Jupyter Integration

import tif1

# Rich display is automatically enabled
session = tif1.get_session(2021, "Belgian Grand Prix", "Race")
session  # Displays rich HTML representation

import tif1

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")
session

🏎️ F1 Session
┌─────────────┬──────────────────────────┐
│ Year        │ 2021                     │
│ Grand Prix  │ Belgian Grand Prix       │
│ Session     │ Race                     │
│ Library     │ pandas                   │
│ Drivers     │ 20                       │
└─────────────┴──────────────────────────┘

ver = session.get_driver("VER")
ver
```yaml

**Output:**
```text
👤 Driver: VER
┌─────────────┬──────────────────────────────────────┐
│ Session     │ 2021 Belgian Grand Prix - Race       │
│ Laps loaded │ Yes                                  │
└─────────────┴──────────────────────────────────────┘

lap = ver.get_lap(19)
lap
```text **Output:**
```text
🏁 Lap 19
┌──────────────────┬──────────────────────────────────────┐
│ Driver           │ VER                                  │
│ Session          │ 2021 Belgian Grand Prix - Race       │
│ Telemetry loaded │ No                                   │
└──────────────────┴──────────────────────────────────────┘

from tif1.jupyter import enable_jupyter_display

enable_jupyter_display()
```python

### Check if running in jupyter

```python
from tif1.jupyter import _is_notebook

if _is_notebook():
    print("Running in Jupyter")
else:
    print("Not in Jupyter")
```python

---

## DataFrame Summaries

DataFrames automatically display with summary information:

```python
laps = session.laps
laps
```python

**Output includes:**
- Row count
- Column count
- Memory usage
- Standard DataFrame display

---

## Custom display functions

### `display_session_info()`

Generate HTML display for a Session object.

```python
from tif1.jupyter import display_session_info

html = display_session_info(session)
# Returns HTML string
```python

### `display_driver_info()`

Generate HTML display for a Driver object.

```python
from tif1.jupyter import display_driver_info

html = display_driver_info(driver)
# Returns HTML string
```python

### `display_lap_info()`

Generate HTML display for a Lap object.

```python
from tif1.jupyter import display_lap_info

html = display_lap_info(lap)
# Returns HTML string
```python ### `display_dataframe_summary()`

Generate summary display for a DataFrame.

```python
from tif1.jupyter import display_dataframe_summary

html = display_dataframe_summary(laps)
# Returns HTML string with row/column/memory info
```python

---

## Notebook best practices

### 1. Load data efficiently

```python
import tif1

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Load session data
session.load()
laps = session.laps

from IPython.display import display, HTML
import tif1

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

display(HTML("<p>Loading session data...</p>"))
laps = session.laps
display(HTML(f"<p>✓ Loaded {len(laps)} laps</p>"))

import tif1

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Display session info
session

# Get driver codes from drivers_df
drivers_df = session.drivers_df
for driver_code in drivers_df["Driver"][:3]:
    driver = session.get_driver(driver_code)
    display(driver)

import tif1
import matplotlib.pyplot as plt
from tif1.plotting import setup_mpl, get_driver_color_mapping

# Setup F1 theme
setup_mpl()

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")
laps = session.laps
colors = get_driver_color_mapping(session)

# Plot lap times for top drivers
fig, ax = plt.subplots(figsize=(12, 6))

for driver in ["VER", "HAM", "BOT"]:
    driver_laps = laps[laps["Driver"] == driver]
    ax.plot(
        driver_laps["LapNumber"],
        driver_laps["LapTimeSeconds"],
        color=colors.get(driver),
        label=driver,
        linewidth=2
    )

ax.set_xlabel("Lap Number")
ax.set_ylabel("Lap Time (s)")
ax.set_title("Race Lap Times - 2021 Belgian Grand Prix")
ax.legend()
ax.grid(True, alpha=0.3)

plt.show()

import tif1
import ipywidgets as widgets
from IPython.display import display

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Get driver codes from drivers_df
drivers_df = session.drivers_df
driver_codes = drivers_df["Driver"].tolist()

