cm('kg',linear_ternary_green_0_46_c42)
cm_n['kg_r'] = mpl_cm('kg_r',list(reversed(linear_ternary_green_0_46_c42)))
-register_cmap('cet_kg',m_linear_ternary_green_0_46_c42)
-register_cmap('cet_kg_r',m_linear_ternary_green_0_46_c42_r)
CET_L14 = b_linear_ternary_green_0_46_c42
m_CET_L14 = m_linear_ternary_green_0_46_c42
m_CET_L14_r = m_linear_ternary_green_0_46_c42_r
@@ -3980,8 +3967,6 @@
cm['colorwheel_r'] = m_cyclic_mygbm_30_95_c78_s25_r
cm_n['colorwheel'] = mpl_cm('colorwheel',cyclic_mygbm_30_95_c78_s25)
cm_n['colorwheel_r'] =
mpl_cm('colorwheel_r',list(reversed(cyclic_mygbm_30_95_c78_s25)))
-register_cmap('cet_colorwheel',m_cyclic_mygbm_30_95_c78_s25)
-register_cmap('cet_colorwheel_r',m_cyclic_mygbm_30_95_c78_s25_r)
CET_C2s = b_cyclic_mygbm_30_95_c78_s25
m_CET_C2s = m_cyclic_mygbm_30_95_c78_s25
m_CET_C2s_r = m_cyclic_mygbm_30_95_c78_s25_r
@@ -4537,8 +4522,6 @@
cm['bgyw_r'] = m_linear_bgyw_15_100_c68_r
cm_n['bgyw'] = mpl_cm('bgyw',linear_bgyw_15_100_c68)
cm_n['bgyw_r'] = mpl_cm('bgyw_r',list(reversed(linear_bgyw_15_100_c68)))
-register_cmap('cet_bgyw',m_linear_bgyw_15_100_c68)
-register_cmap('cet_bgyw_r',m_linear_bgyw_15_100_c68_r)
@@ -5905,8 +5888,6 @@
cm['gray_r'] = m_linear_grey_0_100_c0_r
cm_n['gray'] = mpl_cm('gray',linear_grey_0_100_c0)
cm_n['gray_r'] = mpl_cm('gray_r',list(reversed(linear_grey_0_100_c0)))
-register_cmap('cet_gray',m_linear_grey_0_100_c0)
-register_cmap('cet_gray_r',m_linear_grey_0_100_c0_r)
CET_L1 = b_linear_grey_0_100_c0
m_CET_L1 = m_linear_grey_0_100_c0
m_CET_L1_r = m_linear_grey_0_100_c0_r
@@ -6727,8 +6708,6 @@
cm['rainbow_r'] = m_rainbow_bgyr_35_85_c73_r
cm_n['rainbow'] = mpl_cm('rainbow',rainbow_bgyr_35_85_c73)
cm_n['rainbow_r'] = mpl_cm('rainbow_r',list(reversed(rainbow_bgyr_35_85_c73)))
-register_cmap('cet_rainbow',m_rainbow_bgyr_35_85_c73)
-register_cmap('cet_rainbow_r',m_rainbow_bgyr_35_85_c73_r)
@@ -7541,8 +7520,6 @@
cm['blues_r'] = m_linear_blue_95_50_c20_r
cm_n['blues'] = mpl_cm('blues',linear_blue_95_50_c20)
cm_n['blues_r'] = mpl_cm('blues_r',list(reversed(linear_blue_95_50_c20)))
-register_cmap('cet_blues',m_linear_blue_95_50_c20)
-register_cmap('cet_blues_r',m_linear_blue_95_50_c20_r)
CET_L12 = b_linear_blue_95_50_c20
m_CET_L12 = m_linear_blue_95_50_c20
m_CET_L12_r = m_linear_blue_95_50_c20_r
@@ -7825,8 +7802,6 @@
cm['kr_r'] = m_linear_ternary_red_0_50_c52_r
cm_n['kr'] = mpl_cm('kr',linear_ternary_red_0_50_c52)
