Matplotlib Integration

def show(close=None):
    """
    Display matplotlib figures inline.
    
    Shows all active matplotlib figures in the Jupyter frontend
    and optionally closes them after display.
    
    Parameters:
    - close (bool, optional): Whether to close figures after showing
                             If None, uses configuration default
    """

def draw_if_interactive():
    """
    Draw figures if matplotlib is in interactive mode.
    
    Triggers drawing of matplotlib figures when in interactive mode,
    ensuring plots are updated in Jupyter frontends.
    """

def flush_figures():
    """
    Flush all pending matplotlib figures.
    
    Forces display of all matplotlib figures that are ready to be shown,
    ensuring they appear in the Jupyter frontend.
    """

def configure_inline_support(shell, backend):
    """
    Configure matplotlib inline backend for IPython shell.
    
    Sets up matplotlib integration with the IPython shell, enabling
    inline display of plots and configuring display hooks.
    
    Parameters:
    - shell: IPython shell instance
    - backend: Matplotlib backend configuration
    """

class InlineBackendConfig:
    """
    Configuration class for matplotlib inline backend.
    
    Manages settings for how matplotlib figures are displayed
    inline in Jupyter environments.
    """
    
    # Configuration attributes
    figure_formats: set       # Set of figure formats to use
    print_figure_kwargs: dict # Keyword arguments for figure printing
    close_figures: bool       # Whether to close figures after display
    shell: object            # Reference to IPython shell

class InlineBackend:
    """
    Main inline backend class for matplotlib integration.
    
    Handles the display and formatting of matplotlib figures
    within Jupyter notebook environments.
    """
    
    def show(self, close=None):
        """
        Show matplotlib figures inline.
        
        Parameters:
        - close (bool, optional): Whether to close after showing
        """
    
    def draw_if_interactive(self):
        """Draw figures if in interactive mode."""
    
    def flush_figures(self):
        """Flush all pending figures."""
    
    # Backend configuration
    config: InlineBackendConfig  # Backend configuration object

from ipykernel.pylab.backend_inline import show, flush_figures
import matplotlib.pyplot as plt
import numpy as np

# Create a simple plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2, label='sin(x)')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Sine Wave Plot')
plt.legend()
plt.grid(True)

# Display the plot inline
show()

from ipykernel.pylab.backend_inline import show, flush_figures
import matplotlib.pyplot as plt
import numpy as np

# Create multiple figures
x = np.linspace(0, 10, 100)

# Figure 1: Sine wave
plt.figure(1, figsize=(8, 4))
plt.plot(x, np.sin(x), 'b-', label='sin(x)')
plt.title('Sine Wave')
plt.legend()

# Figure 2: Cosine wave  
plt.figure(2, figsize=(8, 4))
plt.plot(x, np.cos(x), 'r-', label='cos(x)')
plt.title('Cosine Wave')
plt.legend()

# Figure 3: Both waves
plt.figure(3, figsize=(10, 6))
plt.plot(x, np.sin(x), 'b-', label='sin(x)')
plt.plot(x, np.cos(x), 'r-', label='cos(x)')
plt.title('Sine and Cosine Waves')
plt.legend()

# Display all figures
flush_figures()

from ipykernel.pylab.backend_inline import show, draw_if_interactive
import matplotlib.pyplot as plt
import numpy as np
import time

# Enable interactive mode
plt.ion()

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))
x = np.linspace(0, 10, 100)

# Initial plot
line, = ax.plot(x, np.sin(x), 'b-', linewidth=2)
ax.set_xlabel('X values')
ax.set_ylabel('Y values')
ax.set_title('Animated Sine Wave')
ax.grid(True)

# Show initial plot
show()

# Animate the plot
for phase in np.linspace(0, 4*np.pi, 20):
    # Update data
    y = np.sin(x + phase)
    line.set_ydata(y)
    
    # Trigger redraw
    draw_if_interactive()
    
    # Small delay for animation effect
    time.sleep(0.1)

plt.ioff()  # Turn off interactive mode

from ipykernel.pylab.backend_inline import configure_inline_support, show
from ipykernel.pylab.config import InlineBackend, InlineBackendConfig
import matplotlib.pyplot as plt
import numpy as np

# Configure matplotlib for high-quality inline display
config = InlineBackendConfig()
config.figure_formats = {'png', 'svg'}  # Use both PNG and SVG
config.print_figure_kwargs = {
    'bbox_inches': 'tight',
    'dpi': 150,
    'facecolor': 'white'
}
config.close_figures = True  # Close figures after display

