pypi prep (#73)

Author: coreyneskey

RELEASE_NOTES.rst

@@ -1,6 +1,14 @@
 Release Notes
 =============
 
+Version 0.1-alpha.13
+--------------------
+
+* Updated package metadata and dependencies
+* Improved package classifiers
+* Changed requires to install_requires for better dependency management
+* Added Python version requirement (>=3.6)
+
 Version 0.1-alpha.12
 --------------------
 

setup.cfg

@@ -1,2 +1,2 @@
 [metadata]
-description-file = README.md
+description-file = README.rst

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name="pyfair",
-    version="0.1-alpha.12",
+    version="0.1-alpha.13",
     description="Open FAIR Monte Carlo creator",
     long_description="""
         Factor Analysis of Information Risk (Open FAIR) model in Python.
@@ -19,7 +19,7 @@
 
     """,
     author="Hive Systems",
-    author_email="pyfair@hivesystems.io",
+    author_email="pyfair@hivesystems.com",
     packages=[
         "pyfair",
         "pyfair.model",
@@ -28,12 +28,15 @@
     ],
     license="MIT",
     url="https://github.com/Hive-Systems/pyfair",
-    keywords=["FAIR", "risk"],
+    keywords=["FAIR", "risk", "monte carlo", "cyber risk", "risk analysis"],
     classifiers=[
         "Development Status :: 3 - Alpha",
         "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Topic :: Security",
+        "Topic :: Scientific/Engineering :: Mathematics",
     ],
-    requires=[
+    install_requires=[
         "pandas",
         "numpy",
         "scipy",
@@ -42,4 +45,5 @@
     ],
     package_dir={"pyfair": "./pyfair"},
     package_data={"pyfair": ["./static/*"]},
+    python_requires=">=3.6",
 )

test_fair.py

@@ -0,0 +1,20 @@
+from pyfair import FairModel
+
+# Create a simple FAIR model
+model = FairModel(name="Basic Model", n_simulations=10000)
+
+# Set parameters
+model.input_data("Loss Event Frequency", mean=0.3, stdev=0.1)
+model.input_data("Loss Magnitude", constant=5000000)
+
+# Calculate and display results
+model.calculate_all()
+
+# Get results and print summary statistics
+results = model.export_results()
+print("\nModel Results Summary:")
+print("-" * 20)
+print(f"Risk Statistics:")
+print(f"Mean: ${results['Risk'].mean():,.2f}")
+print(f"Median: ${results['Risk'].median():,.2f}")
+print(f"95th Percentile: ${results['Risk'].quantile(0.95):,.2f}")