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464 | def std_numeric(
value: Any,
*,
on_error: Literal["raise", "nan", "none"] = "raise",
allow_bool: bool = False,
) -> int | float | None:
"""
Convert numeric types to standardPython int or float.
Normalizes numeric values from Python stdlib and third-party libraries
(NumPy, Pandas, Decimal, etc.) into standardPython types for display,
serialization, and processing.
Design Philosophy
-----------------
This function provides **transparent type normalization** without heuristics:
- Types with __index__() → int (exact integer types)
- Types with __float__() → float (all other numeric types)
- No "smart" conversions based on numeric values
The source type's __float__() method handles overflow/underflow:
- Overflow: values too large become inf/-inf
- Underflow: values too small become 0.0/-0.0
- Special values (nan, inf) pass through unchanged
Parameters
----------
value : various
Numeric value to convert. Supports Python int/float/None, Decimal,
Fraction, and third-party types via __index__, __float__, .item(),
or .value protocols.
on_error : {"raise", "nan", "none"}, default "raise"
How to handle TYPE ERRORS (unsupported types like str, list, dict):
- "raise": Raise TypeError (default)
- "nan": Return float('nan')
- "none": Return None
Note: Numeric edge cases (inf, nan) are valid values, not errors.
allow_bool : bool, default False
If True, convert bool to int (True→1, False→0). If False, treat
bool as type error. Default False helps catch bugs since bool is
a subclass of int in Python.
Returns
-------
int
For Python int (arbitrary precision) or types implementing __index__
(NumPy integers, etc.).
float
For all other numeric types via __float__(), including special IEEE 754
values (inf, -inf, nan, 0.0, -0.0).
None
For None input, pandas.NA, numpy.ma.masked, or type errors when
on_error="none".
Raises
------
TypeError
When on_error="raise" and value is unsupported type or bool when
allow_bool=False.
Type Conversion Rules
---------------------
**Integer types** (via __index__):
Python int, NumPy int8-64/uint8-64 → int (arbitrary precision)
**Float types** (via __float__):
Python float, Decimal, Fraction, NumPy float16-128 → float
**Special values** (pass through as float):
inf, -inf, nan → preserved unchanged
**Overflow/underflow** (handled by source type's __float__):
Decimal('1e400').__float__() → inf
Decimal('1e-400').__float__() → 0.0
**Array scalars** (via .item()):
NumPy/PyTorch/TensorFlow/JAX tensor scalars extracted then converted
**Physical quantities** (via .value):
Astropy Quantity → extract .value, discard units, convert
**Missing data**:
None → None
pandas.NA → nan
numpy.ma.masked → nan
Common Types Supported
----------------------
- Python: int, float, None, Decimal, Fraction
- NumPy: int/uint/float scalars, nan, inf, arrays via .item()
- Pandas: numeric scalars, pd.NA
- ML: PyTorch/TensorFlow/JAX scalars via .item()/.numpy()
- Scientific: Astropy Quantity (via .value)
- Any type with __float__() or __index__()
Examples
--------
Basic types:
>>> std_numeric(42)
42
>>> std_numeric(3.14)
3.14
>>> std_numeric(None) # Returns None
Decimal and Fraction (always become float):
>>> from decimal import Decimal
>>> std_numeric(Decimal('42'))
42.0
>>> std_numeric(Decimal('3.14'))
3.14
Overflow and underflow (handled by __float__):
>>> std_numeric(Decimal('1e400'))
inf
>>> std_numeric(Decimal('1e-400'))
0.0
Special values:
>>> std_numeric(float('inf'))
inf
>>> std_numeric(float('nan'))
nan
Error handling:
>>> std_numeric("invalid")
Traceback (most recent call last):
...
TypeError: unsupported numeric type: str
>>> std_numeric("invalid", on_error="nan")
nan
Boolean handling:
>>> std_numeric(True)
Traceback (most recent call last):
...
