# entropy_py.py

## `ofrak.core.entropy.entropy_py`

### `entropy_py(data, window_size, log_percent=None)`

Return a list of entropy values where each value represents the Shannon entropy of the byte value distribution over a fixed-size, sliding window.

Source code in `ofrak/core/entropy/entropy_py.py`
``````def entropy_py(
data: bytes, window_size: int, log_percent: Optional[Callable[[int], None]] = None
) -> bytes:
"""
Return a list of entropy values where each value represents the Shannon entropy of the byte
value distribution over a fixed-size, sliding window.
"""
if log_percent is None:
log_percent = lambda x: None
else:
# Sort of hacky way to know we are being called from the tests and don't need to log this
logging.warning(
f"Using the Python implementation of the Shannon entropy calculation! This is potentially "
f"very slow, and is only used when the C extension cannot be built/found."
)

# Create a histogram, and populate it with initial values
histogram =  * 256
for b in data[:window_size]:
histogram[b] += 1

# Calculate the entropy using a sliding window
entropy =  * (len(data) - window_size)
last_percent_logged = 0
for i in range(len(entropy)):
entropy[i] = math.floor(255 * _shannon_entropy(histogram, window_size))
histogram[data[i]] -= 1
histogram[data[i + window_size]] += 1
percent = int((i * 100) / len(data))
if percent > last_percent_logged and percent % 10 == 0:
log_percent(percent)
last_percent_logged = percent
return bytes(entropy)
``````

### `_shannon_entropy(distribution, window_size)` `private`

Return the Shannon entropy of the input probability distribution (represented as a histogram counting byte occurrences over a window of known size).

Shannon entropy represents how uniform a probability distribution is. Since more uniform implies less predictable (because the probability of any outcome is equally likely in a uniform distribution), a sample with higher entropy is "more random" than one with lower entropy. More here: https://en.wikipedia.org/wiki/Entropy_(information_theory).

Source code in `ofrak/core/entropy/entropy_py.py`
``````def _shannon_entropy(distribution: List[int], window_size: int) -> float:
"""
Return the Shannon entropy of the input probability distribution (represented as a histogram
counting byte occurrences over a window of known size).

Shannon entropy represents how uniform a probability distribution is. Since more uniform
implies less predictable (because the probability of any outcome is equally likely in a
uniform distribution), a sample with higher entropy is "more random" than one with lower
entropy. More here: <https://en.wikipedia.org/wiki/Entropy_(information_theory)>.
"""

result = 0.0
for num_occurrences in distribution:
probability = num_occurrences / window_size
# Note that the zero check is required because the domain of log2 is the positive reals
result += probability * math.log2(probability) if probability != 0.0 else 0.0
return -result / math.log2(window_size)
`````` 