%global _empty_manifest_terminate_build 0 Name: python-MonsterLab Version: 1.2.7 Release: 1 Summary: Monster Generator License: Free for non-commercial use URL: https://github.com/BrokenShell/MonsterLab Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1c/f0/d251b7300022e5fb1941f786d473211bf0c5d8e4758629515fbc7052f89e/MonsterLab-1.2.7.tar.gz BuildArch: noarch Requires: python3-pytz %description # MonsterLab by Robert Sharp ## Monster Class ### Optional Inputs It is recommended to pass all the optional arguments or none of them. For example, a custom type requires a custom name. - Name: Compound Gaussian Distribution -> String - Derived from Type - Multidimensional distribution of types and subtypes - Type: Wide Flat Distribution -> String - Demonic - Devilkin - Dragon - Undead - Elemental - Fey - Undead - Level: Poisson Distribution -> Integer - Range: [1..20] - Most Common: [4..7] ~64% - Mean: 6.001 - Median: 6 - Rarity: Linear Distribution [Rank 0..Rank 5] -> String - Rank 0: 30.5% Very Common - Rank 1: 25.0% Common - Rank 2: 19.4% Uncommon - Rank 3: 13.8% Rare - Rank 4: 8.3% Epic - Rank 5: 2.7% Legendary ### Derived Fields - Damage: Compound Geometric Distribution with Linear Noise -> String - Derived from Level and Rarity - Health: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Energy: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Sanity: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Time Stamp: The Monster's Birthday -> String ### Example Monster - Name: Revenant - Type: Undead - Level: 3 - Rarity: Rank 0 - Damage: 3d2+1 - Health: 6.35 - Energy: 5.78 - Sanity: 6.0 - Time Stamp: 2021-08-09 14:23:23 ### Code Example ``` $ pip install MonsterLab $ python3 >>> from MonsterLab import Monster >>> Monster() Name: Imp Type: Demonic Level: 10 Rarity: Rank 0 Damage: 10d2+1 Health: 20.89 Energy: 20.55 Sanity: 20.79 Time Stamp: 2021-08-09 14:23:23 ``` %package -n python3-MonsterLab Summary: Monster Generator Provides: python-MonsterLab BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-MonsterLab # MonsterLab by Robert Sharp ## Monster Class ### Optional Inputs It is recommended to pass all the optional arguments or none of them. For example, a custom type requires a custom name. - Name: Compound Gaussian Distribution -> String - Derived from Type - Multidimensional distribution of types and subtypes - Type: Wide Flat Distribution -> String - Demonic - Devilkin - Dragon - Undead - Elemental - Fey - Undead - Level: Poisson Distribution -> Integer - Range: [1..20] - Most Common: [4..7] ~64% - Mean: 6.001 - Median: 6 - Rarity: Linear Distribution [Rank 0..Rank 5] -> String - Rank 0: 30.5% Very Common - Rank 1: 25.0% Common - Rank 2: 19.4% Uncommon - Rank 3: 13.8% Rare - Rank 4: 8.3% Epic - Rank 5: 2.7% Legendary ### Derived Fields - Damage: Compound Geometric Distribution with Linear Noise -> String - Derived from Level and Rarity - Health: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Energy: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Sanity: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Time Stamp: The Monster's Birthday -> String ### Example Monster - Name: Revenant - Type: Undead - Level: 3 - Rarity: Rank 0 - Damage: 3d2+1 - Health: 6.35 - Energy: 5.78 - Sanity: 6.0 - Time Stamp: 2021-08-09 14:23:23 ### Code Example ``` $ pip install MonsterLab $ python3 >>> from MonsterLab import Monster >>> Monster() Name: Imp Type: Demonic Level: 10 Rarity: Rank 0 Damage: 10d2+1 Health: 20.89 Energy: 20.55 Sanity: 20.79 Time Stamp: 2021-08-09 14:23:23 ``` %package help Summary: Development documents and examples for MonsterLab Provides: python3-MonsterLab-doc %description help # MonsterLab by Robert Sharp ## Monster Class ### Optional Inputs It is recommended to pass all the optional arguments or none of them. For example, a custom type requires a custom name. - Name: Compound Gaussian Distribution -> String - Derived from Type - Multidimensional distribution of types and subtypes - Type: Wide Flat Distribution -> String - Demonic - Devilkin - Dragon - Undead - Elemental - Fey - Undead - Level: Poisson Distribution -> Integer - Range: [1..20] - Most Common: [4..7] ~64% - Mean: 6.001 - Median: 6 - Rarity: Linear Distribution [Rank 0..Rank 5] -> String - Rank 0: 30.5% Very Common - Rank 1: 25.0% Common - Rank 2: 19.4% Uncommon - Rank 3: 13.8% Rare - Rank 4: 8.3% Epic - Rank 5: 2.7% Legendary ### Derived Fields - Damage: Compound Geometric Distribution with Linear Noise -> String - Derived from Level and Rarity - Health: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Energy: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Sanity: Geometric Distribution with Gaussian Noise -> Float - Derived from Level and Rarity - Time Stamp: The Monster's Birthday -> String ### Example Monster - Name: Revenant - Type: Undead - Level: 3 - Rarity: Rank 0 - Damage: 3d2+1 - Health: 6.35 - Energy: 5.78 - Sanity: 6.0 - Time Stamp: 2021-08-09 14:23:23 ### Code Example ``` $ pip install MonsterLab $ python3 >>> from MonsterLab import Monster >>> Monster() Name: Imp Type: Demonic Level: 10 Rarity: Rank 0 Damage: 10d2+1 Health: 20.89 Energy: 20.55 Sanity: 20.79 Time Stamp: 2021-08-09 14:23:23 ``` %prep %autosetup -n MonsterLab-1.2.7 %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-MonsterLab -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 1.2.7-1 - Package Spec generated