TypeError: boolean values not supported, got True. Set allow_bool=True to convert booleans to int
>>> std_numeric(True, allow_bool=True)
1
NumPy scalars and arrays:
>>> import numpy as np # doctest: +SKIP
>>> std_numeric(np.int64(42)) # doctest: +SKIP
42
>>> std_numeric(np.float32(123)) # doctest: +SKIP
123.0
>>> std_numeric(np.array([42]).item()) # doctest: +SKIP
42
Pandas types:
>>> import pandas as pd # doctest: +SKIP
>>> std_numeric(pd.Series([42]).iloc[0]) # doctest: +SKIP
42
>>> std_numeric(pd.NA, on_error="nan") # doctest: +SKIP
nan
PyTorch tensors:
>>> import torch # doctest: +SKIP
>>> std_numeric(torch.tensor(42).item()) # doctest: +SKIP
42
>>> std_numeric(torch.tensor(3.14, dtype=torch.float32).item()) # doctest: +SKIP
3.140000104904175
TensorFlow tensors:
>>> import tensorflow as tf # doctest: +SKIP
>>> std_numeric(tf.constant(42).numpy().item()) # doctest: +SKIP
42
>>> std_numeric(tf.constant(123).numpy()) # doctest: +SKIP
123
JAX arrays:
>>> import jax.numpy as jnp # doctest: +SKIP
>>> std_numeric(jnp.array(42).item()) # doctest: +SKIP
42
>>> std_numeric(jnp.array(3.14)) # doctest: +SKIP
3.140000104904175
Astropy quantities:
>>> from astropy import units as u # doctest: +SKIP
>>> std_numeric((123 * u.second).value) # doctest: +SKIP
123.0
See Also
--------
float() : Python built-in for float conversion
int() : Python built-in for integer conversion
"""
def __is_bool_type(val: Any) -> bool:
"""Check if value is any kind of boolean."""
val_type = type(val)
val_type_name = val_type.__name__.lower()
if val_type is bool:
return True
if "bool" in val_type_name:
return True
if isinstance(val, (bool, int)) and val_type_name in ("bool_", "bool8", "bool"):
return True
# Check for mock/custom __name__ attribute on the class
if hasattr(val_type, "__name__"):
custom_name = getattr(val_type, "__name__", "")
if isinstance(custom_name, str) and "bool" in custom_name.lower():
return True
return False
def __handle_error(error_type: str = "unsupported") -> int | float | None:
"""Handle errors based on on_error parameter."""
if on_error == "raise":
if error_type == "bool":
raise TypeError("boolean values not supported")
elif error_type == "complex":
raise TypeError("complex numbers not supported")
elif error_type == "collection":
raise TypeError(
f"sizable collection not supported, got {type(value).__name__} "
f"with length {len(value)}. Extract scalar first"
)
else: # unsupported
raise TypeError(
f"unsupported numeric type: {type(value).__name__}. "
f"Expected int, float, or types with __index__, __float__, or .item()"
)
elif on_error == "nan":
return float("nan")
else: # "none"
return None
def __handle_special_cases() -> int | float | None | bool:
"""Handle pandas.NA, numpy.ma.masked, and other special cases. Returns False if not handled."""
# pandas.NA special case
if hasattr(value, "__class__"):
cls = value.__class__
cls_name = getattr(cls, "__name__", "")
cls_module = getattr(cls, "__module__", "")
if cls_name == "NAType" and "pandas" in cls_module:
return float("nan")
# numpy.ma.masked special case
if hasattr(value, "__class__"):
cls = value.__class__
if getattr(cls, "__name__", "") == "MaskedConstant" and getattr(
cls, "__module__", ""
).startswith("numpy.ma"):
return float("nan")
return False
def __extract_from_dtype() -> int | float | None | bool:
"""Extract and convert values from objects with dtype attribute. Returns False if not handled."""
