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claude security upgrade

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modified:   requirements.txt
	modified:   research/activity24-math-plot.yaml
This commit is contained in:
Russell Ballestrini 2025-06-23 22:36:23 -04:00
parent 648df5a0b2
commit 2a1efd1f90
2 changed files with 119 additions and 37 deletions
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1
requirements.txt
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@ -24,3 +24,4 @@ pyyaml
# if you want to plot charts.
matplotlib
numpy
sympy

155
research/activity24-math-plot.yaml
View file

@ -41,47 +41,128 @@ sections:
import io
import base64
import re
import sympy as sp
# Get the user's function input from metadata
user_function = metadata.get("user_function", "x")
original_function = user_function
# Preprocess the function to ensure valid syntax
# Replace '^' with '**' for exponentiation
user_function = user_function.replace('^', '**')
# Add asterisks for implied multiplication (e.g., '4x' -> '4*x')
user_function = re.sub(r'(?<=\d)(?=[a-zA-Z])', '*', user_function)
user_function = re.sub(r'(?<=[a-zA-Z])(?=\d)', '*', user_function)
# Replace common math functions with their math module equivalents
math_functions = [
'sin', 'cos', 'tan', 'exp', 'log', 'sqrt', 'abs', 'pi', 'e', 'inf',
'sinh', 'cosh', 'tanh', 'arctan',
]
for func in math_functions:
user_function = re.sub(r'\b' + func + r'\b', f'numpy.{func}', user_function)
# Prepare the x values
x = numpy.linspace(-10, 10, 400)
# Evaluate the function using eval with math module
y = eval(user_function, {"numpy": numpy, "x": x})
# Plot the function
matplotlib.pyplot.figure()
matplotlib.pyplot.plot(x, y, label=f'y = {user_function}')
matplotlib.pyplot.title(f'Plot of y = {user_function}')
matplotlib.pyplot.xlabel('x')
matplotlib.pyplot.ylabel('y')
matplotlib.pyplot.grid(True)
matplotlib.pyplot.legend()
buf = io.BytesIO()
matplotlib.pyplot.savefig(buf, format='png')
matplotlib.pyplot.close()
buf.seek(0)
plot_image = base64.b64encode(buf.getvalue()).decode('utf-8')
script_result = {"plot_image": plot_image}
try:
# Support multiple functions separated by semicolon or comma
function_list = re.split(r'[;,]', user_function)
function_list = [f.strip() for f in function_list if f.strip()]
# Colors for multiple functions
colors = ['blue', 'red', 'green', 'orange', 'purple', 'brown', 'pink', 'gray']
matplotlib.pyplot.figure(figsize=(10, 6))
all_y_values = []
function_info = []
for i, func_str in enumerate(function_list):
# Preprocess each function
processed_func = func_str.replace('^', '**')
processed_func = re.sub(r'(?<=\d)(?=[a-zA-Z])', '*', processed_func)
processed_func = re.sub(r'(?<=[a-zA-Z])(?=\d)', '*', processed_func)
# Enhanced function preprocessing
enhanced_replacements = {
'arctan': 'atan',
'arcsin': 'asin',
'arccos': 'acos',
'log': 'ln',
'ln': 'log', # Allow both ln and log
'abs': 'Abs'
}
parsed_function = processed_func
for old, new in enhanced_replacements.items():
parsed_function = re.sub(r'\b' + old + r'\b', new, parsed_function)
# Create sympy symbol and parse expression
x_sym = sp.Symbol('x')
expr = sp.sympify(parsed_function, locals={'x': x_sym})
# Analyze function characteristics for dynamic range
func_type = analyze_function_type(expr, x_sym)
x_range = determine_optimal_range(expr, x_sym, func_type)
# Prepare x values with dynamic range
x_vals = numpy.linspace(x_range[0], x_range[1], 400)
# Convert to numpy function and evaluate
func = sp.lambdify(x_sym, expr, 'numpy')
y = func(x_vals)
# Handle complex results
if numpy.iscomplexobj(y):
y = numpy.real(y)
# Filter out infinite/NaN values for better plotting
valid_mask = numpy.isfinite(y)
x_vals_clean = x_vals[valid_mask]
y_clean = y[valid_mask]
if len(y_clean) > 0:
all_y_values.extend(y_clean)
color = colors[i % len(colors)]
matplotlib.pyplot.plot(x_vals_clean, y_clean,
label=f'y = {func_str}',
color=color, linewidth=2)
# Store function analysis info
function_info.append({
'function': func_str,
'type': func_type,
'range': x_range
})
# Dynamic y-axis limits based on all functions
if all_y_values:
y_min, y_max = numpy.percentile(all_y_values, [5, 95])
y_range = y_max - y_min
matplotlib.pyplot.ylim(y_min - 0.1*y_range, y_max + 0.1*y_range)
# Enhanced plot styling
matplotlib.pyplot.title(f'Plot of: {original_function}', fontsize=14, fontweight='bold')
matplotlib.pyplot.xlabel('x', fontsize=12)
matplotlib.pyplot.ylabel('y', fontsize=12)
matplotlib.pyplot.grid(True, alpha=0.3)
matplotlib.pyplot.legend(fontsize=10)
# Add function analysis as text
analysis_text = generate_function_analysis(function_info)
buf = io.BytesIO()
matplotlib.pyplot.tight_layout()
matplotlib.pyplot.savefig(buf, format='png', dpi=100, bbox_inches='tight')
matplotlib.pyplot.close()
buf.seek(0)
plot_image = base64.b64encode(buf.getvalue()).decode('utf-8')
script_result = {
"plot_image": plot_image,
"function_analysis": analysis_text,
"function_info": function_info
}
except Exception as e:
# Handle errors gracefully with error message plot
matplotlib.pyplot.figure()
matplotlib.pyplot.text(0.5, 0.5, f'Error: Invalid function\n"{original_function}"\n\n{str(e)[:100]}...',
horizontalalignment='center', verticalalignment='center',
transform=matplotlib.pyplot.gca().transAxes, fontsize=12,
bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral"))
matplotlib.pyplot.title('Function Error')
matplotlib.pyplot.axis('off')
buf = io.BytesIO()
matplotlib.pyplot.savefig(buf, format='png')
matplotlib.pyplot.close()
buf.seek(0)
plot_image = base64.b64encode(buf.getvalue()).decode('utf-8')
script_result = {"plot_image": plot_image, "error": str(e)}
buckets:
- correct