automatic import of python-colorspacious

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+/colorspacious-1.1.2.tar.gz
diff --git a/python-colorspacious.spec b/python-colorspacious.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-colorspacious
+Version:	1.1.2
+Release:	1
+Summary:	A powerful, accurate, and easy-to-use Python library for doing colorspace conversions
+License:	MIT
+URL:		https://github.com/njsmith/colorspacious
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+
+%description
+Colorspacious is a powerful, accurate, and easy-to-use library for
+performing colorspace conversions.
+In addition to the most common standard colorspaces (sRGB, XYZ, xyY,
+CIELab, CIELCh), we also include: color vision deficiency ("color
+blindness") simulations using the approach of Machado et al (2009); a
+complete implementation of `CIECAM02
+<https://en.wikipedia.org/wiki/CIECAM02>`_; and the perceptually
+uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al
+(2006).
+To get started, simply write::
+  from colorspacious import cspace_convert
+  Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS")
+This converts an sRGB value (represented as integers between 0-255) to
+CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing
+conditions by default). This requires passing through 4 intermediate
+colorspaces; ``cspace_convert`` automatically finds the optimal route
+and applies all conversions in sequence:
+This function also of course accepts arbitrary NumPy arrays, so
+converting a whole image is just as easy as converting a single value.
+Documentation:
+  http://colorspacious.readthedocs.org/
+Installation:
+  ``pip install colorspacious``
+Downloads:
+  https://pypi.python.org/pypi/colorspacious/
+Code and bug tracker:
+  https://github.com/njsmith/colorspacious
+Contact:
+  Nathaniel J. Smith <njs@pobox.com>
+Dependencies:
+  * Python 2.6+, or 3.3+
+  * NumPy
+Developer dependencies (only needed for hacking on source):
+  * nose: needed to run tests
+License:
+  MIT, see LICENSE.txt for details.
+References for algorithms we implement:
+  * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on
+    CIECAM02 colour appearance model. Color Research & Application, 31(4),
+    320–330. doi:10.1002/col.20227
+  * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A
+    physiologically-based model for simulation of color vision
+    deficiency. Visualization and Computer Graphics, IEEE Transactions on,
+    15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html
+Other Python packages with similar functionality that you might want
+to check out as well or instead:
+* ``colour``: http://colour-science.org/
+* ``colormath``: http://python-colormath.readthedocs.org/
+* ``ciecam02``: https://pypi.python.org/pypi/ciecam02/
+* ``ColorPy``: http://markkness.net/colorpy/ColorPy.html
+
+%package -n python3-colorspacious
+Summary:	A powerful, accurate, and easy-to-use Python library for doing colorspace conversions
+Provides:	python-colorspacious
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-colorspacious
+Colorspacious is a powerful, accurate, and easy-to-use library for
+performing colorspace conversions.
+In addition to the most common standard colorspaces (sRGB, XYZ, xyY,
+CIELab, CIELCh), we also include: color vision deficiency ("color
+blindness") simulations using the approach of Machado et al (2009); a
+complete implementation of `CIECAM02
+<https://en.wikipedia.org/wiki/CIECAM02>`_; and the perceptually
+uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al
+(2006).
+To get started, simply write::
+  from colorspacious import cspace_convert
+  Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS")
+This converts an sRGB value (represented as integers between 0-255) to
+CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing
+conditions by default). This requires passing through 4 intermediate
+colorspaces; ``cspace_convert`` automatically finds the optimal route
+and applies all conversions in sequence:
+This function also of course accepts arbitrary NumPy arrays, so
+converting a whole image is just as easy as converting a single value.
+Documentation:
+  http://colorspacious.readthedocs.org/
+Installation:
+  ``pip install colorspacious``
+Downloads:
+  https://pypi.python.org/pypi/colorspacious/
+Code and bug tracker:
+  https://github.com/njsmith/colorspacious
+Contact:
+  Nathaniel J. Smith <njs@pobox.com>
+Dependencies:
+  * Python 2.6+, or 3.3+
+  * NumPy
+Developer dependencies (only needed for hacking on source):
+  * nose: needed to run tests
+License:
+  MIT, see LICENSE.txt for details.
+References for algorithms we implement:
+  * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on
+    CIECAM02 colour appearance model. Color Research & Application, 31(4),
+    320–330. doi:10.1002/col.20227
+  * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A
+    physiologically-based model for simulation of color vision
+    deficiency. Visualization and Computer Graphics, IEEE Transactions on,
+    15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html
+Other Python packages with similar functionality that you might want
+to check out as well or instead:
+* ``colour``: http://colour-science.org/
+* ``colormath``: http://python-colormath.readthedocs.org/
+* ``ciecam02``: https://pypi.python.org/pypi/ciecam02/
+* ``ColorPy``: http://markkness.net/colorpy/ColorPy.html
+
+%package help
+Summary:	Development documents and examples for colorspacious
+Provides:	python3-colorspacious-doc
+%description help
+Colorspacious is a powerful, accurate, and easy-to-use library for
+performing colorspace conversions.
+In addition to the most common standard colorspaces (sRGB, XYZ, xyY,
+CIELab, CIELCh), we also include: color vision deficiency ("color
+blindness") simulations using the approach of Machado et al (2009); a
+complete implementation of `CIECAM02
+<https://en.wikipedia.org/wiki/CIECAM02>`_; and the perceptually
+uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al
+(2006).
+To get started, simply write::
+  from colorspacious import cspace_convert
+  Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS")
+This converts an sRGB value (represented as integers between 0-255) to
+CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing
+conditions by default). This requires passing through 4 intermediate
+colorspaces; ``cspace_convert`` automatically finds the optimal route
+and applies all conversions in sequence:
+This function also of course accepts arbitrary NumPy arrays, so
+converting a whole image is just as easy as converting a single value.
+Documentation:
+  http://colorspacious.readthedocs.org/
+Installation:
+  ``pip install colorspacious``
+Downloads:
+  https://pypi.python.org/pypi/colorspacious/
+Code and bug tracker:
+  https://github.com/njsmith/colorspacious
+Contact:
+  Nathaniel J. Smith <njs@pobox.com>
+Dependencies:
+  * Python 2.6+, or 3.3+
+  * NumPy
+Developer dependencies (only needed for hacking on source):
+  * nose: needed to run tests
+License:
+  MIT, see LICENSE.txt for details.
+References for algorithms we implement:
+  * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on
+    CIECAM02 colour appearance model. Color Research & Application, 31(4),
+    320–330. doi:10.1002/col.20227
+  * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A
+    physiologically-based model for simulation of color vision
+    deficiency. Visualization and Computer Graphics, IEEE Transactions on,
+    15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html
+Other Python packages with similar functionality that you might want
+to check out as well or instead:
+* ``colour``: http://colour-science.org/
+* ``colormath``: http://python-colormath.readthedocs.org/
+* ``ciecam02``: https://pypi.python.org/pypi/ciecam02/
+* ``ColorPy``: http://markkness.net/colorpy/ColorPy.html
+
+%prep
+%autosetup -n colorspacious-1.1.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-colorspacious -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c365f94
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+2f457686bd0afb8b0816b68cd903b8f9  colorspacious-1.1.2.tar.gz