I have legacy windows numpy code with a lot of nd.array intgers without explicit dtype. In windows they are treated as np.int32. Moving to linux, they become np.int64 which cause a lot of types problems.
Instead of adding explicit dtype on many places in the code,
Can I somehow force numpy on linux 64 to treat integers as np.int32 and not np.int64. For example: np.array(1) will become np.int32.
I saw it's been asked in 1, ~two years ago and wondering if maybe something had changed since then.
One workaround for your legacy code could be a decorator for array constructors that turns objects of dtype int64 in to those of dtype int32:
from functools import wraps
def intas32(func):
@wraps(func)
def wrapper(*args, **kwargs):
obj = func(*args, **kwargs)
if (kwargs.get('dtype') is None
and hasattr(obj, 'dtype')
and obj.dtype == np.int64):
return obj.astype(np.int32)
return obj
return wrapper
Now create your one versions:
my_arange = intas32(np.arange)
and use it:
>>> my_arange(2)
array([0, 1], dtype=int32)
or monkey patch NumPy for all needed functions:
>>> np.arange = intas32(np.arange)
>>> np.arange(2)
array([0, 1], dtype=int32)
>>> np.array = intas32(np.array)
>>> np.array(1)
array(1, dtype=int32)
Be careful and test if this really works.
You can do this programmatically:
for name in ['array', 'arange']:
obj = getattr(np, name)
setattr(np, name, intas32(obj))
1.