cm_n['kr_r'] = mpl_cm('kr_r',list(reversed(linear_ternary_red_0_50_c52)))
-register_cmap('cet_kr',m_linear_ternary_red_0_50_c52)
-register_cmap('cet_kr_r',m_linear_ternary_red_0_50_c52_r)
CET_L13 = b_linear_ternary_red_0_50_c52
m_CET_L13 = m_linear_ternary_red_0_50_c52
m_CET_L13_r = m_linear_ternary_red_0_50_c52_r
@@ -8912,8 +8887,6 @@
cm['fire_r'] = m_linear_kryw_0_100_c71_r
cm_n['fire'] = mpl_cm('fire',linear_kryw_0_100_c71)
cm_n['fire_r'] = mpl_cm('fire_r',list(reversed(linear_kryw_0_100_c71)))
-register_cmap('cet_fire',m_linear_kryw_0_100_c71)
-register_cmap('cet_fire_r',m_linear_kryw_0_100_c71_r)
CET_L3 = b_linear_kryw_0_100_c71
m_CET_L3 = m_linear_kryw_0_100_c71
m_CET_L3_r = m_linear_kryw_0_100_c71_r
@@ -9196,8 +9169,6 @@
cm['bkr_r'] = m_diverging_bkr_55_10_c35_r
cm_n['bkr'] = mpl_cm('bkr',diverging_bkr_55_10_c35)
cm_n['bkr_r'] = mpl_cm('bkr_r',list(reversed(diverging_bkr_55_10_c35)))
-register_cmap('cet_bkr',m_diverging_bkr_55_10_c35)
-register_cmap('cet_bkr_r',m_diverging_bkr_55_10_c35_r)
CET_D4 = b_diverging_bkr_55_10_c35
m_CET_D4 = m_diverging_bkr_55_10_c35
m_CET_D4_r = m_diverging_bkr_55_10_c35_r
@@ -9480,8 +9451,7 @@
cm['kgy_r'] = m_linear_green_5_95_c69_r
cm_n['kgy'] = mpl_cm('kgy',linear_green_5_95_c69)
cm_n['kgy_r'] = mpl_cm('kgy_r',list(reversed(linear_green_5_95_c69)))
-register_cmap('cet_kgy',m_linear_green_5_95_c69)
-register_cmap('cet_kgy_r',m_linear_green_5_95_c69_r)
+
@@ -9756,8 +9726,6 @@
cm['bmw_r'] = m_linear_bmw_5_95_c89_r
cm_n['bmw'] = mpl_cm('bmw',linear_bmw_5_95_c89)
cm_n['bmw_r'] = mpl_cm('bmw_r',list(reversed(linear_bmw_5_95_c89)))
-register_cmap('cet_bmw',m_linear_bmw_5_95_c89)
-register_cmap('cet_bmw_r',m_linear_bmw_5_95_c89_r)
@@ -10305,8 +10273,6 @@
cm['isolum_r'] = m_isoluminant_cgo_80_c38_r
cm_n['isolum'] = mpl_cm('isolum',isoluminant_cgo_80_c38)
cm_n['isolum_r'] = mpl_cm('isolum_r',list(reversed(isoluminant_cgo_80_c38)))
-register_cmap('cet_isolum',m_isoluminant_cgo_80_c38)
-register_cmap('cet_isolum_r',m_isoluminant_cgo_80_c38_r)
CET_I2 = b_isoluminant_cgo_80_c38
m_CET_I2 = m_isoluminant_cgo_80_c38
m_CET_I2_r = m_isoluminant_cgo_80_c38_r
@@ -11408,8 +11374,6 @@
cm['bmy_r'] = m_linear_bmy_10_95_c78_r
cm_n['bmy'] = mpl_cm('bmy',linear_bmy_10_95_c78)
cm_n['bmy_r'] = mpl_cm('bmy_r',list(reversed(linear_bmy_10_95_c78)))
-register_cmap('cet_bmy',m_linear_bmy_10_95_c78)
