path: root/python-cvsslib.spec

%global _empty_manifest_terminate_build 0
Name:		python-cvsslib
Version:	1.0.0
Release:	1
Summary:	CVSS 2/3 utilities
License:	GPL-3.0-or-later
URL:		https://github.com/orf/cvsslib/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c1/8a/1f26cf24ad993dd01c4ce42f3e15d1ed09e5263fdacdaa5776f3e6c43adb/cvsslib-1.0.0.tar.gz
BuildArch:	noarch


%description
# CVSSlib ![Main workflow](https://github.com/orf/cvsslib/workflows/Tests/badge.svg)

A Python 3 library for calculating CVSS v2, CVSS v3 and CVSS v3.1 vectors, with tests. Examples on how to use
the library is shown below, and there is some documentation on the internals within the `docs` directory. The library 
is designed to be completely extendable, so it is possible to implement your own custom scoring systems (or those of your clients)
and have it work with the same API, and with the same bells and whistles.

**Python 3 only**

## API

It's pretty simple to use. `cvsslib` has a `cvss2`, `cvss3` and `cvss31` sub modules that contains all of the enums
and calculation code. There are also some functions to manipulate vectors that take these cvss modules
as arguments. E.G:

```python
from cvsslib import cvss2, cvss31, calculate_vector

vector_v2 = "AV:L/AC:M/Au:S/C:N/I:P/A:C/E:U/RL:OF/RC:UR/CDP:N/TD:L/CR:H/IR:H/AR:H"
calculate_vector(vector_v2, cvss2)
>> (5, 3.5, 1.2)

vector_v3 = "CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N"
calculate_vector(vector_v3, cvss31)
>> (5.8, 5.8, 7.1)
```

You can access every CVSS enum through the `cvss2`, `cvss3` or `cvss31` modules:

```python
from cvsslib import cvss2
# In this case doing from 'cvsslib.cvss2.enums import *' might be less verbose.
value = cvss2.ReportConfidence.CONFIRMED

if value != cvss2.ReportConfidence.NOT_DEFINED:
    do_something()
```  
        
There are some powerful mixin functions if you need a class with CVSS members. These functions
take a cvss version and return a base class you can inherit from. This class hassome utility functions like 
`to_vector()` and `from_vector()` you can use.

```python
from cvsslib import cvss3, class_mixin

BaseClass = class_mixin(cvss3)  # Can pass cvss2 module instead

class SomeObject(BaseClass):
    def print_stats(self):
        for item, value in self.enums:
            print("{0} is {1}".format(item, value)
 
state = SomeObject()
print("\n".join(state.debug()))
print(state.calculate())
state.from_vector("CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N")
print("Vector: " + state.to_vector())

# Access members:
if state.report_confidence == ReportConfidence.NOT_DEFINED:
    do_something()
```

It also supports Django models. Requires the `django-enumfields` package.

```python
from cvsslib.contrib.django_model import django_mixin
from cvsslib import cvss2
from django.db import models

CVSSBase = django_mixin(cvss2)

class CVSSModel(models.Model, metaclass=CVSSBase)
    pass
    
# CVSSModel now has lots of enum you can use
x = CVSSModel()
x.save()
x.exploitability
```

If you want it to work with django Migrations you need to give an attribute name to the `django_mixin` function. This
should match the attribute name it is being assigned to:

```python
CVSSBase = django_mixin(cvss2, attr_name="CVSSBase")
```
 
And there is a command line tool available:
 
```python
> cvss CVSS:3.0/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:N/E:P/RL:U/RC:U/CR:H/IR:L/AR:H/MAV:L/MUI:R/MS:C/MC:N/MI:L/MA:N
Base Score:     5.3
Temporal:       4.6
Environment:    1.3
```
 
## Custom Scoring Systems

Creating a new scoring system is very simple. First create a Python file with the correct name, e.g `super_scores.py`. 
Next create some enums with the correct values for your system:
 
```python
 from cvsslib.base_enum import BaseEnum
 
 
 class Risk(BaseEnum):
     """
     Vector: S
     """
     HIGH = 1
     MEDIUM = 2
     LOW = 3
     
 class Difficulty(BaseEnum):
     """
     Vector: D
     """
     DIFFICULT = 1
     MODERATE = 2
     EASY = 3
```
 
And lastly add a `calculate` function in the module that accepts some vector values and 
returns a result of some kind:

```python

def calculate(difficulty: Difficulty, risk: Risk):
   if difficulty == Difficulty.EASY and risk == Risk.CRITICAL:
       return "oh nuts you're screwed"
   
   return "You're probs ok m8"
```

Once you define this you can pass your `super_scores` module to any 
cvsslib function like `calculate_vector` or `django_mixin` and it will 
all just work. You can even serialize the data to and from a vector 
if you define the correct `vector: X` in the enum docstrings.