# Create driver selector
driver_dropdown = widgets.Dropdown(
    options=driver_codes,
    description='Driver:',
)

# Create output widget
output = widgets.Output()

def on_driver_change(change):
    with output:
        output.clear_output()
        driver = session.get_driver(change['new'])
        fastest = driver.get_fastest_lap()
        if not fastest.empty:
            print(f"Fastest lap: {fastest['LapTimeSeconds'].iloc[0]:.3f}s")
            print(f"Compound: {fastest['Compound'].iloc[0]}")

driver_dropdown.observe(on_driver_change, names='value')

display(driver_dropdown, output)

# Manually enable
from tif1.jupyter import enable_jupyter_display
enable_jupyter_display()

# Check if in notebook
from tif1.jupyter import _is_notebook
print(_is_notebook())  # Should be True
```python

### HTML Display Issues

If HTML displays are broken:

```python
# Fall back to repr
session = tif1.get_session(2021, "Belgian Grand Prix", "Race")
print(repr(session))
``` ### Memory Issues

For large datasets in notebooks:

```python

import tif1

config = tif1.get_config()
config.set("lib", "polars")

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Cell 1: Setup
import tif1
import matplotlib.pyplot as plt
from tif1.plotting import setup_mpl, get_driver_color_mapping

setup_mpl()

# Cell 2: Load session
session = tif1.get_session(2021, "Belgian Grand Prix", "Race")
session  # Rich display

# Cell 3: Explore drivers
drivers_df = session.drivers_df
print(f"Drivers: {len(drivers_df)}")
for driver_code in drivers_df["Driver"][:5]:
    driver = session.get_driver(driver_code)
    display(driver)

# Cell 4: Get fastest laps
fastest = session.get_fastest_laps(by_driver=True)
fastest[["Driver", "Team", "LapTimeSeconds", "Compound"]].head(10)

# Cell 5: Analyze specific driver
ver = session.get_driver("VER")
ver_laps = ver.laps

print(f"Total laps: {len(ver_laps)}")
print(f"Fastest: {ver_laps['LapTimeSeconds'].min():.3f}s")
print(f"Average: {ver_laps['LapTimeSeconds'].mean():.3f}s")

# Cell 6: Get telemetry
tel = ver.get_fastest_lap_tel()

if not tel.empty:
    print(f"Telemetry samples: {len(tel)}")
    print(f"Max speed: {tel['Speed'].max():.1f} km/h")
    if 'RPM' in tel.columns:
        print(f"Max RPM: {tel['RPM'].max()}")

# Cell 7: Plot telemetry
if not tel.empty:
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8))

    # Speed trace
    ax1.plot(tel['Distance'], tel['Speed'], linewidth=2)
    ax1.set_ylabel('Speed (km/h)')
    ax1.set_title('VER Fastest Lap - 2021 Belgian Grand Prix')
    ax1.grid(True, alpha=0.3)

    # Throttle and brake
    ax2.plot(tel['Distance'], tel['Throttle'], label='Throttle', linewidth=2)
    if 'Brake' in tel.columns:
        ax2.fill_between(tel['Distance'], 0, 100, where=tel['Brake'] > 0,
                          alpha=0.3, color='red', label='Brake')
    ax2.set_xlabel('Distance (m)')
    ax2.set_ylabel('Throttle (%)')
    ax2.legend()
    ax2.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.show()

# Cell 8: Compare drivers
drivers_to_compare = ["VER", "HAM", "BOT"]
colors = get_driver_color_mapping(session)

fig, ax = plt.subplots(figsize=(12, 6))

for driver_code in drivers_to_compare:
    driver_laps = session.laps[session.laps["Driver"] == driver_code]
    ax.plot(
        driver_laps["LapNumber"],
        driver_laps["LapTimeSeconds"],
        color=colors.get(driver_code),
        label=driver_code,
        linewidth=2,
        marker='o',
        markersize=4
    )

ax.set_xlabel('Lap Number')
ax.set_ylabel('Lap Time (s)')
ax.set_title('Lap Time Comparison - 2021 Belgian Grand Prix')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()