Jupyter Notebook Integration

class HaloNotebook(Halo):
    def __init__(
        self,
        text="",
        color="cyan",
        text_color=None,
        spinner=None,
        placement="left",
        animation=None,
        interval=-1,
        enabled=True,
        stream=sys.stdout
    ):
        """
        Initialize a Jupyter notebook-compatible spinner.
        
        Parameters: Same as Halo.__init__
        - text (str): Text to display alongside spinner (default: "")
        - color (str): Spinner color (default: "cyan") 
        - text_color (str): Text color (default: None)
        - spinner (str|dict): Spinner type or custom definition (default: "dots")
        - placement (str): Spinner position - "left" or "right" (default: "left")
        - animation (str): Text animation - "bounce", "marquee" (default: None)
        - interval (int): Frame interval in milliseconds (default: -1)
        - enabled (bool): Whether spinner is active (default: True)
        - stream (io.TextIOWrapper): Output stream (default: sys.stdout)
        """

@property
def output(self):
    """
    Get the IPython Output widget instance.
    
    Returns:
    - ipywidgets.Output: The widget used for spinner display
    """

# In Jupyter notebook cell
from halo import HaloNotebook
import time

# Basic usage - identical to Halo
spinner = HaloNotebook(text='Processing data', spinner='dots', color='blue')
spinner.start()
time.sleep(3)
spinner.succeed('✓ Processing complete')

from halo import Halo
import time

# Automatically uses HaloNotebook in Jupyter, regular Halo in terminal
with Halo(text='Loading', spinner='dots'):
    time.sleep(2)

pip install halo[ipython]

# In Jupyter notebook cell
from halo import HaloNotebook
import time

# Context manager usage
with HaloNotebook(text='Training model', spinner='dots', color='green'):
    # Simulate long-running operation
    for epoch in range(5):
        time.sleep(1)
        print(f"Epoch {epoch + 1} completed")  # This won't interfere with spinner

print("Training completed!")

# In Jupyter notebook cell  
from halo import HaloNotebook
import time

spinner = HaloNotebook(text='Starting analysis', spinner='star')
spinner.start()

# Update text during execution
for i in range(1, 6):
    spinner.text = f'Processing batch {i}/5'
    time.sleep(1)

spinner.succeed('✓ Analysis completed successfully')

# In Jupyter notebook cell
from halo import HaloNotebook
import time

@HaloNotebook(text='Running computation', spinner='dots', color='cyan')
def heavy_computation():
    time.sleep(3)
    return "computation_result"

result = heavy_computation()
print(f"Result: {result}")

# In Jupyter notebook cell
from halo import HaloNotebook
import time

def analyze_data():
    spinner = HaloNotebook(text='Analyzing dataset', spinner='arrow')
    spinner.start()
    
    time.sleep(2)
    
    # Different completion methods work the same
    try:
        # Simulate analysis
        success = True
        if success:
            spinner.succeed('✓ Analysis completed - 1000 records processed')
        else:
            spinner.fail('✗ Analysis failed')
    except Exception as e:
        spinner.fail(f'✗ Error during analysis: {e}')

analyze_data()

# In Jupyter notebook cell
from halo import HaloNotebook
import time

def data_pipeline():
    # Step 1: Data loading
    loader = HaloNotebook(text='Loading data', spinner='dots', color='blue')
    loader.start()
    time.sleep(1)
    loader.succeed('✓ Data loaded - 10,000 records')
    
    # Step 2: Data processing  
    processor = HaloNotebook(text='Processing data', spinner='star', color='green')
    processor.start()
    time.sleep(2)
    processor.succeed('✓ Data processed - features extracted')
    
    # Step 3: Model training
    trainer = HaloNotebook(text='Training model', spinner='arrow', color='red')
    trainer.start()
    time.sleep(3)
    trainer.succeed('✓ Model trained - accuracy: 95.2%')

data_pipeline()

# In Jupyter notebook cell
from halo import HaloNotebook
import time

def process_files():
    files = ['data1.csv', 'data2.csv', 'data3.csv', 'data4.csv', 'data5.csv']
    
    spinner = HaloNotebook(text='Starting file processing', spinner='dots')
    spinner.start()
    
    for i, file in enumerate(files, 1):
        spinner.text = f'Processing {file} ({i}/{len(files)})'
        time.sleep(1)  # Simulate processing time
    
    spinner.succeed(f'✓ Processed {len(files)} files successfully')

process_files()

# In Jupyter notebook cell
from halo import HaloNotebook
import pandas as pd
import numpy as np
import time

def data_analysis():
    spinner = HaloNotebook(text='Loading dataset', spinner='dots', color='blue')
    spinner.start()
    
    # Simulate data loading
    time.sleep(1)
    data = pd.DataFrame(np.random.randn(10000, 5))
    
    spinner.text = 'Computing statistics'
    time.sleep(1)
    stats = data.describe()
    
    spinner.text = 'Performing correlation analysis'
    time.sleep(1)
    corr = data.corr()
    
    spinner.succeed('✓ Analysis complete - results ready')
    
    return stats, corr

stats, correlations = data_analysis()

from halo._utils import get_environment

env = get_environment()
print(f"Current environment: {env}")
# Possible values: "terminal", "ipython", "jupyter"

# Force use of HaloNotebook even in terminal (not recommended)
from halo import HaloNotebook

# Force use of regular Halo in notebook (will not display properly)  
from halo.halo import Halo