# Create custom backend with configuration
backend = InlineBackend()
backend.config = config

# Create high-quality plot
plt.figure(figsize=(12, 8))
x = np.linspace(0, 20, 1000)
y1 = np.sin(x) * np.exp(-x/10)
y2 = np.cos(x) * np.exp(-x/10)

plt.plot(x, y1, 'b-', linewidth=2, label='Damped Sine', alpha=0.8)
plt.plot(x, y2, 'r-', linewidth=2, label='Damped Cosine', alpha=0.8)
plt.fill_between(x, y1, alpha=0.3, color='blue')
plt.fill_between(x, y2, alpha=0.3, color='red')

plt.xlabel('Time', fontsize=14)
plt.ylabel('Amplitude', fontsize=14)
plt.title('Damped Oscillations', fontsize=16, fontweight='bold')
plt.legend(fontsize=12)
plt.grid(True, alpha=0.3)

# Display with custom configuration
backend.show()

from ipykernel.pylab.backend_inline import show
import matplotlib.pyplot as plt
import numpy as np

# Create subplot figure
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
fig.suptitle('Multiple Subplot Example', fontsize=16, fontweight='bold')

x = np.linspace(0, 10, 100)

# Subplot 1: Linear function
axes[0, 0].plot(x, x, 'b-', linewidth=2)
axes[0, 0].set_title('Linear: y = x')
axes[0, 0].grid(True)

# Subplot 2: Quadratic function
axes[0, 1].plot(x, x**2, 'r-', linewidth=2)
axes[0, 1].set_title('Quadratic: y = x²')
axes[0, 1].grid(True)

# Subplot 3: Exponential function
axes[1, 0].plot(x, np.exp(x/5), 'g-', linewidth=2)
axes[1, 0].set_title('Exponential: y = e^(x/5)')
axes[1, 0].grid(True)

# Subplot 4: Logarithmic function
x_log = np.linspace(0.1, 10, 100)
axes[1, 1].plot(x_log, np.log(x_log), 'm-', linewidth=2)
axes[1, 1].set_title('Logarithmic: y = ln(x)')
axes[1, 1].grid(True)

# Adjust layout and display
plt.tight_layout()
show()

from ipykernel.pylab.backend_inline import show
from IPython.display import display, Image, SVG
import matplotlib.pyplot as plt
import numpy as np
import io
import base64

def create_plot_data():
    """Create sample plot data."""
    x = np.linspace(0, 2*np.pi, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    return x, y1, y2

def plot_to_base64(fig, format='png', dpi=100):
    """Convert matplotlib figure to base64 string."""
    buffer = io.BytesIO()
    fig.savefig(buffer, format=format, dpi=dpi, bbox_inches='tight')
    buffer.seek(0)
    image_data = buffer.getvalue()
    buffer.close()
    
    return base64.b64encode(image_data).decode()

# Create plot
x, y1, y2 = create_plot_data()

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y1, 'b-', linewidth=2, label='sin(x)')
ax.plot(x, y2, 'r--', linewidth=2, label='cos(x)')
ax.set_xlabel('Angle (radians)')
ax.set_ylabel('Value')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)

# Display using different methods
print("Method 1: Standard inline display")
show()

print("Method 2: Manual base64 encoding")
b64_data = plot_to_base64(fig)
display(Image(data=base64.b64decode(b64_data)))

plt.close(fig)  # Clean up

from ipykernel.pylab.backend_inline import show, flush_figures
import matplotlib.pyplot as plt
import numpy as np

def safe_plot_display():
    """Safely create and display plots with error handling."""
    try:
        # Create plot
        x = np.linspace(0, 10, 100)
        y = np.sin(x) / x  # This might cause division by zero
        
        plt.figure(figsize=(8, 6))
        plt.plot(x, y, 'b-', linewidth=2)
        plt.title('sinc(x) = sin(x)/x')
        plt.xlabel('x')
        plt.ylabel('sinc(x)')
        plt.grid(True)
        
        # Display plot
        show(close=True)  # Close after showing
        
        print("Plot displayed successfully")
        
    except Exception as e:
        print(f"Error creating plot: {e}")
        
        # Clean up any partial figures
        plt.close('all')
    
    finally:
        # Ensure all figures are flushed
        flush_figures()

# Run safe plotting
safe_plot_display()

# Verify no figures remain open
print(f"Open figures: {plt.get_fignums()}")