-register_cmap('cet_bmy_r',m_linear_bmy_10_95_c78_r)
@@ -11957,8 +11921,6 @@
cm['kb_r'] = m_linear_ternary_blue_0_44_c57_r
cm_n['kb'] = mpl_cm('kb',linear_ternary_blue_0_44_c57)
cm_n['kb_r'] = mpl_cm('kb_r',list(reversed(linear_ternary_blue_0_44_c57)))
-register_cmap('cet_kb',m_linear_ternary_blue_0_44_c57)
-register_cmap('cet_kb_r',m_linear_ternary_blue_0_44_c57_r)
CET_L15 = b_linear_ternary_blue_0_44_c57
m_CET_L15 = m_linear_ternary_blue_0_44_c57
m_CET_L15_r = m_linear_ternary_blue_0_44_c57_r
@@ -13060,8 +13022,6 @@
cm['bgy_r'] = m_linear_bgy_10_95_c74_r
cm_n['bgy'] = mpl_cm('bgy',linear_bgy_10_95_c74)
cm_n['bgy_r'] = mpl_cm('bgy_r',list(reversed(linear_bgy_10_95_c74)))
-register_cmap('cet_bgy',m_linear_bgy_10_95_c74)
-register_cmap('cet_bgy_r',m_linear_bgy_10_95_c74_r)
@@ -14147,8 +14107,6 @@
cm['bjy_r'] = m_diverging_linear_bjy_30_90_c45_r
cm_n['bjy'] = mpl_cm('bjy',diverging_linear_bjy_30_90_c45)
cm_n['bjy_r'] = mpl_cm('bjy_r',list(reversed(diverging_linear_bjy_30_90_c45)))
-register_cmap('cet_bjy',m_diverging_linear_bjy_30_90_c45)
-register_cmap('cet_bjy_r',m_diverging_linear_bjy_30_90_c45_r)
CET_D7 = b_diverging_linear_bjy_30_90_c45
m_CET_D7 = m_diverging_linear_bjy_30_90_c45
m_CET_D7_r = m_diverging_linear_bjy_30_90_c45_r
@@ -16888,8 +16846,6 @@
cm['bwy_r'] = m_diverging_protanopic_deuteranopic_bwy_60_95_c32_r
cm_n['bwy'] = mpl_cm('bwy',diverging_protanopic_deuteranopic_bwy_60_95_c32)
cm_n['bwy_r'] =
mpl_cm('bwy_r',list(reversed(diverging_protanopic_deuteranopic_bwy_60_95_c32)))
-register_cmap('cet_bwy',m_diverging_protanopic_deuteranopic_bwy_60_95_c32)
-register_cmap('cet_bwy_r',m_diverging_protanopic_deuteranopic_bwy_60_95_c32_r)
CET_CBD1 = b_diverging_protanopic_deuteranopic_bwy_60_95_c32
m_CET_CBD1 = m_diverging_protanopic_deuteranopic_bwy_60_95_c32
m_CET_CBD1_r = m_diverging_protanopic_deuteranopic_bwy_60_95_c32_r
@@ -17172,8 +17128,6 @@
cm['cwr_r'] = m_diverging_tritanopic_cwr_75_98_c20_r
cm_n['cwr'] = mpl_cm('cwr',diverging_tritanopic_cwr_75_98_c20)
cm_n['cwr_r'] =
mpl_cm('cwr_r',list(reversed(diverging_tritanopic_cwr_75_98_c20)))
-register_cmap('cet_cwr',m_diverging_tritanopic_cwr_75_98_c20)
-register_cmap('cet_cwr_r',m_diverging_tritanopic_cwr_75_98_c20_r)
CET_CBTD1 = b_diverging_tritanopic_cwr_75_98_c20
m_CET_CBTD1 = m_diverging_tritanopic_cwr_75_98_c20
m_CET_CBTD1_r = m_diverging_tritanopic_cwr_75_98_c20_r
@@ -20178,8 +20132,6 @@
