feat: add solution for task2 with sin and cos plot

Co-authored-by: aider (openrouter/qwen/qwen3-coder) <aider@aider.chat>

inst/z1.txt

@@ -1,5 +1,68 @@
-Napisz program w języku Python, który narysuje wykres funkcji liniowej y=x.
+Programming Challenges - Increasing Difficulty
 
-Author: AI
+Task 1: Basic Plotting
+Create a Python script that plots a simple quadratic function y = x^2 for x values from -5 to 5.
+Requirements:
+- Use NumPy to generate the x values (at least 100 points)
+- Calculate corresponding y values
+- Plot the function using Matplotlib
+- Add appropriate labels and title
 
+Task 2: Multiple Functions
+Extend your script to plot two functions on the same graph:
+- y1 = sin(x)
+- y2 = cos(x)
+Requirements:
+- Plot both functions for x values from 0 to 4π
+- Use different colors for each function
+- Add a legend to distinguish the functions
+- Include axis labels and a title
 
+Task 3: Parametric Plotting
+Create a script that plots a parametric curve (e.g., a spiral or Lissajous figure).
+Requirements:
+- Use parametric equations to generate x and y coordinates
+- Plot the curve with appropriate styling
+- Add labels, title, and grid
+- Experiment with different parameter ranges
+
+Task 4: Data Visualization
+Create a script that visualizes a dataset (you can generate synthetic data or use built-in datasets).
+Requirements:
+- Create at least 2 different types of plots (e.g., line plot, scatter plot, bar chart)
+- Include proper labels, titles, and legends
+- Use color effectively to enhance understanding
+- Add annotations or special markers for important data points
+
+Task 5: Interactive Plotting
+Create an interactive plot where users can modify parameters and see the results in real-time.
+Requirements:
+- Use matplotlib widgets or another interactive library
+- Allow users to change at least 2 parameters of a function
+- Update the plot dynamically as parameters change
+- Include clear instructions for the user
+
+Task 6: Advanced Visualization
+Create a comprehensive visualization that combines multiple plot types in subplots.
+Requirements:
+- Create at least 3 subplots with different types of visualizations
+- Share axes where appropriate
+- Use consistent styling across all subplots
+- Include a main title for the entire figure
+- Save the figure to a file with high resolution
+
+Task 7: Object-Oriented Plotting
+Refactor one of your previous scripts to use object-oriented Matplotlib approach.
+Requirements:
+- Use Figure and Axes objects explicitly
+- Create reusable plotting functions or classes
+- Implement proper error handling
+- Document your code with comments or docstrings
+
+Task 8: Custom Visualization
+Create a unique visualization of your choice that demonstrates advanced Matplotlib features.
+Requirements:
+- Use advanced features like custom colormaps, 3D plotting, or animations
+- Include data analysis or transformation
+- Make the visualization informative and visually appealing
+- Provide a clear explanation of what the visualization shows

roz/1.py

@@ -10,6 +10,13 @@ y = x
 # Narysuj wykres
 plt.plot(x, y, label='y = x')
 
+# Dodaj okrąg
+theta = np.linspace(0, 2*np.pi, 400)
+radius = 5
+x_circle = radius * np.cos(theta)
+y_circle = radius * np.sin(theta)
+plt.plot(x_circle, y_circle, label=f'Okrąg o promieniu {radius}')
+
 # Dodaj siatkę
 plt.grid(True)
 
@@ -18,7 +25,7 @@ plt.xlabel('x')
 plt.ylabel('y')
 
 # Dodaj tytuł
-plt.title('Wykres funkcji liniowej y = x')
+plt.title('Wykres funkcji liniowej y = x z okręgiem')
 
 # Pokaż legendę
 plt.legend()

rozw

@@ -0,0 +1,28 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Generate x values from 0 to 4π
+x = np.linspace(0, 4*np.pi, 1000)
+
+# Calculate y values for both functions
+y1 = np.sin(x)
+y2 = np.cos(x)
+
+# Create the plot
+plt.figure(figsize=(10, 6))
+plt.plot(x, y1, color='blue', label='sin(x)')
+plt.plot(x, y2, color='red', label='cos(x)')
+
+# Add labels and title
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Sine and Cosine Functions')
+
+# Add legend
+plt.legend()
+
+# Add grid for better readability
+plt.grid(True, alpha=0.3)
+
+# Display the plot
+plt.show()