%package -n python3-cvsslib
Summary:	CVSS 2/3 utilities
Provides:	python-cvsslib
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-cvsslib
# CVSSlib ![Main workflow](https://github.com/orf/cvsslib/workflows/Tests/badge.svg)

A Python 3 library for calculating CVSS v2, CVSS v3 and CVSS v3.1 vectors, with tests. Examples on how to use
the library is shown below, and there is some documentation on the internals within the `docs` directory. The library 
is designed to be completely extendable, so it is possible to implement your own custom scoring systems (or those of your clients)
and have it work with the same API, and with the same bells and whistles.

**Python 3 only**

## API

It's pretty simple to use. `cvsslib` has a `cvss2`, `cvss3` and `cvss31` sub modules that contains all of the enums
and calculation code. There are also some functions to manipulate vectors that take these cvss modules
as arguments. E.G:

```python
from cvsslib import cvss2, cvss31, calculate_vector

vector_v2 = "AV:L/AC:M/Au:S/C:N/I:P/A:C/E:U/RL:OF/RC:UR/CDP:N/TD:L/CR:H/IR:H/AR:H"
calculate_vector(vector_v2, cvss2)
>> (5, 3.5, 1.2)

vector_v3 = "CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N"
calculate_vector(vector_v3, cvss31)
>> (5.8, 5.8, 7.1)
```

You can access every CVSS enum through the `cvss2`, `cvss3` or `cvss31` modules:

```python
from cvsslib import cvss2
# In this case doing from 'cvsslib.cvss2.enums import *' might be less verbose.
value = cvss2.ReportConfidence.CONFIRMED

if value != cvss2.ReportConfidence.NOT_DEFINED:
    do_something()
```  
        
There are some powerful mixin functions if you need a class with CVSS members. These functions
take a cvss version and return a base class you can inherit from. This class hassome utility functions like 
`to_vector()` and `from_vector()` you can use.

```python
from cvsslib import cvss3, class_mixin

BaseClass = class_mixin(cvss3)  # Can pass cvss2 module instead

class SomeObject(BaseClass):
    def print_stats(self):
        for item, value in self.enums:
            print("{0} is {1}".format(item, value)
 
state = SomeObject()
print("\n".join(state.debug()))
print(state.calculate())
state.from_vector("CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N")
print("Vector: " + state.to_vector())

# Access members:
if state.report_confidence == ReportConfidence.NOT_DEFINED:
    do_something()
```

It also supports Django models. Requires the `django-enumfields` package.

```python
from cvsslib.contrib.django_model import django_mixin
from cvsslib import cvss2
from django.db import models

CVSSBase = django_mixin(cvss2)

class CVSSModel(models.Model, metaclass=CVSSBase)
    pass
    
# CVSSModel now has lots of enum you can use
x = CVSSModel()
x.save()
x.exploitability
```

If you want it to work with django Migrations you need to give an attribute name to the `django_mixin` function. This
should match the attribute name it is being assigned to:

```python
CVSSBase = django_mixin(cvss2, attr_name="CVSSBase")
```
 
And there is a command line tool available:
 
```python
> cvss CVSS:3.0/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:N/E:P/RL:U/RC:U/CR:H/IR:L/AR:H/MAV:L/MUI:R/MS:C/MC:N/MI:L/MA:N
Base Score:     5.3
Temporal:       4.6
Environment:    1.3
```
 
## Custom Scoring Systems

Creating a new scoring system is very simple. First create a Python file with the correct name, e.g `super_scores.py`. 
Next create some enums with the correct values for your system:
 
```python
 from cvsslib.base_enum import BaseEnum
 
 
 class Risk(BaseEnum):
     """
     Vector: S
     """
     HIGH = 1
     MEDIUM = 2
     LOW = 3
     
 class Difficulty(BaseEnum):
     """
     Vector: D
     """
     DIFFICULT = 1
     MODERATE = 2
     EASY = 3
```
 
And lastly add a `calculate` function in the module that accepts some vector values and 
returns a result of some kind:

```python

def calculate(difficulty: Difficulty, risk: Risk):
   if difficulty == Difficulty.EASY and risk == Risk.CRITICAL:
       return "oh nuts you're screwed"
   
   return "You're probs ok m8"
```

Once you define this you can pass your `super_scores` module to any 
cvsslib function like `calculate_vector` or `django_mixin` and it will 
all just work. You can even serialize the data to and from a vector 
if you define the correct `vector: X` in the enum docstrings.