cm['glasbey_light_r'] = m_glasbey_bw_minc_20_minl_30_r
cm_n['glasbey_light'] = mpl_cm('glasbey_light',glasbey_bw_minc_20_minl_30)
cm_n['glasbey_light_r'] =
mpl_cm('glasbey_light_r',list(reversed(glasbey_bw_minc_20_minl_30)))
-register_cmap('cet_glasbey_light',m_glasbey_bw_minc_20_minl_30)
-register_cmap('cet_glasbey_light_r',m_glasbey_bw_minc_20_minl_30_r)
@@ -20454,8 +20406,6 @@
cm['glasbey_warm_r'] = m_glasbey_bw_minc_20_hue_330_100_r
cm_n['glasbey_warm'] = mpl_cm('glasbey_warm',glasbey_bw_minc_20_hue_330_100)
cm_n['glasbey_warm_r'] =
mpl_cm('glasbey_warm_r',list(reversed(glasbey_bw_minc_20_hue_330_100)))
-register_cmap('cet_glasbey_warm',m_glasbey_bw_minc_20_hue_330_100)
-register_cmap('cet_glasbey_warm_r',m_glasbey_bw_minc_20_hue_330_100_r)
@@ -20730,8 +20680,6 @@
cm['glasbey_r'] = m_glasbey_bw_minc_20_r
cm_n['glasbey'] = mpl_cm('glasbey',glasbey_bw_minc_20)
cm_n['glasbey_r'] = mpl_cm('glasbey_r',list(reversed(glasbey_bw_minc_20)))
-register_cmap('cet_glasbey',m_glasbey_bw_minc_20)
-register_cmap('cet_glasbey_r',m_glasbey_bw_minc_20_r)
@@ -21271,8 +21219,6 @@
cm['glasbey_dark_r'] = m_glasbey_bw_minc_20_maxl_70_r
cm_n['glasbey_dark'] = mpl_cm('glasbey_dark',glasbey_bw_minc_20_maxl_70)
cm_n['glasbey_dark_r'] =
mpl_cm('glasbey_dark_r',list(reversed(glasbey_bw_minc_20_maxl_70)))
-register_cmap('cet_glasbey_dark',m_glasbey_bw_minc_20_maxl_70)
-register_cmap('cet_glasbey_dark_r',m_glasbey_bw_minc_20_maxl_70_r)
@@ -21812,8 +21758,6 @@
cm['glasbey_cool_r'] = m_glasbey_bw_minc_20_hue_150_280_r
cm_n['glasbey_cool'] = mpl_cm('glasbey_cool',glasbey_bw_minc_20_hue_150_280)
cm_n['glasbey_cool_r'] =
mpl_cm('glasbey_cool_r',list(reversed(glasbey_bw_minc_20_hue_150_280)))
-register_cmap('cet_glasbey_cool',m_glasbey_bw_minc_20_hue_150_280)
-register_cmap('cet_glasbey_cool_r',m_glasbey_bw_minc_20_hue_150_280_r)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/colorcet/examples/index.ipynb
new/colorcet-2.0.2/colorcet/examples/index.ipynb
--- old/colorcet-2.0.1/colorcet/examples/index.ipynb 2019-04-03
19:15:16.000000000 +0200
+++ new/colorcet-2.0.2/colorcet/examples/index.ipynb 2019-08-28
18:06:12.000000000 +0200
@@ -105,10 +105,9 @@
"## Learning more\n",
"\n",
"You can see all the details about the methods used to create these\n",
- "colormaps in [Peter Kovesi's 2015 arXiv\n",
- "paper](https://arxiv.org/pdf/1509.03700v1.pdf) and in [Glasbey et al.
