### Using the get() Method with Fuzzy Matching

Source: https://context7.com/pysnippet/fuzzymap/llms.txt

Demonstrates the `get()` method's behavior with FuzzyMap, including fuzzy matching and returning a default value when no match is found.

```python
from fuzzymap import FuzzyMap

data = FuzzyMap({
    'Barcelona FC': {'stadium': 'Camp Nou'},
    'Bayern Munich': {'stadium': 'Allianz Arena'},
})

# Fuzzy get with match
result = data.get('FC Barcelona', default={'stadium': 'Unknown'})
# Returns: {'stadium': 'Camp Nou'}

# Get with no match returns default
result = data.get('Unknown Team', default={'stadium': 'Unknown'})
# Returns: {'stadium': 'Unknown'}

# Get without default returns None
result = data.get('Unknown Team')
# Returns: None
```

--------------------------------

### Initialize and Access FuzzyMap

Source: https://context7.com/pysnippet/fuzzymap/llms.txt

Demonstrates initializing FuzzyMap with a dictionary and accessing values using both exact and similar keys. Non-matching keys return None.

```python
from fuzzymap import FuzzyMap

# Initialize with a dictionary
data = FuzzyMap({
    'SK Rapid Wien - First Vienna FC': {'w1': 1.97, 'x': 2.3, 'w2': 8.2},
    'Bourj FC - Nejmeh SC Beirut': {'w1': 32, 'x': 12, 'w2': 1.05},
})

# Access with exact key
result = data['SK Rapid Wien - First Vienna FC']
# Returns: {'w1': 1.97, 'x': 2.3, 'w2': 8.2}

# Access with similar key (fuzzy matching)
result = data['Rapid Wien - First Vienna']
# Returns: {'w1': 1.97, 'x': 2.3, 'w2': 8.2}

# Another fuzzy match example
result = data['Al Bourj - Al Nejmeh']
# Returns: {'w1': 32, 'x': 12, 'w2': 1.05}

# Non-matching key returns None
result = data['Some Completely Different Key']
# Returns: None
```

--------------------------------

### Match keys across data sources using FuzzyMap

Source: https://github.com/pysnippet/fuzzymap/blob/master/README.md

Demonstrates using FuzzyMap to resolve inconsistent game names between two data sources by mapping keys based on similarity.

```python
from fuzzymap import FuzzyMap

source_1 = {
    'Rapid Wien - First Vienna': {'w1': 1.93, 'x': 2.32, 'w2': 7.44},
    'Al Bourj - Al Nejmeh': {'w1': 26, 'x': 11.5, 'w2': 1.05},
    # hundreds of other games' data
}

source_2 = FuzzyMap({
    'Bourj FC - Nejmeh SC Beirut': {'w1': 32, 'x': 12, 'w2': 1.05},
    'SK Rapid Wien - First Vienna FC': {'w1': 1.97, 'x': 2.3, 'w2': 8.2},
    # hundreds of other games' data
})

for game, odds1 in source_1.items():
    odds2 = source_2[game]

    # odds1 = {"w1": 1.93, "x": 2.32, "w2": 7.44}
    # odds2 = {"w1": 1.97, "x": 2.3, "w2": 8.2}
    handle_fork(odds1, odds2)
```

--------------------------------

### Configure FuzzyMap Similarity Ratio

Source: https://context7.com/pysnippet/fuzzymap/llms.txt

Shows how to adjust the `ratio` attribute to control the minimum similarity percentage for fuzzy matches. Lower values are more lenient, higher values are stricter.

```python
from fuzzymap import FuzzyMap

data = FuzzyMap({
    'Manchester United FC': {'league': 'Premier League'},
    'Real Madrid CF': {'league': 'La Liga'},
})

# Default ratio (60) - strict matching
result = data['Man Utd']
# Returns: None (similarity below threshold)

# Lower the ratio for more lenient matching
data.ratio = 30
result = data['Man Utd']
# Returns: {'league': 'Premier League'}

# Increase ratio for stricter matching
data.ratio = 90
result = data['Manchester United']
# Returns: None (requires higher similarity)

data.ratio = 60
result = data['Manchester United']
# Returns: {'league': 'Premier League'}
```

--------------------------------

### Fuzzy Key Setting in FuzzyMap

Source: https://context7.com/pysnippet/fuzzymap/llms.txt

Illustrates how FuzzyMap uses fuzzy matching when setting values, updating existing similar keys rather than creating new entries.

```python
from fuzzymap import FuzzyMap

data = FuzzyMap({
    'SK Rapid Wien - First Vienna FC': {'w1': 1.97, 'x': 2.3, 'w2': 8.2},
})

# Setting with a similar key updates the existing entry
data['Rapid Wien - First Vienna'] = {'w1': 2.0, 'x': 2.5, 'w2': 8.0}

# The original key's value is updated
print(data['SK Rapid Wien - First Vienna FC'])
# Returns: {'w1': 2.0, 'x': 2.5, 'w2': 8.0}

# Only one key exists in the dictionary
print(len(data))
# Returns: 1
```

--------------------------------

### Cross-Source Data Matching with FuzzyMap

Source: https://context7.com/pysnippet/fuzzymap/llms.txt

Illustrates a primary use case of FuzzyMap for matching data across different sources that have varying naming conventions for the same entities.

```python
from fuzzymap import FuzzyMap

# Data from source 1 (bookmaker A)
source_1 = {
    'Rapid Wien - First Vienna': {'w1': 1.93, 'x': 2.32, 'w2': 7.44},
    'Al Bourj - Al Nejmeh': {'w1': 26, 'x': 11.5, 'w2': 1.05},
}

# Data from source 2 (bookmaker B) - different naming convention
source_2 = FuzzyMap({
    'Bourj FC - Nejmeh SC Beirut': {'w1': 32, 'x': 12, 'w2': 1.05},
    'SK Rapid Wien - First Vienna FC': {'w1': 1.97, 'x': 2.3, 'w2': 8.2},
})

# Match games across sources
for game, odds_1 in source_1.items():
    odds_2 = source_2[game]

    if odds_2:
        print(f"Game: {game}")
        print(f"  Source 1 odds: {odds_1}")
        print(f"  Source 2 odds: {odds_2}")

        # Calculate potential arbitrage
        implied_prob = (1/odds_1['w1']) + (1/odds_2['w2'])
        if implied_prob < 1:
            print(f"  Potential arbitrage opportunity!")

# Output:
# Game: Rapid Wien - First Vienna
```

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