if not hasattr(value, "dtype"):
return False
try:
dtype_str = str(value.dtype)
# Reject complex types
if "complex" in dtype_str:
return __handle_error("complex")
# Check for boolean dtype
dtype_is_bool = False
if hasattr(value.dtype, "is_bool"):
try:
dtype_is_bool = value.dtype.is_bool
except (AttributeError, TypeError):
pass
if not dtype_is_bool and "bool" in dtype_str.lower():
dtype_is_bool = True
if dtype_is_bool and not allow_bool:
return __handle_error("bool")
except AttributeError:
pass
# Extract scalar from tensor
result = None
if hasattr(value, "item") and callable(value.item):
try:
result = value.item()
except (TypeError, ValueError, AttributeError):
pass
if result is None and hasattr(value, "numpy") and callable(value.numpy):
try:
result = value.numpy()
except (TypeError, ValueError, AttributeError):
pass
if result is not None:
if __is_bool_type(result):
if allow_bool:
return 1 if result else 0
else:
return __handle_error("bool")
if type(result) in (int, float) or result is None:
return result
elif isinstance(result, (int, float)):
return int(result) if isinstance(result, int) else float(result)
return False
def __try_protocol_methods() -> int | float | None | bool:
"""Try various protocol methods (__index__, __float__, __int__). Returns False if not handled."""
# __index__() for exact integer types
if hasattr(value, "__index__"):
try:
return operator.index(value)
except (TypeError, ValueError, AttributeError):
pass
# Astropy Quantity (has .value and .unit)
if hasattr(value, "value") and hasattr(value, "unit"):
try:
magnitude = value.value
return std_numeric(magnitude, on_error=on_error, allow_bool=allow_bool)
except (TypeError, ValueError, AttributeError):
pass
# __float__() for all other numeric types
if hasattr(value, "__float__"):
try:
return float(value)
except OverflowError:
try:
if value < 0:
return float("-inf")
else:
return float("inf")
except (TypeError, AttributeError):
return float("inf")
except (TypeError, ValueError) as e:
if on_error == "raise":
raise TypeError(f"cannot convert {type(value).__name__} to float: {e}") from e
elif on_error == "nan":
return float("nan")
else:
return None
# __int__() as last resort
if hasattr(value, "__int__"):
try:
return int(value)
except (TypeError, ValueError, OverflowError) as e:
if on_error == "raise":
raise TypeError(f"cannot convert {type(value).__name__} to int: {e}") from e
elif on_error == "nan":
return float("nan")
else:
return None
return False
# ===== Main conversion logic =====
# None passthrough
if value is None:
return None
# Boolean handling
if isinstance(value, bool):
if allow_bool:
return int(value)
else:
if on_error == "raise":
raise TypeError(
f"boolean values not supported, got {value}. "
f"Set allow_bool=True to convert booleans to int"
)
elif on_error == "nan":
return float("nan")
else:
return None
# Standard Python types - fast path
if type(value) in (int, float):
return value
# Handle special cases (pandas.NA, numpy.ma.masked)
special_result = __handle_special_cases()
if special_result is not False:
return special_result
# Reject array-like collections
if hasattr(value, "__len__"):
try:
length = len(value)
except TypeError:
pass
else:
if on_error == "raise":
if isinstance(value, str):
raise TypeError(f"unsupported numeric type: {type(value).__name__}")
return __handle_error("collection")
elif on_error == "nan":
return float("nan")
else:
return None
# Handle array/tensor scalars with dtype
dtype_result = __extract_from_dtype()
if dtype_result is not False:
return dtype_result
# Try .item() for non-tensor objects (e.g., pandas scalars)
if hasattr(value, "item") and callable(value.item):
try:
result = value.item()
if __is_bool_type(result):
if allow_bool:
return 1 if result else 0
else:
return __handle_error("bool")
elif type(result) in (int, float) or result is None:
return result
elif isinstance(result, (int, float)):
return int(result) if isinstance(result, int) else float(result)
except (TypeError, ValueError, AttributeError):
pass
# SymPy Boolean handling (only when allow_bool=True)
if allow_bool and hasattr(value, "__class__"):
cls = value.__class__
cls_name = cls.__dict__.get("__name__", cls.__name__ if hasattr(cls, "__name__") else "")
cls_module = cls.__dict__.get(
"__module__", cls.__module__ if hasattr(cls, "__module__") else ""
)
if "sympy" in cls_module and cls_name in ("BooleanTrue", "BooleanFalse"):
try:
return int(bool(value))
except (TypeError, ValueError):
return 1 if cls_name == "BooleanTrue" else 0
# Try protocol methods (__index__, __float__, __int__)
protocol_result = __try_protocol_methods()
if protocol_result is not False:
return protocol_result
# Type not supported
return __handle_error("unsupported")
|