%package help
Summary:	Development documents and examples for cvsslib
Provides:	python3-cvsslib-doc
%description help
# CVSSlib ![Main workflow](https://github.com/orf/cvsslib/workflows/Tests/badge.svg)

A Python 3 library for calculating CVSS v2, CVSS v3 and CVSS v3.1 vectors, with tests. Examples on how to use
the library is shown below, and there is some documentation on the internals within the `docs` directory. The library 
is designed to be completely extendable, so it is possible to implement your own custom scoring systems (or those of your clients)
and have it work with the same API, and with the same bells and whistles.

**Python 3 only**

## API

It's pretty simple to use. `cvsslib` has a `cvss2`, `cvss3` and `cvss31` sub modules that contains all of the enums
and calculation code. There are also some functions to manipulate vectors that take these cvss modules
as arguments. E.G:

```python
from cvsslib import cvss2, cvss31, calculate_vector

vector_v2 = "AV:L/AC:M/Au:S/C:N/I:P/A:C/E:U/RL:OF/RC:UR/CDP:N/TD:L/CR:H/IR:H/AR:H"
calculate_vector(vector_v2, cvss2)
>> (5, 3.5, 1.2)

vector_v3 = "CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N"
calculate_vector(vector_v3, cvss31)
>> (5.8, 5.8, 7.1)
```

You can access every CVSS enum through the `cvss2`, `cvss3` or `cvss31` modules:

```python
from cvsslib import cvss2
# In this case doing from 'cvsslib.cvss2.enums import *' might be less verbose.
value = cvss2.ReportConfidence.CONFIRMED

if value != cvss2.ReportConfidence.NOT_DEFINED:
    do_something()
```  
        
There are some powerful mixin functions if you need a class with CVSS members. These functions
take a cvss version and return a base class you can inherit from. This class hassome utility functions like 
`to_vector()` and `from_vector()` you can use.

```python
from cvsslib import cvss3, class_mixin

BaseClass = class_mixin(cvss3)  # Can pass cvss2 module instead

class SomeObject(BaseClass):
    def print_stats(self):
        for item, value in self.enums:
            print("{0} is {1}".format(item, value)
 
state = SomeObject()
print("\n".join(state.debug()))
print(state.calculate())
state.from_vector("CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N")
print("Vector: " + state.to_vector())

# Access members:
if state.report_confidence == ReportConfidence.NOT_DEFINED:
    do_something()
```

It also supports Django models. Requires the `django-enumfields` package.

```python
from cvsslib.contrib.django_model import django_mixin
from cvsslib import cvss2
from django.db import models

CVSSBase = django_mixin(cvss2)

class CVSSModel(models.Model, metaclass=CVSSBase)
    pass
    
# CVSSModel now has lots of enum you can use
x = CVSSModel()
x.save()
x.exploitability
```

If you want it to work with django Migrations you need to give an attribute name to the `django_mixin` function. This
should match the attribute name it is being assigned to:

```python
CVSSBase = django_mixin(cvss2, attr_name="CVSSBase")
```
 
And there is a command line tool available:
 
```python
> cvss CVSS:3.0/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:N/E:P/RL:U/RC:U/CR:H/IR:L/AR:H/MAV:L/MUI:R/MS:C/MC:N/MI:L/MA:N
Base Score:     5.3
Temporal:       4.6
Environment:    1.3
```
 
## Custom Scoring Systems

Creating a new scoring system is very simple. First create a Python file with the correct name, e.g `super_scores.py`. 
Next create some enums with the correct values for your system:
 
```python
 from cvsslib.base_enum import BaseEnum
 
 
 class Risk(BaseEnum):
     """
     Vector: S
     """
     HIGH = 1
     MEDIUM = 2
     LOW = 3
     
 class Difficulty(BaseEnum):
     """
     Vector: D
     """
     DIFFICULT = 1
     MODERATE = 2
     EASY = 3
```
 
And lastly add a `calculate` function in the module that accepts some vector values and 
returns a result of some kind:

```python

def calculate(difficulty: Difficulty, risk: Risk):
   if difficulty == Difficulty.EASY and risk == Risk.CRITICAL:
       return "oh nuts you're screwed"
   
   return "You're probs ok m8"
```

Once you define this you can pass your `super_scores` module to any 
cvsslib function like `calculate_vector` or `django_mixin` and it will 
all just work. You can even serialize the data to and from a vector 
if you define the correct `vector: X` in the enum docstrings.


%prep
%autosetup -n cvsslib-1.0.0

%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-cvsslib -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
- Package Spec generated