2007](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf). Other
useful\n",
- "background is available in a \n",
- "[1996 paper from
IBM](http://www.research.ibm.com/people/l/lloydt/color/color.HTM).\n",
+ "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",
+ "background is available in a research paper from IBM\n",
+ "[(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",
"\n",
"The Matplotlib project also has a number of relevant resources, including
an excellent \n",
"[2015 SciPy talk](https://www.youtube.com/watch?v=xAoljeRJ3lU), the\n",
@@ -125,13 +124,6 @@
"\n",
"But the complete set of 60+ is shown in the [User
Guide](./user_guide/index.ipynb)."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/colorcet-2.0.1/colorcet/examples/user_guide/Categorical.ipynb
new/colorcet-2.0.2/colorcet/examples/user_guide/Categorical.ipynb
--- old/colorcet-2.0.1/colorcet/examples/user_guide/Categorical.ipynb
2019-04-03 19:15:16.000000000 +0200
+++ new/colorcet-2.0.2/colorcet/examples/user_guide/Categorical.ipynb
2019-08-28 18:06:12.000000000 +0200
@@ -132,7 +132,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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 110°, 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:"
+ "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:"
]
},
{
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/colorcet/tests/test_matplotlib.py
new/colorcet-2.0.2/colorcet/tests/test_matplotlib.py
--- old/colorcet-2.0.1/colorcet/tests/test_matplotlib.py 2019-04-03
19:15:16.000000000 +0200
+++ new/colorcet-2.0.2/colorcet/tests/test_matplotlib.py 2019-08-28
18:06:12.000000000 +0200
@@ -41,3 +41,8 @@
from colorcet.plotting import swatch
fig = hv.render(swatch('kbc'), backend='matplotlib')
return fig
+
[email protected]('k,v', list(cc.cm.items()))
+def test_get_cm(k, v):
+ import matplotlib.cm as mcm
+ assert mcm.get_cmap('cet_' + k) is v
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/colorcet.egg-info/PKG-INFO
new/colorcet-2.0.2/colorcet.egg-info/PKG-INFO
--- old/colorcet-2.0.1/colorcet.egg-info/PKG-INFO 2019-04-03
19:15:57.000000000 +0200
+++ new/colorcet-2.0.2/colorcet.egg-info/PKG-INFO 2019-08-28
18:06:51.000000000 +0200
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: colorcet
-Version: 2.0.1
+Version: 2.0.2
Summary: Collection of perceptually uniform colormaps
Home-page: https://colorcet.pyviz.org
Author: James A. Bednar
@@ -21,9 +21,9 @@
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=2.7
-Provides-Extra: tests
-Provides-Extra: build
Provides-Extra: all
-Provides-Extra: examples
-Provides-Extra: tests_extra
Provides-Extra: doc
+Provides-Extra: build
+Provides-Extra: tests_extra
+Provides-Extra: examples
+Provides-Extra: tests
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/examples/index.ipynb
new/colorcet-2.0.2/examples/index.ipynb
--- old/colorcet-2.0.1/examples/index.ipynb 2019-04-03 19:15:16.000000000
+0200
+++ new/colorcet-2.0.2/examples/index.ipynb 2019-08-28 18:06:12.000000000
+0200
@@ -105,10 +105,9 @@
"## Learning more\n",
"\n",
"You can see all the details about the methods used to create these\n",
- "colormaps in [Peter Kovesi's 2015 arXiv\n",
- "paper](https://arxiv.org/pdf/1509.03700v1.pdf) and in [Glasbey et al.
2007](https://strathprints.strath.ac.uk/30312/1/colorpaper_2006.pdf). Other
useful\n",
- "background is available in a \n",
- "[1996 paper from
IBM](http://www.research.ibm.com/people/l/lloydt/color/color.HTM).\n",
+ "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",
+ "background is available in a research paper from IBM\n",
+ "[(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",
"\n",
"The Matplotlib project also has a number of relevant resources, including
an excellent \n",
"[2015 SciPy talk](https://www.youtube.com/watch?v=xAoljeRJ3lU), the\n",
@@ -125,13 +124,6 @@
"\n",
"But the complete set of 60+ is shown in the [User
Guide](./user_guide/index.ipynb)."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/colorcet-2.0.1/examples/user_guide/Categorical.ipynb
new/colorcet-2.0.2/examples/user_guide/Categorical.ipynb
--- old/colorcet-2.0.1/examples/user_guide/Categorical.ipynb 2019-04-03
19:15:16.000000000 +0200
+++ new/colorcet-2.0.2/examples/user_guide/Categorical.ipynb 2019-08-28
18:06:12.000000000 +0200
@@ -132,7 +132,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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 110°, 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:"
+ "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:"
]
},
{
