Current algorithm

python def _choose_float_dtype(dtype, has_offset): """Return a float dtype that can losslessly represent `dtype` values.""" # Keep float32 as-is. Upcast half-precision to single-precision, # because float16 is "intended for storage but not computation" if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 # float32 can exactly represent all integers up to 24 bits if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): # A scale factor is entirely safe (vanishing into the mantissa), # but a large integer offset could lead to loss of precision. # Sensitivity analysis can be tricky, so we just use a float64 # if there's any offset at all - better unoptimised than wrong! if not has_offset: return np.float32 # For all other types and circumstances, we just use float64. # (safe because eg. complex numbers are not supported in NetCDF) return np.float64

Due to calling bug, has_offset is always None, so this can be simplified to:

python def _choose_float_dtype(dtype) if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): return np.float32 return np.float64

Here I call the function twice, once with has_offset False, then True.

```python import numpy as np

def _choose_float_dtype(dtype, has_offset): if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): if not has_offset: return np.float32 return np.float64

generic types

for dtype in [np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc, np.int_, np.uint, np.longlong, np.ulonglong, np.half, np.float16, np.single, np.double, np.longdouble, np.csingle, np.cdouble, np.clongdouble, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, np.float16, np.float32, np.float64]: print("|", dtype, "|", _choose_float_dtype(np.dtype(dtype), False), "|", _choose_float_dtype(np.dtype(dtype), True), "|") ```

| Input | Output as called | Output as written | |-----------------------------|---------------------------|--------------------------| | <class 'numpy.int8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.longlong'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.ulonglong'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float32'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float128'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex128'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex256'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float32'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float64'> | <class 'numpy.float64'> | <class 'numpy.float64'> |